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Scalability and performance

OpenShift Container Platform 4.10

Scaling your OpenShift Container Platform cluster and tuning performance in production environments

Red Hat OpenShift Documentation Team

Abstract

This document provides instructions for scaling your cluster and optimizing the performance of your OpenShift Container Platform environment.

Chapter 1. Recommended host practices

This topic provides recommended host practices for OpenShift Container Platform.

Important

These guidelines apply to OpenShift Container Platform with software-defined networking (SDN), not Open Virtual Network (OVN).

1.1. Recommended node host practices

The OpenShift Container Platform node configuration file contains important options. For example, two parameters control the maximum number of pods that can be scheduled to a node: podsPerCore and maxPods.

When both options are in use, the lower of the two values limits the number of pods on a node. Exceeding these values can result in:

Increased CPU utilization.
Slow pod scheduling.
Potential out-of-memory scenarios, depending on the amount of memory in the node.
Exhausting the pool of IP addresses.
Resource overcommitting, leading to poor user application performance.

Important

In Kubernetes, a pod that is holding a single container actually uses two containers. The second container is used to set up networking prior to the actual container starting. Therefore, a system running 10 pods will actually have 20 containers running.

Note

Disk IOPS throttling from the cloud provider might have an impact on CRI-O and kubelet. They might get overloaded when there are large number of I/O intensive pods running on the nodes. It is recommended that you monitor the disk I/O on the nodes and use volumes with sufficient throughput for the workload.

podsPerCore sets the number of pods the node can run based on the number of processor cores on the node. For example, if podsPerCore is set to 10 on a node with 4 processor cores, the maximum number of pods allowed on the node will be 40.

kubeletConfig:
  podsPerCore: 10

Setting podsPerCore to 0 disables this limit. The default is 0. podsPerCore cannot exceed maxPods.

maxPods sets the number of pods the node can run to a fixed value, regardless of the properties of the node.

 kubeletConfig:
    maxPods: 250

1.2. Creating a KubeletConfig CRD to edit kubelet parameters

The kubelet configuration is currently serialized as an Ignition configuration, so it can be directly edited. However, there is also a new kubelet-config-controller added to the Machine Config Controller (MCC). This lets you use a KubeletConfig custom resource (CR) to edit the kubelet parameters.

Note

As the fields in the kubeletConfig object are passed directly to the kubelet from upstream Kubernetes, the kubelet validates those values directly. Invalid values in the kubeletConfig object might cause cluster nodes to become unavailable. For valid values, see the Kubernetes documentation.

Consider the following guidance:

Create one KubeletConfig CR for each machine config pool with all the config changes you want for that pool. If you are applying the same content to all of the pools, you need only one KubeletConfig CR for all of the pools.
Edit an existing KubeletConfig CR to modify existing settings or add new settings, instead of creating a CR for each change. It is recommended that you create a CR only to modify a different machine config pool, or for changes that are intended to be temporary, so that you can revert the changes.
As needed, create multiple KubeletConfig CRs with a limit of 10 per cluster. For the first KubeletConfig CR, the Machine Config Operator (MCO) creates a machine config appended with kubelet. With each subsequent CR, the controller creates another kubelet machine config with a numeric suffix. For example, if you have a kubelet machine config with a -2 suffix, the next kubelet machine config is appended with -3.

If you want to delete the machine configs, delete them in reverse order to avoid exceeding the limit. For example, you delete the kubelet-3 machine config before deleting the kubelet-2 machine config.

Note

If you have a machine config with a kubelet-9 suffix, and you create another KubeletConfig CR, a new machine config is not created, even if there are fewer than 10 kubelet machine configs.

Example KubeletConfig CR

$ oc get kubeletconfig

NAME                AGE
set-max-pods        15m

Example showing a KubeletConfig machine config

$ oc get mc | grep kubelet

...
99-worker-generated-kubelet-1                  b5c5119de007945b6fe6fb215db3b8e2ceb12511   3.2.0             26m
...

The following procedure is an example to show how to configure the maximum number of pods per node on the worker nodes.

Prerequisites

Obtain the label associated with the static MachineConfigPool CR for the type of node you want to configure. Perform one of the following steps:

View the machine config pool:

$ oc describe machineconfigpool <name>

For example:

$ oc describe machineconfigpool worker

Example output

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
  creationTimestamp: 2019-02-08T14:52:39Z
  generation: 1
  labels:
    custom-kubelet: set-max-pods 1

1: If a label has been added it appears under labels.

If the label is not present, add a key/value pair:

$ oc label machineconfigpool worker custom-kubelet=set-max-pods

Procedure

View the available machine configuration objects that you can select:
```
$ oc get machineconfig
```
By default, the two kubelet-related configs are 01-master-kubelet and 01-worker-kubelet.

Check the current value for the maximum pods per node:

$ oc describe node <node_name>

For example:

$ oc describe node ci-ln-5grqprb-f76d1-ncnqq-worker-a-mdv94

Look for value: pods: <value> in the Allocatable stanza:

Example output

Allocatable:
 attachable-volumes-aws-ebs:  25
 cpu:                         3500m
 hugepages-1Gi:               0
 hugepages-2Mi:               0
 memory:                      15341844Ki
 pods:                        250

Set the maximum pods per node on the worker nodes by creating a custom resource file that contains the kubelet configuration:
```
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: set-max-pods
spec:
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: set-max-pods 1
  kubeletConfig:
    maxPods: 500 2
```
1
Enter the label from the machine config pool.
2
Add the kubelet configuration. In this example, use maxPods to set the maximum pods per node.
Note
The rate at which the kubelet talks to the API server depends on queries per second (QPS) and burst values. The default values, 50 for kubeAPIQPS and 100 for kubeAPIBurst, are sufficient if there are limited pods running on each node. It is recommended to update the kubelet QPS and burst rates if there are enough CPU and memory resources on the node.
```
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: set-max-pods
spec:
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: set-max-pods
  kubeletConfig:
    maxPods: <pod_count>
    kubeAPIBurst: <burst_rate>
    kubeAPIQPS: <QPS>
```
1. Update the machine config pool for workers with the label:
```
$ oc label machineconfigpool worker custom-kubelet=large-pods
```
2. Create the KubeletConfig object:
```
$ oc create -f change-maxPods-cr.yaml
```
3. Verify that the KubeletConfig object is created:
```
$ oc get kubeletconfig
```
  Example output
```
NAME                AGE
set-max-pods        15m
```
  Depending on the number of worker nodes in the cluster, wait for the worker nodes to be rebooted one by one. For a cluster with 3 worker nodes, this could take about 10 to 15 minutes.

Verify that the changes are applied to the node:

Check on a worker node that the maxPods value changed:
```
$ oc describe node <node_name>
```

Locate the Allocatable stanza:

 ...
Allocatable:
  attachable-volumes-gce-pd:  127
  cpu:                        3500m
  ephemeral-storage:          123201474766
  hugepages-1Gi:              0
  hugepages-2Mi:              0
  memory:                     14225400Ki
  pods:                       500 1
 ...

1: In this example, the pods parameter should report the value you set in the KubeletConfig object.

Verify the change in the KubeletConfig object:

$ oc get kubeletconfigs set-max-pods -o yaml

This should show a status of True and type:Success, as shown in the following example:

spec:
  kubeletConfig:
    maxPods: 500
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: set-max-pods
status:
  conditions:
  - lastTransitionTime: "2021-06-30T17:04:07Z"
    message: Success
    status: "True"
    type: Success

1.3. Modifying the number of unavailable worker nodes

By default, only one machine is allowed to be unavailable when applying the kubelet-related configuration to the available worker nodes. For a large cluster, it can take a long time for the configuration change to be reflected. At any time, you can adjust the number of machines that are updating to speed up the process.

Procedure

Edit the worker machine config pool:
```
$ oc edit machineconfigpool worker
```
Set maxUnavailable to the value that you want:
```
spec:
  maxUnavailable: <node_count>
```
Important
When setting the value, consider the number of worker nodes that can be unavailable without affecting the applications running on the cluster.

1.4. Control plane node sizing

The control plane node resource requirements depend on the number and type of nodes and objects in the cluster. The following control plane node size recommendations are based on the results of a control plane density focused testing, or Cluster-density. This test creates the following objects across a given number of namespaces:

1 image stream
1 build
5 deployments, with 2 pod replicas in a sleep state, mounting 4 secrets, 4 config maps, and 1 downward API volume each
5 services, each one pointing to the TCP/8080 and TCP/8443 ports of one of the previous deployments
1 route pointing to the first of the previous services
10 secrets containing 2048 random string characters
10 config maps containing 2048 random string characters

Number of worker nodes	Cluster-density (namespaces)	CPU cores	Memory (GB)
24	500	4	16
120	1000	8	32
252	4000	16	64
501	4000	16	96

On a large and dense cluster with three masters or control plane nodes, the CPU and memory usage will spike up when one of the nodes is stopped, rebooted or fails. The failures can be due to unexpected issues with power, network or underlying infrastructure in addition to intentional cases where the cluster is restarted after shutting it down to save costs. The remaining two control plane nodes must handle the load in order to be highly available which leads to increase in the resource usage. This is also expected during upgrades because the masters are cordoned, drained, and rebooted serially to apply the operating system updates, as well as the control plane Operators update. To avoid cascading failures, keep the overall CPU and memory resource usage on the control plane nodes to at most 60% of all available capacity to handle the resource usage spikes. Increase the CPU and memory on the control plane nodes accordingly to avoid potential downtime due to lack of resources.

Important

The node sizing varies depending on the number of nodes and object counts in the cluster. It also depends on whether the objects are actively being created on the cluster. During object creation, the control plane is more active in terms of resource usage compared to when the objects are in the running phase.

Operator Lifecycle Manager (OLM ) runs on the control plane nodes and it’s memory footprint depends on the number of namespaces and user installed operators that OLM needs to manage on the cluster. Control plane nodes need to be sized accordingly to avoid OOM kills. Following data points are based on the results from cluster maximums testing.

Number of namespaces	OLM memory at idle state (GB)	OLM memory with 5 user operators installed (GB)
500	0.823	1.7
1000	1.2	2.5
1500	1.7	3.2
2000	2	4.4
3000	2.7	5.6
4000	3.8	7.6
5000	4.2	9.02
6000	5.8	11.3
7000	6.6	12.9
8000	6.9	14.8
9000	8	17.7
10,000	9.9	21.6

Important

You can modify the control plane node size in a running OpenShift Container Platform 4.10 cluster for the following configurations only:

Clusters installed with a user-provisioned installation method.
AWS clusters installed with an installer-provisioned infrastructure installation method.

For all other configurations, you must estimate your total node count and use the suggested control plane node size during installation.

Important

The recommendations are based on the data points captured on OpenShift Container Platform clusters with OpenShift SDN as the network plugin.

Note

In OpenShift Container Platform 4.10, half of a CPU core (500 millicore) is now reserved by the system by default compared to OpenShift Container Platform 3.11 and previous versions. The sizes are determined taking that into consideration.

1.4.1. Selecting a larger Amazon Web Services instance type for control plane machines

If the control plane machines in an Amazon Web Services (AWS) cluster require more resources, you can select a larger AWS instance type for the control plane machines to use.

1.4.1.1. Changing the Amazon Web Services instance type by using the AWS console

You can change the Amazon Web Services (AWS) instance type that your control plane machines use by updating the instance type in the AWS console.

Prerequisites

You have access to the AWS console with the permissions required to modify the EC2 Instance for your cluster.
You have access to the OpenShift Container Platform cluster as a user with the cluster-admin role.

Procedure

Open the AWS console and fetch the instances for the control plane machines.
Choose one control plane machine instance.
1. For the selected control plane machine, back up the etcd data by creating an etcd snapshot. For more information, see "Backing up etcd".
2. In the AWS console, stop the control plane machine instance.
3. Select the stopped instance, and click Actions → Instance Settings → Change instance type.
4. Change the instance to a larger type, ensuring that the type is the same base as the previous selection, and apply changes. For example, you can change m6i.xlarge to m6i.2xlarge or m6i.4xlarge.
5. Start the instance.
6. If your OpenShift Container Platform cluster has a corresponding Machine object for the instance, update the instance type of the object to match the instance type set in the AWS console.
Repeat this process for each control plane machine.

Additional resources

Backing up etcd

1.5. Recommended etcd practices

Because etcd writes data to disk and persists proposals on disk, its performance depends on disk performance. Although etcd is not particularly I/O intensive, it requires a low latency block device for optimal performance and stability. Because etcd’s consensus protocol depends on persistently storing metadata to a log (WAL), etcd is sensitive to disk-write latency. Slow disks and disk activity from other processes can cause long fsync latencies.

Those latencies can cause etcd to miss heartbeats, not commit new proposals to the disk on time, and ultimately experience request timeouts and temporary leader loss. High write latencies also lead to an OpenShift API slowness, which affects cluster performance. Because of these reasons, avoid colocating other workloads on the control-plane nodes that are I/O sensitive or intensive and share the same underlying I/O infrastructure.

In terms of latency, run etcd on top of a block device that can write at least 50 IOPS of 8000 bytes long sequentially. That is, with a latency of 20ms, keep in mind that uses fdatasync to synchronize each write in the WAL. For heavy loaded clusters, sequential 500 IOPS of 8000 bytes (2 ms) are recommended. To measure those numbers, you can use a benchmarking tool, such as fio.

To achieve such performance, run etcd on machines that are backed by SSD or NVMe disks with low latency and high throughput. Consider single-level cell (SLC) solid-state drives (SSDs), which provide 1 bit per memory cell, are durable and reliable, and are ideal for write-intensive workloads.

Note

The load on etcd arises from static factors, such as the number of nodes and pods, and dynamic factors, including changes in endpoints due to pod autoscaling, pod restarts, job executions, and other workload-related events. To accurately size your etcd setup, you must analyze the specific requirements of your workload. Consider the number of nodes, pods, and other relevant factors that impact the load on etcd.

The following hard disk features provide optimal etcd performance:

Low latency to support fast read operation.
High-bandwidth writes for faster compactions and defragmentation.
High-bandwidth reads for faster recovery from failures.
Solid state drives as a minimum selection, however NVMe drives are preferred.
Server-grade hardware from various manufacturers for increased reliability.
RAID 0 technology for increased performance.
Dedicated etcd drives. Do not place log files or other heavy workloads on etcd drives.

Note

Avoid NAS or SAN setups and spinning drives. Ceph Rados Block Device (RBD) and other types of network-attached storage can result in unpredictable network latency. To provide fast storage to etcd nodes at scale, use PCI passthrough to pass NVM devices directly to the nodes.

Always benchmark by using utilities such as fio. You can use such utilities to continuously monitor the cluster performance as it increases.

Note

Avoid using the Network File System (NFS) protocol or other network based file systems.

Some key metrics to monitor on a deployed OpenShift Container Platform cluster are p99 of etcd disk write ahead log duration and the number of etcd leader changes. Use Prometheus to track these metrics.

To validate the hardware for etcd before or after you create the OpenShift Container Platform cluster, you can use fio.

Prerequisites

Container runtimes such as Podman or Docker are installed on the machine that you’re testing.
Data is written to the /var/lib/etcd path.

Procedure

Run fio and analyze the results:

If you use Podman, run this command:

$ sudo podman run --volume /var/lib/etcd:/var/lib/etcd:Z quay.io/openshift-scale/etcd-perf

If you use Docker, run this command:

$ sudo docker run --volume /var/lib/etcd:/var/lib/etcd:Z quay.io/openshift-scale/etcd-perf

The output reports whether the disk is fast enough to host etcd by comparing the 99th percentile of the fsync metric captured from the run to see if it is less than 20 ms. A few of the most important etcd metrics that might affected by I/O performance are as follow:

etcd_disk_wal_fsync_duration_seconds_bucket metric reports the etcd’s WAL fsync duration
etcd_disk_backend_commit_duration_seconds_bucket metric reports the etcd backend commit latency duration
etcd_server_leader_changes_seen_total metric reports the leader changes

Because etcd replicates the requests among all the members, its performance strongly depends on network input/output (I/O) latency. High network latencies result in etcd heartbeats taking longer than the election timeout, which results in leader elections that are disruptive to the cluster. A key metric to monitor on a deployed OpenShift Container Platform cluster is the 99th percentile of etcd network peer latency on each etcd cluster member. Use Prometheus to track the metric.

The histogram_quantile(0.99, rate(etcd_network_peer_round_trip_time_seconds_bucket[2m])) metric reports the round trip time for etcd to finish replicating the client requests between the members. Ensure that it is less than 50 ms.

Additional resources

How to use fio to check etcd disk performance in OpenShift Container Platform

1.6. Moving etcd to a different disk

You can move etcd from a shared disk to a separate disk to prevent or resolve performance issues.

Prerequisites

The MachineConfigPool must match metadata.labels[machineconfiguration.openshift.io/role]. This applies to a controller, worker, or a custom pool.
The node’s auxiliary storage device, such as /dev/sdb, must match the sdb. Change this reference in all places in the file.

Note

This procedure does not move parts of the root file system, such as /var/, to another disk or partition on an installed node.

The Machine Config Operator (MCO) is responsible for mounting a secondary disk for an OpenShift Container Platform 4.10 container storage.

Use the following steps to move etcd to a different device:

Procedure

Create a machineconfig YAML file named etcd-mc.yml and add the following information:

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 98-var-lib-etcd
spec:
  config:
    ignition:
      version: 3.2.0
    systemd:
      units:
      - contents: |
          [Unit]
          Description=Make File System on /dev/sdb
          DefaultDependencies=no
          BindsTo=dev-sdb.device
          After=dev-sdb.device var.mount
          Before=systemd-fsck@dev-sdb.service

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          ExecStart=/usr/lib/systemd/systemd-makefs xfs /dev/sdb
          TimeoutSec=0

          [Install]
          WantedBy=var-lib-containers.mount
        enabled: true
        name: systemd-mkfs@dev-sdb.service
      - contents: |
          [Unit]
          Description=Mount /dev/sdb to /var/lib/etcd
          Before=local-fs.target
          Requires=systemd-mkfs@dev-sdb.service
          After=systemd-mkfs@dev-sdb.service var.mount

          [Mount]
          What=/dev/sdb
          Where=/var/lib/etcd
          Type=xfs
          Options=defaults,prjquota

          [Install]
          WantedBy=local-fs.target
        enabled: true
        name: var-lib-etcd.mount
      - contents: |
          [Unit]
          Description=Sync etcd data if new mount is empty
          DefaultDependencies=no
          After=var-lib-etcd.mount var.mount
          Before=crio.service

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          ExecCondition=/usr/bin/test ! -d /var/lib/etcd/member
          ExecStart=/usr/sbin/setenforce 0
          ExecStart=/bin/rsync -ar /sysroot/ostree/deploy/rhcos/var/lib/etcd/ /var/lib/etcd/
          ExecStart=/usr/sbin/setenforce 1
          TimeoutSec=0

          [Install]
          WantedBy=multi-user.target graphical.target
        enabled: true
        name: sync-var-lib-etcd-to-etcd.service
      - contents: |
          [Unit]
          Description=Restore recursive SELinux security contexts
          DefaultDependencies=no
          After=var-lib-etcd.mount
          Before=crio.service

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          ExecStart=/sbin/restorecon -R /var/lib/etcd/
          TimeoutSec=0

          [Install]
          WantedBy=multi-user.target graphical.target
        enabled: true
        name: restorecon-var-lib-etcd.service

Create the machine configuration by entering the following commands:
```
$ oc login -u ${ADMIN} -p ${ADMINPASSWORD} ${API}
... output omitted ...
```
```
$ oc create -f etcd-mc.yml
machineconfig.machineconfiguration.openshift.io/98-var-lib-etcd created
```
```
$ oc login -u ${ADMIN} -p ${ADMINPASSWORD} ${API}
 [... output omitted ...]
```
```
$ oc create -f etcd-mc.yml machineconfig.machineconfiguration.openshift.io/98-var-lib-etcd created
```
The nodes are updated and rebooted. After the reboot completes, the following events occur:
- An XFS file system is created on the specified disk.
- The disk mounts to /var/lib/etc.
- The content from /sysroot/ostree/deploy/rhcos/var/lib/etcd syncs to /var/lib/etcd.
- A restore of SELinux labels is forced for /var/lib/etcd.
- The old content is not removed.

After the nodes are on a separate disk, update the machine configuration file, etcd-mc.yml with the following information:

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 98-var-lib-etcd
spec:
  config:
    ignition:
      version: 3.2.0
    systemd:
      units:
      - contents: |
          [Unit]
          Description=Mount /dev/sdb to /var/lib/etcd
          Before=local-fs.target
          Requires=systemd-mkfs@dev-sdb.service
          After=systemd-mkfs@dev-sdb.service var.mount

          [Mount]
          What=/dev/sdb
          Where=/var/lib/etcd
          Type=xfs
          Options=defaults,prjquota

          [Install]
          WantedBy=local-fs.target
        enabled: true
        name: var-lib-etcd.mount

Apply the modified version that removes the logic for creating and syncing the device by entering the following command:
```
$ oc replace -f etcd-mc.yml
```
The previous step prevents the nodes from rebooting.

Additional resources

Red Hat Enterprise Linux CoreOS (RHCOS)

1.7. Defragmenting etcd data

For large and dense clusters, etcd can suffer from poor performance if the keyspace grows too large and exceeds the space quota. Periodically maintain and defragment etcd to free up space in the data store. Monitor Prometheus for etcd metrics and defragment it when required; otherwise, etcd can raise a cluster-wide alarm that puts the cluster into a maintenance mode that accepts only key reads and deletes.

Monitor these key metrics:

etcd_server_quota_backend_bytes, which is the current quota limit
etcd_mvcc_db_total_size_in_use_in_bytes, which indicates the actual database usage after a history compaction
etcd_mvcc_db_total_size_in_bytes, which shows the database size, including free space waiting for defragmentation

Defragment etcd data to reclaim disk space after events that cause disk fragmentation, such as etcd history compaction.

History compaction is performed automatically every five minutes and leaves gaps in the back-end database. This fragmented space is available for use by etcd, but is not available to the host file system. You must defragment etcd to make this space available to the host file system.

Defragmentation occurs automatically, but you can also trigger it manually.

Note

Automatic defragmentation is good for most cases, because the etcd operator uses cluster information to determine the most efficient operation for the user.

1.7.1. Automatic defragmentation

The etcd Operator automatically defragments disks. No manual intervention is needed.

Verify that the defragmentation process is successful by viewing one of these logs:

etcd logs
cluster-etcd-operator pod
operator status error log

Warning

Automatic defragmentation can cause leader election failure in various OpenShift core components, such as the Kubernetes controller manager, which triggers a restart of the failing component. The restart is harmless and either triggers failover to the next running instance or the component resumes work again after the restart.

Example log output for successful defragmentation

etcd member has been defragmented: <member_name>, memberID: <member_id>

Example log output for unsuccessful defragmentation

failed defrag on member: <member_name>, memberID: <member_id>: <error_message>

1.7.2. Manual defragmentation

A Prometheus alert indicates when you need to use manual defragmentation. The alert is displayed in two cases:

When etcd uses more than 50% of its available space for more than 10 minutes
When etcd is actively using less than 50% of its total database size for more than 10 minutes

You can also determine whether defragmentation is needed by checking the etcd database size in MB that will be freed by defragmentation with the PromQL expression: (etcd_mvcc_db_total_size_in_bytes - etcd_mvcc_db_total_size_in_use_in_bytes)/1024/1024

Warning

Defragmenting etcd is a blocking action. The etcd member will not respond until defragmentation is complete. For this reason, wait at least one minute between defragmentation actions on each of the pods to allow the cluster to recover.

Follow this procedure to defragment etcd data on each etcd member.

Prerequisites

You have access to the cluster as a user with the cluster-admin role.

Procedure

Determine which etcd member is the leader, because the leader should be defragmented last.

Get the list of etcd pods:

$ oc -n openshift-etcd get pods -l k8s-app=etcd -o wide

Example output

etcd-ip-10-0-159-225.example.redhat.com                3/3     Running     0          175m   10.0.159.225   ip-10-0-159-225.example.redhat.com   <none>           <none>
etcd-ip-10-0-191-37.example.redhat.com                 3/3     Running     0          173m   10.0.191.37    ip-10-0-191-37.example.redhat.com    <none>           <none>
etcd-ip-10-0-199-170.example.redhat.com                3/3     Running     0          176m   10.0.199.170   ip-10-0-199-170.example.redhat.com   <none>           <none>

Choose a pod and run the following command to determine which etcd member is the leader:

$ oc rsh -n openshift-etcd etcd-ip-10-0-159-225.example.redhat.com etcdctl endpoint status --cluster -w table

Example output

Defaulting container name to etcdctl.
Use 'oc describe pod/etcd-ip-10-0-159-225.example.redhat.com -n openshift-etcd' to see all of the containers in this pod.
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|         ENDPOINT          |        ID        | VERSION | DB SIZE | IS LEADER | IS LEARNER | RAFT TERM | RAFT INDEX | RAFT APPLIED INDEX | ERRORS |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|  https://10.0.191.37:2379 | 251cd44483d811c3 |   3.4.9 |  104 MB |     false |      false |         7 |      91624 |              91624 |        |
| https://10.0.159.225:2379 | 264c7c58ecbdabee |   3.4.9 |  104 MB |     false |      false |         7 |      91624 |              91624 |        |
| https://10.0.199.170:2379 | 9ac311f93915cc79 |   3.4.9 |  104 MB |      true |      false |         7 |      91624 |              91624 |        |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+

Based on the IS LEADER column of this output, the https://10.0.199.170:2379 endpoint is the leader. Matching this endpoint with the output of the previous step, the pod name of the leader is etcd-ip-10-0-199-170.example.redhat.com.

Defragment an etcd member.

Connect to the running etcd container, passing in the name of a pod that is not the leader:
```
$ oc rsh -n openshift-etcd etcd-ip-10-0-159-225.example.redhat.com
```
Unset the ETCDCTL_ENDPOINTS environment variable:
```
sh-4.4# unset ETCDCTL_ENDPOINTS
```

Defragment the etcd member:

sh-4.4# etcdctl --command-timeout=30s --endpoints=https://localhost:2379 defrag

Example output

Finished defragmenting etcd member[https://localhost:2379]

If a timeout error occurs, increase the value for --command-timeout until the command succeeds.

Verify that the database size was reduced:

sh-4.4# etcdctl endpoint status -w table --cluster

Example output

+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|         ENDPOINT          |        ID        | VERSION | DB SIZE | IS LEADER | IS LEARNER | RAFT TERM | RAFT INDEX | RAFT APPLIED INDEX | ERRORS |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|  https://10.0.191.37:2379 | 251cd44483d811c3 |   3.4.9 |  104 MB |     false |      false |         7 |      91624 |              91624 |        |
| https://10.0.159.225:2379 | 264c7c58ecbdabee |   3.4.9 |   41 MB |     false |      false |         7 |      91624 |              91624 |        | 1
| https://10.0.199.170:2379 | 9ac311f93915cc79 |   3.4.9 |  104 MB |      true |      false |         7 |      91624 |              91624 |        |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+

This example shows that the database size for this etcd member is now 41 MB as opposed to the starting size of 104 MB.

Repeat these steps to connect to each of the other etcd members and defragment them. Always defragment the leader last.
Wait at least one minute between defragmentation actions to allow the etcd pod to recover. Until the etcd pod recovers, the etcd member will not respond.

If any NOSPACE alarms were triggered due to the space quota being exceeded, clear them.
1. Check if there are any NOSPACE alarms:
```
sh-4.4# etcdctl alarm list
```
  Example output
```
memberID:12345678912345678912 alarm:NOSPACE
```
2. Clear the alarms:
```
sh-4.4# etcdctl alarm disarm
```

1.8. OpenShift Container Platform infrastructure components

The following infrastructure workloads do not incur OpenShift Container Platform worker subscriptions:

Kubernetes and OpenShift Container Platform control plane services that run on masters
The default router
The integrated container image registry
The HAProxy-based Ingress Controller
The cluster metrics collection, or monitoring service, including components for monitoring user-defined projects
Cluster aggregated logging
Service brokers
Red Hat Quay
Red Hat OpenShift Data Foundation
Red Hat Advanced Cluster Manager
Red Hat Advanced Cluster Security for Kubernetes
Red Hat OpenShift GitOps
Red Hat OpenShift Pipelines

Any node that runs any other container, pod, or component is a worker node that your subscription must cover.

For information on infrastructure nodes and which components can run on infrastructure nodes, see the "Red Hat OpenShift control plane and infrastructure nodes" section in the OpenShift sizing and subscription guide for enterprise Kubernetes document.

1.9. Moving the monitoring solution

The monitoring stack includes multiple components, including Prometheus, Grafana, and Alertmanager. The Cluster Monitoring Operator manages this stack. To redeploy the monitoring stack to infrastructure nodes, you can create and apply a custom config map.

Procedure

Edit the cluster-monitoring-config config map and change the nodeSelector to use the infra label:

$ oc edit configmap cluster-monitoring-config -n openshift-monitoring

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |+
    alertmanagerMain:
      nodeSelector: 1
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    prometheusK8s:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    prometheusOperator:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    grafana:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    k8sPrometheusAdapter:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    kubeStateMetrics:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    telemeterClient:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    openshiftStateMetrics:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    thanosQuerier:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute

1 1: Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrasructure node, also add a matching toleration.

Watch the monitoring pods move to the new machines:

$ watch 'oc get pod -n openshift-monitoring -o wide'

If a component has not moved to the infra node, delete the pod with this component:
```
$ oc delete pod -n openshift-monitoring <pod>
```
The component from the deleted pod is re-created on the infra node.

1.10. Moving the default registry

You configure the registry Operator to deploy its pods to different nodes.

Prerequisites

Configure additional machine sets in your OpenShift Container Platform cluster.

Procedure

View the config/instance object:

$ oc get configs.imageregistry.operator.openshift.io/cluster -o yaml

Example output

apiVersion: imageregistry.operator.openshift.io/v1
kind: Config
metadata:
  creationTimestamp: 2019-02-05T13:52:05Z
  finalizers:
  - imageregistry.operator.openshift.io/finalizer
  generation: 1
  name: cluster
  resourceVersion: "56174"
  selfLink: /apis/imageregistry.operator.openshift.io/v1/configs/cluster
  uid: 36fd3724-294d-11e9-a524-12ffeee2931b
spec:
  httpSecret: d9a012ccd117b1e6616ceccb2c3bb66a5fed1b5e481623
  logging: 2
  managementState: Managed
  proxy: {}
  replicas: 1
  requests:
    read: {}
    write: {}
  storage:
    s3:
      bucket: image-registry-us-east-1-c92e88cad85b48ec8b312344dff03c82-392c
      region: us-east-1
status:
...

Edit the config/instance object:

$ oc edit configs.imageregistry.operator.openshift.io/cluster

spec:
  affinity:
    podAntiAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
      - podAffinityTerm:
          namespaces:
          - openshift-image-registry
          topologyKey: kubernetes.io/hostname
        weight: 100
  logLevel: Normal
  managementState: Managed
  nodeSelector: 1
    node-role.kubernetes.io/infra: ""
  tolerations:
  - effect: NoSchedule
    key: node-role.kubernetes.io/infra
    value: reserved
  - effect: NoExecute
    key: node-role.kubernetes.io/infra
    value: reserved

1: Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrasructure node, also add a matching toleration.

Verify the registry pod has been moved to the infrastructure node.
1. Run the following command to identify the node where the registry pod is located:
```
$ oc get pods -o wide -n openshift-image-registry
```
2. Confirm the node has the label you specified:
```
$ oc describe node <node_name>
```
  Review the command output and confirm that node-role.kubernetes.io/infra is in the LABELS list.

1.11. Moving the router

You can deploy the router pod to a different machine set. By default, the pod is deployed to a worker node.

Prerequisites

Configure additional machine sets in your OpenShift Container Platform cluster.

Procedure

View the IngressController custom resource for the router Operator:

$ oc get ingresscontroller default -n openshift-ingress-operator -o yaml

The command output resembles the following text:

apiVersion: operator.openshift.io/v1
kind: IngressController
metadata:
  creationTimestamp: 2019-04-18T12:35:39Z
  finalizers:
  - ingresscontroller.operator.openshift.io/finalizer-ingresscontroller
  generation: 1
  name: default
  namespace: openshift-ingress-operator
  resourceVersion: "11341"
  selfLink: /apis/operator.openshift.io/v1/namespaces/openshift-ingress-operator/ingresscontrollers/default
  uid: 79509e05-61d6-11e9-bc55-02ce4781844a
spec: {}
status:
  availableReplicas: 2
  conditions:
  - lastTransitionTime: 2019-04-18T12:36:15Z
    status: "True"
    type: Available
  domain: apps.<cluster>.example.com
  endpointPublishingStrategy:
    type: LoadBalancerService
  selector: ingresscontroller.operator.openshift.io/deployment-ingresscontroller=default

Edit the ingresscontroller resource and change the nodeSelector to use the infra label:

$ oc edit ingresscontroller default -n openshift-ingress-operator

  spec:
    nodePlacement:
      nodeSelector: 1
        matchLabels:
          node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved

1: Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrastructure node, also add a matching toleration.

Confirm that the router pod is running on the infra node.

View the list of router pods and note the node name of the running pod:

$ oc get pod -n openshift-ingress -o wide

Example output

NAME                              READY     STATUS        RESTARTS   AGE       IP           NODE                           NOMINATED NODE   READINESS GATES
router-default-86798b4b5d-bdlvd   1/1      Running       0          28s       10.130.2.4   ip-10-0-217-226.ec2.internal   <none>           <none>
router-default-955d875f4-255g8    0/1      Terminating   0          19h       10.129.2.4   ip-10-0-148-172.ec2.internal   <none>           <none>

In this example, the running pod is on the ip-10-0-217-226.ec2.internal node.

View the node status of the running pod:
```
$ oc get node <node_name> 1
```
1
Specify the <node_name> that you obtained from the pod list.
Example output
```
NAME                          STATUS  ROLES         AGE   VERSION
ip-10-0-217-226.ec2.internal  Ready   infra,worker  17h   v1.23.0
```
Because the role list includes infra, the pod is running on the correct node.

1.12. Infrastructure node sizing

Infrastructure nodes are nodes that are labeled to run pieces of the OpenShift Container Platform environment. The infrastructure node resource requirements depend on the cluster age, nodes, and objects in the cluster, as these factors can lead to an increase in the number of metrics or time series in Prometheus. The following infrastructure node size recommendations are based on the results of cluster maximums and control plane density focused testing.

Number of worker nodes	Cluster density, or number of namespaces	CPU cores	Memory (GB)
27	500	4	24
120	1000	8	48
252	4000	16	128
501	4000	32	128

In general, three infrastructure nodes are recommended per cluster.

Important

These sizing recommendations should be used as a guideline. Prometheus is a highly memory intensive application; the resource usage depends on various factors including the number of nodes, objects, the Prometheus metrics scraping interval, metrics or time series, and the age of the cluster. In addition, the router resource usage can also be affected by the number of routes and the amount/type of inbound requests.

These recommendations apply only to infrastructure nodes hosting Monitoring, Ingress and Registry infrastructure components installed during cluster creation.

Note

1.13. Additional resources

Chapter 2. Recommended host practices for IBM Z & LinuxONE environments

This topic provides recommended host practices for OpenShift Container Platform on IBM Z and LinuxONE.

Note

The s390x architecture is unique in many aspects. Therefore, some recommendations made here might not apply to other platforms.

Note

Unless stated otherwise, these practices apply to both z/VM and Red Hat Enterprise Linux (RHEL) KVM installations on IBM Z and LinuxONE.

2.1. Managing CPU overcommitment

In a highly virtualized IBM Z environment, you must carefully plan the infrastructure setup and sizing. One of the most important features of virtualization is the capability to do resource overcommitment, allocating more resources to the virtual machines than actually available at the hypervisor level. This is very workload dependent and there is no golden rule that can be applied to all setups.

Depending on your setup, consider these best practices regarding CPU overcommitment:

At LPAR level (PR/SM hypervisor), avoid assigning all available physical cores (IFLs) to each LPAR. For example, with four physical IFLs available, you should not define three LPARs with four logical IFLs each.
Check and understand LPAR shares and weights.
An excessive number of virtual CPUs can adversely affect performance. Do not define more virtual processors to a guest than logical processors are defined to the LPAR.
Configure the number of virtual processors per guest for peak workload, not more.
Start small and monitor the workload. Increase the vCPU number incrementally if necessary.
Not all workloads are suitable for high overcommitment ratios. If the workload is CPU intensive, you will probably not be able to achieve high ratios without performance problems. Workloads that are more I/O intensive can keep consistent performance even with high overcommitment ratios.

Additional resources

2.2. Disable Transparent Huge Pages

Transparent Huge Pages (THP) attempt to automate most aspects of creating, managing, and using huge pages. Since THP automatically manages the huge pages, this is not always handled optimally for all types of workloads. THP can lead to performance regressions, since many applications handle huge pages on their own. Therefore, consider disabling THP.

2.3. Boost networking performance with Receive Flow Steering

Receive Flow Steering (RFS) extends Receive Packet Steering (RPS) by further reducing network latency. RFS is technically based on RPS, and improves the efficiency of packet processing by increasing the CPU cache hit rate. RFS achieves this, and in addition considers queue length, by determining the most convenient CPU for computation so that cache hits are more likely to occur within the CPU. Thus, the CPU cache is invalidated less and requires fewer cycles to rebuild the cache. This can help reduce packet processing run time.

2.3.1. Use the Machine Config Operator (MCO) to activate RFS

Procedure

Copy the following MCO sample profile into a YAML file. For example, enable-rfs.yaml:

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: worker
  name: 50-enable-rfs
spec:
  config:
    ignition:
      version: 2.2.0
    storage:
      files:
      - contents:
          source: data:text/plain;charset=US-ASCII,%23%20turn%20on%20Receive%20Flow%20Steering%20%28RFS%29%20for%20all%20network%20interfaces%0ASUBSYSTEM%3D%3D%22net%22%2C%20ACTION%3D%3D%22add%22%2C%20RUN%7Bprogram%7D%2B%3D%22/bin/bash%20-c%20%27for%20x%20in%20/sys/%24DEVPATH/queues/rx-%2A%3B%20do%20echo%208192%20%3E%20%24x/rps_flow_cnt%3B%20%20done%27%22%0A
        filesystem: root
        mode: 0644
        path: /etc/udev/rules.d/70-persistent-net.rules
      - contents:
          source: data:text/plain;charset=US-ASCII,%23%20define%20sock%20flow%20enbtried%20for%20%20Receive%20Flow%20Steering%20%28RFS%29%0Anet.core.rps_sock_flow_entries%3D8192%0A
        filesystem: root
        mode: 0644
        path: /etc/sysctl.d/95-enable-rps.conf

Create the MCO profile:
```
$ oc create -f enable-rfs.yaml
```
Verify that an entry named 50-enable-rfs is listed:
```
$ oc get mc
```
To deactivate, enter:
```
$ oc delete mc 50-enable-rfs
```

Additional resources

2.4. Choose your networking setup

The networking stack is one of the most important components for a Kubernetes-based product like OpenShift Container Platform. For IBM Z setups, the networking setup depends on the hypervisor of your choice. Depending on the workload and the application, the best fit usually changes with the use case and the traffic pattern.

Depending on your setup, consider these best practices:

Consider all options regarding networking devices to optimize your traffic pattern. Explore the advantages of OSA-Express, RoCE Express, HiperSockets, z/VM VSwitch, Linux Bridge (KVM), and others to decide which option leads to the greatest benefit for your setup.
Always use the latest available NIC version. For example, OSA Express 7S 10 GbE shows great improvement compared to OSA Express 6S 10 GbE with transactional workload types, although both are 10 GbE adapters.
Each virtual switch adds an additional layer of latency.
The load balancer plays an important role for network communication outside the cluster. Consider using a production-grade hardware load balancer if this is critical for your application.
OpenShift Container Platform SDN introduces flows and rules, which impact the networking performance. Make sure to consider pod affinities and placements, to benefit from the locality of services where communication is critical.
Balance the trade-off between performance and functionality.

Additional resources

2.5. Ensure high disk performance with HyperPAV on z/VM

DASD and ECKD devices are commonly used disk types in IBM Z environments. In a typical OpenShift Container Platform setup in z/VM environments, DASD disks are commonly used to support the local storage for the nodes. You can set up HyperPAV alias devices to provide more throughput and overall better I/O performance for the DASD disks that support the z/VM guests.

Using HyperPAV for the local storage devices leads to a significant performance benefit. However, you must be aware that there is a trade-off between throughput and CPU costs.

2.5.1. Use the Machine Config Operator (MCO) to activate HyperPAV aliases in nodes using z/VM full-pack minidisks

For z/VM-based OpenShift Container Platform setups that use full-pack minidisks, you can leverage the advantage of MCO profiles by activating HyperPAV aliases in all of the nodes. You must add YAML configurations for both control plane and compute nodes.

Procedure

Copy the following MCO sample profile into a YAML file for the control plane node. For example, 05-master-kernelarg-hpav.yaml:

$ cat 05-master-kernelarg-hpav.yaml
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 05-master-kernelarg-hpav
spec:
  config:
    ignition:
      version: 3.1.0
  kernelArguments:
    - rd.dasd=800-805

Copy the following MCO sample profile into a YAML file for the compute node. For example, 05-worker-kernelarg-hpav.yaml:

$ cat 05-worker-kernelarg-hpav.yaml
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: worker
  name: 05-worker-kernelarg-hpav
spec:
  config:
    ignition:
      version: 3.1.0
  kernelArguments:
    - rd.dasd=800-805

Note

You must modify the rd.dasd arguments to fit the device IDs.

Create the MCO profiles:

$ oc create -f 05-master-kernelarg-hpav.yaml

$ oc create -f 05-worker-kernelarg-hpav.yaml

To deactivate, enter:

$ oc delete -f 05-master-kernelarg-hpav.yaml

$ oc delete -f 05-worker-kernelarg-hpav.yaml

Additional resources

2.6. RHEL KVM on IBM Z host recommendations

Optimizing a KVM virtual server environment strongly depends on the workloads of the virtual servers and on the available resources. The same action that enhances performance in one environment can have adverse effects in another. Finding the best balance for a particular setting can be a challenge and often involves experimentation.

The following section introduces some best practices when using OpenShift Container Platform with RHEL KVM on IBM Z and LinuxONE environments.

2.6.1. Use multiple queues for your VirtIO network interfaces

With multiple virtual CPUs, you can transfer packages in parallel if you provide multiple queues for incoming and outgoing packets. Use the queues attribute of the driver element to configure multiple queues. Specify an integer of at least 2 that does not exceed the number of virtual CPUs of the virtual server.

The following example specification configures two input and output queues for a network interface:

<interface type="direct">
    <source network="net01"/>
    <model type="virtio"/>
    <driver ... queues="2"/>
</interface>

Multiple queues are designed to provide enhanced performance for a network interface, but they also use memory and CPU resources. Start with defining two queues for busy interfaces. Next, try two queues for interfaces with less traffic or more than two queues for busy interfaces.

2.6.2. Use I/O threads for your virtual block devices

To make virtual block devices use I/O threads, you must configure one or more I/O threads for the virtual server and each virtual block device to use one of these I/O threads.

The following example specifies <iothreads>3</iothreads> to configure three I/O threads, with consecutive decimal thread IDs 1, 2, and 3. The iothread="2" parameter specifies the driver element of the disk device to use the I/O thread with ID 2.

Sample I/O thread specification

...
<domain>
 	<iothreads>3</iothreads>1
  	 ...
    	<devices>
       ...
          <disk type="block" device="disk">2
<driver ... iothread="2"/>
    </disk>
       ...
    	</devices>
   ...
</domain>

1: The number of I/O threads.
2: The driver element of the disk device.

Threads can increase the performance of I/O operations for disk devices, but they also use memory and CPU resources. You can configure multiple devices to use the same thread. The best mapping of threads to devices depends on the available resources and the workload.

Start with a small number of I/O threads. Often, a single I/O thread for all disk devices is sufficient. Do not configure more threads than the number of virtual CPUs, and do not configure idle threads.

You can use the virsh iothreadadd command to add I/O threads with specific thread IDs to a running virtual server.

2.6.3. Avoid virtual SCSI devices

Configure virtual SCSI devices only if you need to address the device through SCSI-specific interfaces. Configure disk space as virtual block devices rather than virtual SCSI devices, regardless of the backing on the host.

However, you might need SCSI-specific interfaces for:

A LUN for a SCSI-attached tape drive on the host.
A DVD ISO file on the host file system that is mounted on a virtual DVD drive.

2.6.4. Configure guest caching for disk

Configure your disk devices to do caching by the guest and not by the host.

Ensure that the driver element of the disk device includes the cache="none" and io="native" parameters.

<disk type="block" device="disk">
    <driver name="qemu" type="raw" cache="none" io="native" iothread="1"/>
...
</disk>

2.6.5. Exclude the memory balloon device

Unless you need a dynamic memory size, do not define a memory balloon device and ensure that libvirt does not create one for you. Include the memballoon parameter as a child of the devices element in your domain configuration XML file.

Check the list of active profiles:
```
<memballoon model="none"/>
```

2.6.6. Tune the CPU migration algorithm of the host scheduler

Important

Do not change the scheduler settings unless you are an expert who understands the implications. Do not apply changes to production systems without testing them and confirming that they have the intended effect.

The kernel.sched_migration_cost_ns parameter specifies a time interval in nanoseconds. After the last execution of a task, the CPU cache is considered to have useful content until this interval expires. Increasing this interval results in fewer task migrations. The default value is 500000 ns.

If the CPU idle time is higher than expected when there are runnable processes, try reducing this interval. If tasks bounce between CPUs or nodes too often, try increasing it.

To dynamically set the interval to 60000 ns, enter the following command:

# sysctl kernel.sched_migration_cost_ns=60000

To persistently change the value to 60000 ns, add the following entry to /etc/sysctl.conf:

kernel.sched_migration_cost_ns=60000

2.6.7. Disable the cpuset cgroup controller

Note

This setting applies only to KVM hosts with cgroups version 1. To enable CPU hotplug on the host, disable the cgroup controller.

Procedure

Open /etc/libvirt/qemu.conf with an editor of your choice.
Go to the cgroup_controllers line.
Duplicate the entire line and remove the leading number sign (#) from the copy.

Remove the cpuset entry, as follows:

cgroup_controllers = [ "cpu", "devices", "memory", "blkio", "cpuacct" ]

For the new setting to take effect, you must restart the libvirtd daemon:
1. Stop all virtual machines.
2. Run the following command:
```
# systemctl restart libvirtd
```
3. Restart the virtual machines.

This setting persists across host reboots.

2.6.8. Tune the polling period for idle virtual CPUs

When a virtual CPU becomes idle, KVM polls for wakeup conditions for the virtual CPU before allocating the host resource. You can specify the time interval, during which polling takes place in sysfs at /sys/module/kvm/parameters/halt_poll_ns. During the specified time, polling reduces the wakeup latency for the virtual CPU at the expense of resource usage. Depending on the workload, a longer or shorter time for polling can be beneficial. The time interval is specified in nanoseconds. The default is 50000 ns.

To optimize for low CPU consumption, enter a small value or write 0 to disable polling:
```
# echo 0 > /sys/module/kvm/parameters/halt_poll_ns
```
To optimize for low latency, for example for transactional workloads, enter a large value:
```
# echo 80000 > /sys/module/kvm/parameters/halt_poll_ns
```

Additional resources

Chapter 3. Recommended cluster scaling practices

Important

The guidance in this section is only relevant for installations with cloud provider integration.

These guidelines apply to OpenShift Container Platform with software-defined networking (SDN), not Open Virtual Network (OVN).

Apply the following best practices to scale the number of worker machines in your OpenShift Container Platform cluster. You scale the worker machines by increasing or decreasing the number of replicas that are defined in the worker machine set.

3.1. Recommended practices for scaling the cluster

When scaling up the cluster to higher node counts:

Spread nodes across all of the available zones for higher availability.
Scale up by no more than 25 to 50 machines at once.
Consider creating new machine sets in each available zone with alternative instance types of similar size to help mitigate any periodic provider capacity constraints. For example, on AWS, use m5.large and m5d.large.

Note

Cloud providers might implement a quota for API services. Therefore, gradually scale the cluster.

The controller might not be able to create the machines if the replicas in the machine sets are set to higher numbers all at one time. The number of requests the cloud platform, which OpenShift Container Platform is deployed on top of, is able to handle impacts the process. The controller will start to query more while trying to create, check, and update the machines with the status. The cloud platform on which OpenShift Container Platform is deployed has API request limits and excessive queries might lead to machine creation failures due to cloud platform limitations.

Enable machine health checks when scaling to large node counts. In case of failures, the health checks monitor the condition and automatically repair unhealthy machines.

Note

When scaling large and dense clusters to lower node counts, it might take large amounts of time as the process involves draining or evicting the objects running on the nodes being terminated in parallel. Also, the client might start to throttle the requests if there are too many objects to evict. The default client QPS and burst rates are currently set to 5 and 10 respectively and they cannot be modified in OpenShift Container Platform.

3.2. Modifying a machine set

To make changes to a machine set, edit the MachineSet YAML. Then, remove all machines associated with the machine set by deleting each machine or scaling down the machine set to 0 replicas. Then, scale the replicas back to the desired number. Changes you make to a machine set do not affect existing machines.

If you need to scale a machine set without making other changes, you do not need to delete the machines.

Note

By default, the OpenShift Container Platform router pods are deployed on workers. Because the router is required to access some cluster resources, including the web console, do not scale the worker machine set to 0 unless you first relocate the router pods.

Prerequisites

Install an OpenShift Container Platform cluster and the oc command line.
Log in to oc as a user with cluster-admin permission.

Procedure

Edit the machine set:

$ oc edit machineset <machineset> -n openshift-machine-api

Scale down the machine set to 0:

$ oc scale --replicas=0 machineset <machineset> -n openshift-machine-api

Or:

$ oc edit machineset <machineset> -n openshift-machine-api

Tip

You can alternatively apply the following YAML to scale the machine set:

apiVersion: machine.openshift.io/v1beta1
kind: MachineSet
metadata:
  name: <machineset>
  namespace: openshift-machine-api
spec:
  replicas: 0

Wait for the machines to be removed.

Scale up the machine set as needed:

$ oc scale --replicas=2 machineset <machineset> -n openshift-machine-api

Or:

$ oc edit machineset <machineset> -n openshift-machine-api

Tip

You can alternatively apply the following YAML to scale the machine set:

apiVersion: machine.openshift.io/v1beta1
kind: MachineSet
metadata:
  name: <machineset>
  namespace: openshift-machine-api
spec:
  replicas: 2

Wait for the machines to start. The new machines contain changes you made to the machine set.

3.3. About machine health checks

Machine health checks automatically repair unhealthy machines in a particular machine pool.

To monitor machine health, create a resource to define the configuration for a controller. Set a condition to check, such as staying in the NotReady status for five minutes or displaying a permanent condition in the node-problem-detector, and a label for the set of machines to monitor.

Note

You cannot apply a machine health check to a machine with the master role.

The controller that observes a MachineHealthCheck resource checks for the defined condition. If a machine fails the health check, the machine is automatically deleted and one is created to take its place. When a machine is deleted, you see a machine deleted event.

To limit disruptive impact of the machine deletion, the controller drains and deletes only one node at a time. If there are more unhealthy machines than the maxUnhealthy threshold allows for in the targeted pool of machines, remediation stops and therefore enables manual intervention.

Note

Consider the timeouts carefully, accounting for workloads and requirements.

Long timeouts can result in long periods of downtime for the workload on the unhealthy machine.
Too short timeouts can result in a remediation loop. For example, the timeout for checking the NotReady status must be long enough to allow the machine to complete the startup process.

To stop the check, remove the resource.

3.3.1. Limitations when deploying machine health checks

There are limitations to consider before deploying a machine health check:

Only machines owned by a machine set are remediated by a machine health check.
Control plane machines are not currently supported and are not remediated if they are unhealthy.
If the node for a machine is removed from the cluster, a machine health check considers the machine to be unhealthy and remediates it immediately.
If the corresponding node for a machine does not join the cluster after the nodeStartupTimeout, the machine is remediated.
A machine is remediated immediately if the Machine resource phase is Failed.

3.4. Sample MachineHealthCheck resource

The MachineHealthCheck resource for all cloud-based installation types, and other than bare metal, resembles the following YAML file:

apiVersion: machine.openshift.io/v1beta1
kind: MachineHealthCheck
metadata:
  name: example 1
  namespace: openshift-machine-api
spec:
  selector:
    matchLabels:
      machine.openshift.io/cluster-api-machine-role: <role> 2
      machine.openshift.io/cluster-api-machine-type: <role> 3
      machine.openshift.io/cluster-api-machineset: <cluster_name>-<label>-<zone> 4
  unhealthyConditions:
  - type:    "Ready"
    timeout: "300s" 5
    status: "False"
  - type:    "Ready"
    timeout: "300s" 6
    status: "Unknown"
  maxUnhealthy: "40%" 7
  nodeStartupTimeout: "10m" 8

1: Specify the name of the machine health check to deploy.
2 3: Specify a label for the machine pool that you want to check.
4: Specify the machine set to track in <cluster_name>-<label>-<zone> format. For example, prod-node-us-east-1a.
5 6: Specify the timeout duration for a node condition. If a condition is met for the duration of the timeout, the machine will be remediated. Long timeouts can result in long periods of downtime for a workload on an unhealthy machine.
7: Specify the amount of machines allowed to be concurrently remediated in the targeted pool. This can be set as a percentage or an integer. If the number of unhealthy machines exceeds the limit set by maxUnhealthy, remediation is not performed.
8: Specify the timeout duration that a machine health check must wait for a node to join the cluster before a machine is determined to be unhealthy.

Note

The matchLabels are examples only; you must map your machine groups based on your specific needs.

3.4.1. Short-circuiting machine health check remediation

Short circuiting ensures that machine health checks remediate machines only when the cluster is healthy. Short-circuiting is configured through the maxUnhealthy field in the MachineHealthCheck resource.

If the user defines a value for the maxUnhealthy field, before remediating any machines, the MachineHealthCheck compares the value of maxUnhealthy with the number of machines within its target pool that it has determined to be unhealthy. Remediation is not performed if the number of unhealthy machines exceeds the maxUnhealthy limit.

Important

If maxUnhealthy is not set, the value defaults to 100% and the machines are remediated regardless of the state of the cluster.

The appropriate maxUnhealthy value depends on the scale of the cluster you deploy and how many machines the MachineHealthCheck covers. For example, you can use the maxUnhealthy value to cover multiple machine sets across multiple availability zones so that if you lose an entire zone, your maxUnhealthy setting prevents further remediation within the cluster. In global Azure regions that do not have multiple availability zones, you can use availability sets to ensure high availability.

The maxUnhealthy field can be set as either an integer or percentage. There are different remediation implementations depending on the maxUnhealthy value.

3.4.1.1. Setting maxUnhealthy by using an absolute value

If maxUnhealthy is set to 2:

Remediation will be performed if 2 or fewer nodes are unhealthy
Remediation will not be performed if 3 or more nodes are unhealthy

These values are independent of how many machines are being checked by the machine health check.

3.4.1.2. Setting maxUnhealthy by using percentages

If maxUnhealthy is set to 40% and there are 25 machines being checked:

Remediation will be performed if 10 or fewer nodes are unhealthy
Remediation will not be performed if 11 or more nodes are unhealthy

If maxUnhealthy is set to 40% and there are 6 machines being checked:

Remediation will be performed if 2 or fewer nodes are unhealthy
Remediation will not be performed if 3 or more nodes are unhealthy

Note

The allowed number of machines is rounded down when the percentage of maxUnhealthy machines that are checked is not a whole number.

3.5. Creating a MachineHealthCheck resource

You can create a MachineHealthCheck resource for all MachineSets in your cluster. You should not create a MachineHealthCheck resource that targets control plane machines.

Prerequisites

Install the oc command line interface.

Procedure

Create a healthcheck.yml file that contains the definition of your machine health check.
Apply the healthcheck.yml file to your cluster:
```
$ oc apply -f healthcheck.yml
```

Chapter 4. Using the Node Tuning Operator

Learn about the Node Tuning Operator and how you can use it to manage node-level tuning by orchestrating the tuned daemon.

4.1. About the Node Tuning Operator

The Node Tuning Operator helps you manage node-level tuning by orchestrating the TuneD daemon. The majority of high-performance applications require some level of kernel tuning. The Node Tuning Operator provides a unified management interface to users of node-level sysctls and more flexibility to add custom tuning specified by user needs.

The Operator manages the containerized TuneD daemon for OpenShift Container Platform as a Kubernetes daemon set. It ensures the custom tuning specification is passed to all containerized TuneD daemons running in the cluster in the format that the daemons understand. The daemons run on all nodes in the cluster, one per node.

Node-level settings applied by the containerized TuneD daemon are rolled back on an event that triggers a profile change or when the containerized TuneD daemon is terminated gracefully by receiving and handling a termination signal.

The Node Tuning Operator is part of a standard OpenShift Container Platform installation in version 4.1 and later.

4.2. Accessing an example Node Tuning Operator specification

Use this process to access an example Node Tuning Operator specification.

Procedure

Run:

$ oc get Tuned/default -o yaml -n openshift-cluster-node-tuning-operator

The default CR is meant for delivering standard node-level tuning for the OpenShift Container Platform platform and it can only be modified to set the Operator Management state. Any other custom changes to the default CR will be overwritten by the Operator. For custom tuning, create your own Tuned CRs. Newly created CRs will be combined with the default CR and custom tuning applied to OpenShift Container Platform nodes based on node or pod labels and profile priorities.

Warning

While in certain situations the support for pod labels can be a convenient way of automatically delivering required tuning, this practice is discouraged and strongly advised against, especially in large-scale clusters. The default Tuned CR ships without pod label matching. If a custom profile is created with pod label matching, then the functionality will be enabled at that time. The pod label functionality might be deprecated in future versions of the Node Tuning Operator.

4.3. Default profiles set on a cluster

The following are the default profiles set on a cluster.

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: default
  namespace: openshift-cluster-node-tuning-operator
spec:
  recommend:
  - profile: "openshift-control-plane"
    priority: 30
    match:
    - label: "node-role.kubernetes.io/master"
    - label: "node-role.kubernetes.io/infra"

  - profile: "openshift-node"
    priority: 40

Starting with OpenShift Container Platform 4.9, all OpenShift TuneD profiles are shipped with the TuneD package. You can use the oc exec command to view the contents of these profiles:

$ oc exec $tuned_pod -n openshift-cluster-node-tuning-operator -- find /usr/lib/tuned/openshift{,-control-plane,-node} -name tuned.conf -exec grep -H ^ {} \;

4.4. Verifying that the TuneD profiles are applied

Verify the TuneD profiles that are applied to your cluster node.

$ oc get profile -n openshift-cluster-node-tuning-operator

Example output

NAME             TUNED                     APPLIED   DEGRADED   AGE
master-0         openshift-control-plane   True      False      6h33m
master-1         openshift-control-plane   True      False      6h33m
master-2         openshift-control-plane   True      False      6h33m
worker-a         openshift-node            True      False      6h28m
worker-b         openshift-node            True      False      6h28m

NAME: Name of the Profile object. There is one Profile object per node and their names match.
TUNED: Name of the desired TuneD profile to apply.
APPLIED: True if the TuneD daemon applied the desired profile. (True/False/Unknown).
DEGRADED: True if any errors were reported during application of the TuneD profile (True/False/Unknown).
AGE: Time elapsed since the creation of Profile object.

4.5. Custom tuning specification

The custom resource (CR) for the Operator has two major sections. The first section, profile:, is a list of TuneD profiles and their names. The second, recommend:, defines the profile selection logic.

Multiple custom tuning specifications can co-exist as multiple CRs in the Operator’s namespace. The existence of new CRs or the deletion of old CRs is detected by the Operator. All existing custom tuning specifications are merged and appropriate objects for the containerized TuneD daemons are updated.

Management state

The Operator Management state is set by adjusting the default Tuned CR. By default, the Operator is in the Managed state and the spec.managementState field is not present in the default Tuned CR. Valid values for the Operator Management state are as follows:

Managed: the Operator will update its operands as configuration resources are updated
Unmanaged: the Operator will ignore changes to the configuration resources
Removed: the Operator will remove its operands and resources the Operator provisioned

Profile data

The profile: section lists TuneD profiles and their names.

profile:
- name: tuned_profile_1
  data: |
    # TuneD profile specification
    [main]
    summary=Description of tuned_profile_1 profile

    [sysctl]
    net.ipv4.ip_forward=1
    # ... other sysctl's or other TuneD daemon plugins supported by the containerized TuneD

# ...

- name: tuned_profile_n
  data: |
    # TuneD profile specification
    [main]
    summary=Description of tuned_profile_n profile

    # tuned_profile_n profile settings

Recommended profiles

The profile: selection logic is defined by the recommend: section of the CR. The recommend: section is a list of items to recommend the profiles based on a selection criteria.

recommend:
<recommend-item-1>
# ...
<recommend-item-n>

The individual items of the list:

- machineConfigLabels: 1
    <mcLabels> 2
  match: 3
    <match> 4
  priority: <priority> 5
  profile: <tuned_profile_name> 6
  operand: 7
    debug: <bool> 8

1: Optional.
2: A dictionary of key/value MachineConfig labels. The keys must be unique.
3: If omitted, profile match is assumed unless a profile with a higher priority matches first or machineConfigLabels is set.
4: An optional list.
5: Profile ordering priority. Lower numbers mean higher priority (0 is the highest priority).
6: A TuneD profile to apply on a match. For example tuned_profile_1.
7: Optional operand configuration.
8: Turn debugging on or off for the TuneD daemon. Options are true for on or false for off. The default is false.

<match> is an optional list recursively defined as follows:

- label: <label_name> 1
  value: <label_value> 2
  type: <label_type> 3
    <match> 4

1: Node or pod label name.
2: Optional node or pod label value. If omitted, the presence of <label_name> is enough to match.
3: Optional object type (node or pod). If omitted, node is assumed.
4: An optional <match> list.

If <match> is not omitted, all nested <match> sections must also evaluate to true. Otherwise, false is assumed and the profile with the respective <match> section will not be applied or recommended. Therefore, the nesting (child <match> sections) works as logical AND operator. Conversely, if any item of the <match> list matches, the entire <match> list evaluates to true. Therefore, the list acts as logical OR operator.

If machineConfigLabels is defined, machine config pool based matching is turned on for the given recommend: list item. <mcLabels> specifies the labels for a machine config. The machine config is created automatically to apply host settings, such as kernel boot parameters, for the profile <tuned_profile_name>. This involves finding all machine config pools with machine config selector matching <mcLabels> and setting the profile <tuned_profile_name> on all nodes that are assigned the found machine config pools. To target nodes that have both master and worker roles, you must use the master role.

The list items match and machineConfigLabels are connected by the logical OR operator. The match item is evaluated first in a short-circuit manner. Therefore, if it evaluates to true, the machineConfigLabels item is not considered.

Important

When using machine config pool based matching, it is advised to group nodes with the same hardware configuration into the same machine config pool. Not following this practice might result in TuneD operands calculating conflicting kernel parameters for two or more nodes sharing the same machine config pool.

Example: node or pod label based matching

- match:
  - label: tuned.openshift.io/elasticsearch
    match:
    - label: node-role.kubernetes.io/master
    - label: node-role.kubernetes.io/infra
    type: pod
  priority: 10
  profile: openshift-control-plane-es
- match:
  - label: node-role.kubernetes.io/master
  - label: node-role.kubernetes.io/infra
  priority: 20
  profile: openshift-control-plane
- priority: 30
  profile: openshift-node

The CR above is translated for the containerized TuneD daemon into its recommend.conf file based on the profile priorities. The profile with the highest priority (10) is openshift-control-plane-es and, therefore, it is considered first. The containerized TuneD daemon running on a given node looks to see if there is a pod running on the same node with the tuned.openshift.io/elasticsearch label set. If not, the entire <match> section evaluates as false. If there is such a pod with the label, in order for the <match> section to evaluate to true, the node label also needs to be node-role.kubernetes.io/master or node-role.kubernetes.io/infra.

If the labels for the profile with priority 10 matched, openshift-control-plane-es profile is applied and no other profile is considered. If the node/pod label combination did not match, the second highest priority profile (openshift-control-plane) is considered. This profile is applied if the containerized TuneD pod runs on a node with labels node-role.kubernetes.io/master or node-role.kubernetes.io/infra.

Finally, the profile openshift-node has the lowest priority of 30. It lacks the <match> section and, therefore, will always match. It acts as a profile catch-all to set openshift-node profile, if no other profile with higher priority matches on a given node.

Example: machine config pool based matching

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: openshift-node-custom
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - data: |
      [main]
      summary=Custom OpenShift node profile with an additional kernel parameter
      include=openshift-node
      [bootloader]
      cmdline_openshift_node_custom=+skew_tick=1
    name: openshift-node-custom

  recommend:
  - machineConfigLabels:
      machineconfiguration.openshift.io/role: "worker-custom"
    priority: 20
    profile: openshift-node-custom

To minimize node reboots, label the target nodes with a label the machine config pool’s node selector will match, then create the Tuned CR above and finally create the custom machine config pool itself.

4.6. Custom tuning examples

Using TuneD profiles from the default CR

The following CR applies custom node-level tuning for OpenShift Container Platform nodes with label tuned.openshift.io/ingress-node-label set to any value.

Example: custom tuning using the openshift-control-plane TuneD profile

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: ingress
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - data: |
      [main]
      summary=A custom OpenShift ingress profile
      include=openshift-control-plane
      [sysctl]
      net.ipv4.ip_local_port_range="1024 65535"
      net.ipv4.tcp_tw_reuse=1
    name: openshift-ingress
  recommend:
  - match:
    - label: tuned.openshift.io/ingress-node-label
    priority: 10
    profile: openshift-ingress

Important

Custom profile writers are strongly encouraged to include the default TuneD daemon profiles shipped within the default Tuned CR. The example above uses the default openshift-control-plane profile to accomplish this.

Using built-in TuneD profiles

Given the successful rollout of the NTO-managed daemon set, the TuneD operands all manage the same version of the TuneD daemon. To list the built-in TuneD profiles supported by the daemon, query any TuneD pod in the following way:

$ oc exec $tuned_pod -n openshift-cluster-node-tuning-operator -- find /usr/lib/tuned/ -name tuned.conf -printf '%h\n' | sed 's|^.*/||'

You can use the profile names retrieved by this in your custom tuning specification.

Example: using built-in hpc-compute TuneD profile

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: openshift-node-hpc-compute
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - data: |
      [main]
      summary=Custom OpenShift node profile for HPC compute workloads
      include=openshift-node,hpc-compute
    name: openshift-node-hpc-compute

  recommend:
  - match:
    - label: tuned.openshift.io/openshift-node-hpc-compute
    priority: 20
    profile: openshift-node-hpc-compute

In addition to the built-in hpc-compute profile, the example above includes the openshift-node TuneD daemon profile shipped within the default Tuned CR to use OpenShift-specific tuning for compute nodes.

4.7. Supported TuneD daemon plugins

Excluding the [main] section, the following TuneD plugins are supported when using custom profiles defined in the profile: section of the Tuned CR:

audio
cpu
disk
eeepc_she
modules
mounts
net
scheduler
scsi_host
selinux
sysctl
sysfs
usb
video
vm
bootloader

There is some dynamic tuning functionality provided by some of these plugins that is not supported. The following TuneD plugins are currently not supported:

script
systemd

Note

The TuneD bootloader plugin only supports Red Hat Enterprise Linux CoreOS (RHCOS) worker nodes.

Additional references

Chapter 5. Using CPU Manager and Topology Manager

CPU Manager manages groups of CPUs and constrains workloads to specific CPUs.

CPU Manager is useful for workloads that have some of these attributes:

Require as much CPU time as possible.
Are sensitive to processor cache misses.
Are low-latency network applications.
Coordinate with other processes and benefit from sharing a single processor cache.

Topology Manager collects hints from the CPU Manager, Device Manager, and other Hint Providers to align pod resources, such as CPU, SR-IOV VFs, and other device resources, for all Quality of Service (QoS) classes on the same non-uniform memory access (NUMA) node.

Topology Manager uses topology information from the collected hints to decide if a pod can be accepted or rejected on a node, based on the configured Topology Manager policy and pod resources requested.

Topology Manager is useful for workloads that use hardware accelerators to support latency-critical execution and high throughput parallel computation.

To use Topology Manager you must configure CPU Manager with the static policy.

5.1. Setting up CPU Manager

Procedure

Optional: Label a node:

# oc label node perf-node.example.com cpumanager=true

Edit the MachineConfigPool of the nodes where CPU Manager should be enabled. In this example, all workers have CPU Manager enabled:
```
# oc edit machineconfigpool worker
```

Add a label to the worker machine config pool:

metadata:
  creationTimestamp: 2020-xx-xxx
  generation: 3
  labels:
    custom-kubelet: cpumanager-enabled

Create a KubeletConfig, cpumanager-kubeletconfig.yaml, custom resource (CR). Refer to the label created in the previous step to have the correct nodes updated with the new kubelet config. See the machineConfigPoolSelector section:
```
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: cpumanager-enabled
spec:
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: cpumanager-enabled
  kubeletConfig:
     cpuManagerPolicy: static 1
     cpuManagerReconcilePeriod: 5s 2
```
1
Specify a policy:
none. This policy explicitly enables the existing default CPU affinity scheme, providing no affinity beyond what the scheduler does automatically. This is the default policy.
static. This policy allows containers in guaranteed pods with integer CPU requests. It also limits access to exclusive CPUs on the node. If static, you must use a lowercase s.
2
Optional. Specify the CPU Manager reconcile frequency. The default is 5s.
Create the dynamic kubelet config:
```
# oc create -f cpumanager-kubeletconfig.yaml
```
This adds the CPU Manager feature to the kubelet config and, if needed, the Machine Config Operator (MCO) reboots the node. To enable CPU Manager, a reboot is not needed.

Check for the merged kubelet config:

# oc get machineconfig 99-worker-XXXXXX-XXXXX-XXXX-XXXXX-kubelet -o json | grep ownerReference -A7

Example output

       "ownerReferences": [
            {
                "apiVersion": "machineconfiguration.openshift.io/v1",
                "kind": "KubeletConfig",
                "name": "cpumanager-enabled",
                "uid": "7ed5616d-6b72-11e9-aae1-021e1ce18878"
            }
        ]

Check the worker for the updated kubelet.conf:
```
# oc debug node/perf-node.example.com
sh-4.2# cat /host/etc/kubernetes/kubelet.conf | grep cpuManager
```
Example output
```
cpuManagerPolicy: static        1
cpuManagerReconcilePeriod: 5s   2
```
1
cpuManagerPolicy is defined when you create the KubeletConfig CR.
2
cpuManagerReconcilePeriod is defined when you create the KubeletConfig CR.

Create a pod that requests a core or multiple cores. Both limits and requests must have their CPU value set to a whole integer. That is the number of cores that will be dedicated to this pod:

# cat cpumanager-pod.yaml

Example output

apiVersion: v1
kind: Pod
metadata:
  generateName: cpumanager-
spec:
  containers:
  - name: cpumanager
    image: gcr.io/google_containers/pause-amd64:3.0
    resources:
      requests:
        cpu: 1
        memory: "1G"
      limits:
        cpu: 1
        memory: "1G"
  nodeSelector:
    cpumanager: "true"

Create the pod:
```
# oc create -f cpumanager-pod.yaml
```

Verify that the pod is scheduled to the node that you labeled:

# oc describe pod cpumanager

Example output

Name:               cpumanager-6cqz7
Namespace:          default
Priority:           0
PriorityClassName:  <none>
Node:  perf-node.example.com/xxx.xx.xx.xxx
...
 Limits:
      cpu:     1
      memory:  1G
    Requests:
      cpu:        1
      memory:     1G
...
QoS Class:       Guaranteed
Node-Selectors:  cpumanager=true

Verify that the cgroups are set up correctly. Get the process ID (PID) of the pause process:

# ├─init.scope
│ └─1 /usr/lib/systemd/systemd --switched-root --system --deserialize 17
└─kubepods.slice
  ├─kubepods-pod69c01f8e_6b74_11e9_ac0f_0a2b62178a22.slice
  │ ├─crio-b5437308f1a574c542bdf08563b865c0345c8f8c0b0a655612c.scope
  │ └─32706 /pause

Pods of quality of service (QoS) tier Guaranteed are placed within the kubepods.slice. Pods of other QoS tiers end up in child cgroups of kubepods:

# cd /sys/fs/cgroup/cpuset/kubepods.slice/kubepods-pod69c01f8e_6b74_11e9_ac0f_0a2b62178a22.slice/crio-b5437308f1ad1a7db0574c542bdf08563b865c0345c86e9585f8c0b0a655612c.scope
# for i in `ls cpuset.cpus tasks` ; do echo -n "$i "; cat $i ; done

Example output

cpuset.cpus 1
tasks 32706

Check the allowed CPU list for the task:

# grep ^Cpus_allowed_list /proc/32706/status

Example output

 Cpus_allowed_list:    1

Verify that another pod (in this case, the pod in the burstable QoS tier) on the system cannot run on the core allocated for the Guaranteed pod:

# cat /sys/fs/cgroup/cpuset/kubepods.slice/kubepods-besteffort.slice/kubepods-besteffort-podc494a073_6b77_11e9_98c0_06bba5c387ea.slice/crio-c56982f57b75a2420947f0afc6cafe7534c5734efc34157525fa9abbf99e3849.scope/cpuset.cpus
0
# oc describe node perf-node.example.com

Example output

...
Capacity:
 attachable-volumes-aws-ebs:  39
 cpu:                         2
 ephemeral-storage:           124768236Ki
 hugepages-1Gi:               0
 hugepages-2Mi:               0
 memory:                      8162900Ki
 pods:                        250
Allocatable:
 attachable-volumes-aws-ebs:  39
 cpu:                         1500m
 ephemeral-storage:           124768236Ki
 hugepages-1Gi:               0
 hugepages-2Mi:               0
 memory:                      7548500Ki
 pods:                        250
-------                               ----                           ------------  ----------  ---------------  -------------  ---
  default                                 cpumanager-6cqz7               1 (66%)       1 (66%)     1G (12%)         1G (12%)       29m

Allocated resources:
  (Total limits may be over 100 percent, i.e., overcommitted.)
  Resource                    Requests          Limits
  --------                    --------          ------
  cpu                         1440m (96%)       1 (66%)

This VM has two CPU cores. The system-reserved setting reserves 500 millicores, meaning that half of one core is subtracted from the total capacity of the node to arrive at the Node Allocatable amount. You can see that Allocatable CPU is 1500 millicores. This means you can run one of the CPU Manager pods since each will take one whole core. A whole core is equivalent to 1000 millicores. If you try to schedule a second pod, the system will accept the pod, but it will never be scheduled:

NAME                    READY   STATUS    RESTARTS   AGE
cpumanager-6cqz7        1/1     Running   0          33m
cpumanager-7qc2t        0/1     Pending   0          11s

5.2. Topology Manager policies

Topology Manager aligns Pod resources of all Quality of Service (QoS) classes by collecting topology hints from Hint Providers, such as CPU Manager and Device Manager, and using the collected hints to align the Pod resources.

Topology Manager supports four allocation policies, which you assign in the KubeletConfig custom resource (CR) named cpumanager-enabled:

none policy: This is the default policy and does not perform any topology alignment.
best-effort policy: For each container in a pod with the best-effort topology management policy, kubelet calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager stores the preferred NUMA Node affinity for that container. If the affinity is not preferred, Topology Manager stores this and admits the pod to the node.
restricted policy: For each container in a pod with the restricted topology management policy, kubelet calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager stores the preferred NUMA Node affinity for that container. If the affinity is not preferred, Topology Manager rejects this pod from the node, resulting in a pod in a Terminated state with a pod admission failure.
single-numa-node policy: For each container in a pod with the single-numa-node topology management policy, kubelet calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager determines if a single NUMA Node affinity is possible. If it is, the pod is admitted to the node. If a single NUMA Node affinity is not possible, the Topology Manager rejects the pod from the node. This results in a pod in a Terminated state with a pod admission failure.

5.3. Setting up Topology Manager

To use Topology Manager, you must configure an allocation policy in the KubeletConfig custom resource (CR) named cpumanager-enabled. This file might exist if you have set up CPU Manager. If the file does not exist, you can create the file.

Prequisites

Configure the CPU Manager policy to be static.

Procedure

To activate Topololgy Manager:

Configure the Topology Manager allocation policy in the custom resource.

$ oc edit KubeletConfig cpumanager-enabled

apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: cpumanager-enabled
spec:
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: cpumanager-enabled
  kubeletConfig:
     cpuManagerPolicy: static 1
     cpuManagerReconcilePeriod: 5s
     topologyManagerPolicy: single-numa-node 2

1: This parameter must be static with a lowercase s.
2: Specify your selected Topology Manager allocation policy. Here, the policy is single-numa-node. Acceptable values are: default, best-effort, restricted, single-numa-node.

5.4. Pod interactions with Topology Manager policies

The example Pod specs below help illustrate pod interactions with Topology Manager.

The following pod runs in the BestEffort QoS class because no resource requests or limits are specified.

spec:
  containers:
  - name: nginx
    image: nginx

The next pod runs in the Burstable QoS class because requests are less than limits.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
      requests:
        memory: "100Mi"

If the selected policy is anything other than none, Topology Manager would not consider either of these Pod specifications.

The last example pod below runs in the Guaranteed QoS class because requests are equal to limits.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
        example.com/device: "1"
      requests:
        memory: "200Mi"
        cpu: "2"
        example.com/device: "1"

Topology Manager would consider this pod. The Topology Manager would consult the hint providers, which are CPU Manager and Device Manager, to get topology hints for the pod.

Topology Manager will use this information to store the best topology for this container. In the case of this pod, CPU Manager and Device Manager will use this stored information at the resource allocation stage.

Chapter 6. Scheduling NUMA-aware workloads

Learn about NUMA-aware scheduling and how you can use it to deploy high performance workloads in an OpenShift Container Platform cluster.

Important

NUMA-aware scheduling is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

The NUMA Resources Operator allows you to schedule high-performance workloads in the same NUMA zone. It deploys a node resources exporting agent that reports on available cluster node NUMA resources, and a secondary scheduler that manages the workloads.

6.1. About NUMA-aware scheduling

Non-Uniform Memory Access (NUMA) is a compute platform architecture that allows different CPUs to access different regions of memory at different speeds. NUMA resource topology refers to the locations of CPUs, memory, and PCI devices relative to each other in the compute node. Co-located resources are said to be in the same NUMA zone. For high-performance applications, the cluster needs to process pod workloads in a single NUMA zone.

NUMA architecture allows a CPU with multiple memory controllers to use any available memory across CPU complexes, regardless of where the memory is located. This allows for increased flexibility at the expense of performance. A CPU processing a workload using memory that is outside its NUMA zone is slower than a workload processed in a single NUMA zone. Also, for I/O-constrained workloads, the network interface on a distant NUMA zone slows down how quickly information can reach the application. High-performance workloads, such as telecommunications workloads, cannot operate to specification under these conditions. NUMA-aware scheduling aligns the requested cluster compute resources (CPUs, memory, devices) in the same NUMA zone to process latency-sensitive or high-performance workloads efficiently. NUMA-aware scheduling also improves pod density per compute node for greater resource efficiency.

The default OpenShift Container Platform pod scheduler scheduling logic considers the available resources of the entire compute node, not individual NUMA zones. If the most restrictive resource alignment is requested in the kubelet topology manager, error conditions can occur when admitting the pod to a node. Conversely, if the most restrictive resource alignment is not requested, the pod can be admitted to the node without proper resource alignment, leading to worse or unpredictable performance. For example, runaway pod creation with Topology Affinity Error statuses can occur when the pod scheduler makes suboptimal scheduling decisions for guaranteed pod workloads by not knowing if the pod’s requested resources are available. Scheduling mismatch decisions can cause indefinite pod startup delays. Also, depending on the cluster state and resource allocation, poor pod scheduling decisions can cause extra load on the cluster because of failed startup attempts.

The NUMA Resources Operator deploys a custom NUMA resources secondary scheduler and other resources to mitigate against the shortcomings of the default OpenShift Container Platform pod scheduler. The following diagram provides a high-level overview of NUMA-aware pod scheduling.

Figure 6.1. NUMA-aware scheduling overview

Diagram of NUMA-aware scheduling that shows how the various components interact with each other in the cluster

NodeResourceTopology API: The NodeResourceTopology API describes the available NUMA zone resources in each compute node.
NUMA-aware scheduler: The NUMA-aware secondary scheduler receives information about the available NUMA zones from the NodeResourceTopology API and schedules high-performance workloads on a node where it can be optimally processed.
Node topology exporter: The node topology exporter exposes the available NUMA zone resources for each compute node to the NodeResourceTopology API. The node topology exporter daemon tracks the resource allocation from the kubelet by using the PodResources API.
PodResources API: The PodResources API is local to each node and exposes the resource topology and available resources to the kubelet.

Additional resources

For more information about running secondary pod schedulers in your cluster and how to deploy pods with a secondary pod scheduler, see Scheduling pods using a secondary scheduler.

6.2. Installing the NUMA Resources Operator

NUMA Resources Operator deploys resources that allow you to schedule NUMA-aware workloads and deployments. You can install the NUMA Resources Operator using the OpenShift Container Platform CLI or the web console.

6.2.1. Installing the NUMA Resources Operator using the CLI

As a cluster administrator, you can install the Operator using the CLI.

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.

Procedure

Create a namespace for the NUMA Resources Operator:
1. Save the following YAML in the nro-namespace.yaml file:
```
apiVersion: v1
kind: Namespace
metadata:
  name: openshift-numaresources
```
2. Create the Namespace CR by running the following command:
```
$ oc create -f nro-namespace.yaml
```

Create the Operator group for the NUMA Resources Operator:

Save the following YAML in the nro-operatorgroup.yaml file:

apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: numaresources-operator
  namespace: openshift-numaresources
spec:
  targetNamespaces:
  - openshift-numaresources

Create the OperatorGroup CR by running the following command:
```
$ oc create -f nro-operatorgroup.yaml
```

Create the subscription for the NUMA Resources Operator:

Save the following YAML in the nro-sub.yaml file:

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: numaresources-operator
  namespace: openshift-numaresources
spec:
  channel: "{product-version}"
  name: numaresources-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace

Create the Subscription CR by running the following command:
```
$ oc create -f nro-sub.yaml
```

Verification

Verify that the installation succeeded by inspecting the CSV resource in the openshift-numaresources namespace. Run the following command:

$ oc get csv -n openshift-numaresources

Example output

NAME                            DISPLAY                      VERSION   REPLACES   PHASE
numaresources-operator.v4.10.0  NUMA Resources Operator      4.10.0               Succeeded

6.2.2. Installing the NUMA Resources Operator using the web console

As a cluster administrator, you can install the NUMA Resources Operator using the web console.

Procedure

Install the NUMA Resources Operator using the OpenShift Container Platform web console:
1. In the OpenShift Container Platform web console, click Operators → OperatorHub.
2. Choose NUMA Resources Operator from the list of available Operators, and then click Install.
Optional: Verify that the NUMA Resources Operator installed successfully:
1. Switch to the Operators → Installed Operators page.
2. Ensure that NUMA Resources Operator is listed in the default project with a Status of InstallSucceeded.
  Note
  During installation an Operator might display a Failed status. If the installation later succeeds with an InstallSucceeded message, you can ignore the Failed message.
  If the Operator does not appear as installed, to troubleshoot further:
  - Go to the Operators → Installed Operators page and inspect the Operator Subscriptions and Install Plans tabs for any failure or errors under Status.
  - Go to the Workloads → Pods page and check the logs for pods in the default project.

6.3. Creating the NUMAResourcesOperator custom resource

When you have installed the NUMA Resources Operator, then create the NUMAResourcesOperator custom resource (CR) that instructs the NUMA Resources Operator to install all the cluster infrastructure needed to support the NUMA-aware scheduler, including daemon sets and APIs.

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.
Install the NUMA Resources Operator.

Procedure

Create the MachineConfigPool custom resource that enables custom kubelet configurations for worker nodes:

Save the following YAML in the nro-machineconfig.yaml file:

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
  labels:
    cnf-worker-tuning: enabled
    machineconfiguration.openshift.io/mco-built-in: ""
    pools.operator.machineconfiguration.openshift.io/worker: ""
  name: worker
spec:
  machineConfigSelector:
    matchLabels:
      machineconfiguration.openshift.io/role: worker
  nodeSelector:
    matchLabels:
      node-role.kubernetes.io/worker: ""

Create the MachineConfigPool CR by running the following command:
```
$ oc create -f nro-machineconfig.yaml
```

Create the NUMAResourcesOperator custom resource:

Save the following YAML in the nrop.yaml file:

apiVersion: nodetopology.openshift.io/v1alpha1
kind: NUMAResourcesOperator
metadata:
  name: numaresourcesoperator
spec:
  nodeGroups:
  - machineConfigPoolSelector:
      matchLabels:
        pools.operator.machineconfiguration.openshift.io/worker: "" 1

1: Should match the label applied to worker nodes in the related MachineConfigPool CR.

Create the NUMAResourcesOperator CR by running the following command:
```
$ oc create -f nrop.yaml
```

Verification

Verify that the NUMA Resources Operator deployed successfully by running the following command:

$ oc get numaresourcesoperators.nodetopology.openshift.io

Example output

NAME                    AGE
numaresourcesoperator   10m

6.4. Deploying the NUMA-aware secondary pod scheduler

After you install the NUMA Resources Operator, do the following to deploy the NUMA-aware secondary pod scheduler:

Configure the pod admittance policy for the required machine profile
Create the required machine config pool
Deploy the NUMA-aware secondary scheduler

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.
Install the NUMA Resources Operator.

Procedure

Create the KubeletConfig custom resource that configures the pod admittance policy for the machine profile:

Save the following YAML in the nro-kubeletconfig.yaml file:

apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: cnf-worker-tuning
spec:
  machineConfigPoolSelector:
    matchLabels:
      cnf-worker-tuning: enabled
  kubeletConfig:
    cpuManagerPolicy: "static" 1
    cpuManagerReconcilePeriod: "5s"
    reservedSystemCPUs: "0,1"
    memoryManagerPolicy: "Static" 2
    evictionHard:
      memory.available: "100Mi"
    kubeReserved:
      memory: "512Mi"
    reservedMemory:
      - numaNode: 0
        limits:
          memory: "1124Mi"
    systemReserved:
      memory: "512Mi"
    topologyManagerPolicy: "single-numa-node" 3
    topologyManagerScope: "pod"

1: For cpuManagerPolicy, static must use a lowercase s.
2: For memoryManagerPolicy, Static must use an uppercase S.
3: topologyManagerPolicy must be set to single-numa-node.

Create the KubeletConfig custom resource (CR) by running the following command:
```
$ oc create -f nro-kubeletconfig.yaml
```

Create the NUMAResourcesScheduler custom resource that deploys the NUMA-aware custom pod scheduler:

Save the following YAML in the nro-scheduler.yaml file:

apiVersion: nodetopology.openshift.io/v1alpha1
kind: NUMAResourcesScheduler
metadata:
  name: numaresourcesscheduler
spec:
  imageSpec: "registry.redhat.io/openshift4/noderesourcetopology-scheduler-container-rhel8:v4.10"

Create the NUMAResourcesScheduler CR by running the following command:
```
$ oc create -f nro-scheduler.yaml
```

Verification

Verify that the required resources deployed successfully by running the following command:

$ oc get all -n openshift-numaresources

Example output

NAME                                                    READY   STATUS    RESTARTS   AGE
pod/numaresources-controller-manager-7575848485-bns4s   1/1     Running   0          13m
pod/numaresourcesoperator-worker-dvj4n                  2/2     Running   0          16m
pod/numaresourcesoperator-worker-lcg4t                  2/2     Running   0          16m
pod/secondary-scheduler-56994cf6cf-7qf4q                1/1     Running   0          16m
NAME                                          DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR                     AGE
daemonset.apps/numaresourcesoperator-worker   2         2         2       2            2           node-role.kubernetes.io/worker=   16m
NAME                                               READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/numaresources-controller-manager   1/1     1            1           13m
deployment.apps/secondary-scheduler                1/1     1            1           16m
NAME                                                          DESIRED   CURRENT   READY   AGE
replicaset.apps/numaresources-controller-manager-7575848485   1         1         1       13m
replicaset.apps/secondary-scheduler-56994cf6cf                1         1         1       16m

6.5. Scheduling workloads with the NUMA-aware scheduler

You can schedule workloads with the NUMA-aware scheduler using Deployment CRs that specify the minimum required resources to process the workload.

The following example deployment uses NUMA-aware scheduling for a sample workload.

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.
Install the NUMA Resources Operator and deploy the NUMA-aware secondary scheduler.

Procedure

Get the name of the NUMA-aware scheduler that is deployed in the cluster by running the following command:

$ oc get numaresourcesschedulers.nodetopology.openshift.io numaresourcesscheduler -o json | jq '.status.schedulerName'

Example output

topo-aware-scheduler

Create a Deployment CR that uses scheduler named topo-aware-scheduler, for example:

Save the following YAML in the nro-deployment.yaml file:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: numa-deployment-1
  namespace: openshift-numaresources
spec:
  replicas: 1
  selector:
    matchLabels:
      app: test
  template:
    metadata:
      labels:
        app: test
    spec:
      schedulerName: topo-aware-scheduler 1
      containers:
      - name: ctnr
        image: quay.io/openshifttest/hello-openshift:openshift
        imagePullPolicy: IfNotPresent
        resources:
          limits:
            memory: "100Mi"
            cpu: "10"
          requests:
            memory: "100Mi"
            cpu: "10"
      - name: ctnr2
        image: registry.access.redhat.com/rhel:latest
        imagePullPolicy: IfNotPresent
        command: ["/bin/sh", "-c"]
        args: [ "while true; do sleep 1h; done;" ]
        resources:
          limits:
            memory: "100Mi"
            cpu: "8"
          requests:
            memory: "100Mi"
            cpu: "8"

1: schedulerName must match the name of the NUMA-aware scheduler that is deployed in your cluster, for example topo-aware-scheduler.

Create the Deployment CR by running the following command:
```
$ oc create -f nro-deployment.yaml
```

Verification

Verify that the deployment was successful:

$ oc get pods -n openshift-numaresources

Example output

NAME                                                READY   STATUS    RESTARTS   AGE
numa-deployment-1-56954b7b46-pfgw8                  2/2     Running   0          129m
numaresources-controller-manager-7575848485-bns4s   1/1     Running   0          15h
numaresourcesoperator-worker-dvj4n                  2/2     Running   0          18h
numaresourcesoperator-worker-lcg4t                  2/2     Running   0          16h
secondary-scheduler-56994cf6cf-7qf4q                1/1     Running   0          18h

Verify that the topo-aware-scheduler is scheduling the deployed pod by running the following command:
```
$ oc describe pod numa-deployment-1-56954b7b46-pfgw8 -n openshift-numaresources
```
Example output
```
Events:
  Type    Reason          Age   From                  Message
  ----    ------          ----  ----                  -------
  Normal  Scheduled       130m  topo-aware-scheduler  Successfully assigned openshift-numaresources/numa-deployment-1-56954b7b46-pfgw8 to compute-0.example.com
```
Note
Deployments that request more resources than is available for scheduling will fail with a MinimumReplicasUnavailable error. The deployment succeeds when the required resources become available. Pods remain in the Pending state until the required resources are available.

Verify that the expected allocated resources are listed for the node. Run the following command:

$ oc describe noderesourcetopologies.topology.node.k8s.io

Example output

...

Zones:
  Costs:
    Name:   node-0
    Value:  10
    Name:   node-1
    Value:  21
  Name:     node-0
  Resources:
    Allocatable:  39
    Available:    21 1
    Capacity:     40
    Name:         cpu
    Allocatable:  6442450944
    Available:    6442450944
    Capacity:     6442450944
    Name:         hugepages-1Gi
    Allocatable:  134217728
    Available:    134217728
    Capacity:     134217728
    Name:         hugepages-2Mi
    Allocatable:  262415904768
    Available:    262206189568
    Capacity:     270146007040
    Name:         memory
  Type:           Node

1: The Available capacity is reduced because of the resources that have been allocated to the guaranteed pod.

Resources consumed by guaranteed pods are subtracted from the available node resources listed under noderesourcetopologies.topology.node.k8s.io.

Resource allocations for pods with a Best-effort or Burstable quality of service (qosClass) are not reflected in the NUMA node resources under noderesourcetopologies.topology.node.k8s.io. If a pod’s consumed resources are not reflected in the node resource calculation, verify that the pod has qosClass of Guaranteed by running the following command:
```
$ oc get pod <pod_name> -n <pod_namespace> -o jsonpath="{ .status.qosClass }"
```
Example output
```
Guaranteed
```

6.6. Troubleshooting NUMA-aware scheduling

To troubleshoot common problems with NUMA-aware pod scheduling, perform the following steps.

Prerequisites

Install the OpenShift Container Platform CLI (oc).
Log in as a user with cluster-admin privileges.
Install the NUMA Resources Operator and deploy the NUMA-aware secondary scheduler.

Procedure

Verify that the noderesourcetopologies CRD is deployed in the cluster by running the following command:

$ oc get crd | grep noderesourcetopologies

Example output

NAME                                                              CREATED AT
noderesourcetopologies.topology.node.k8s.io                       2022-01-18T08:28:06Z

Check that the NUMA-aware scheduler name matches the name specified in your NUMA-aware workloads by running the following command:
```
$ oc get numaresourcesschedulers.nodetopology.openshift.io numaresourcesscheduler -o json | jq '.status.schedulerName'
```
Example output
```
topo-aware-scheduler
```
Verify that NUMA-aware scheduable nodes have the noderesourcetopologies CR applied to them. Run the following command:
```
$ oc get noderesourcetopologies.topology.node.k8s.io
```
Example output
```
NAME                    AGE
compute-0.example.com   17h
compute-1.example.com   17h
```
Note
The number of nodes should equal the number of worker nodes that are configured by the machine config pool (mcp) worker definition.

Verify the NUMA zone granularity for all scheduable nodes by running the following command:

$ oc get noderesourcetopologies.topology.node.k8s.io -o yaml

Example output

apiVersion: v1
items:
- apiVersion: topology.node.k8s.io/v1alpha1
  kind: NodeResourceTopology
  metadata:
    annotations:
      k8stopoawareschedwg/rte-update: periodic
    creationTimestamp: "2022-06-16T08:55:38Z"
    generation: 63760
    name: worker-0
    resourceVersion: "8450223"
    uid: 8b77be46-08c0-4074-927b-d49361471590
  topologyPolicies:
  - SingleNUMANodeContainerLevel
  zones:
  - costs:
    - name: node-0
      value: 10
    - name: node-1
      value: 21
    name: node-0
    resources:
    - allocatable: "38"
      available: "38"
      capacity: "40"
      name: cpu
    - allocatable: "134217728"
      available: "134217728"
      capacity: "134217728"
      name: hugepages-2Mi
    - allocatable: "262352048128"
      available: "262352048128"
      capacity: "270107316224"
      name: memory
    - allocatable: "6442450944"
      available: "6442450944"
      capacity: "6442450944"
      name: hugepages-1Gi
    type: Node
  - costs:
    - name: node-0
      value: 21
    - name: node-1
      value: 10
    name: node-1
    resources:
    - allocatable: "268435456"
      available: "268435456"
      capacity: "268435456"
      name: hugepages-2Mi
    - allocatable: "269231067136"
      available: "269231067136"
      capacity: "270573244416"
      name: memory
    - allocatable: "40"
      available: "40"
      capacity: "40"
      name: cpu
    - allocatable: "1073741824"
      available: "1073741824"
      capacity: "1073741824"
      name: hugepages-1Gi
    type: Node
- apiVersion: topology.node.k8s.io/v1alpha1
  kind: NodeResourceTopology
  metadata:
    annotations:
      k8stopoawareschedwg/rte-update: periodic
    creationTimestamp: "2022-06-16T08:55:37Z"
    generation: 62061
    name: worker-1
    resourceVersion: "8450129"
    uid: e8659390-6f8d-4e67-9a51-1ea34bba1cc3
  topologyPolicies:
  - SingleNUMANodeContainerLevel
  zones: 1
  - costs:
    - name: node-0
      value: 10
    - name: node-1
      value: 21
    name: node-0
    resources: 2
    - allocatable: "38"
      available: "38"
      capacity: "40"
      name: cpu
    - allocatable: "6442450944"
      available: "6442450944"
      capacity: "6442450944"
      name: hugepages-1Gi
    - allocatable: "134217728"
      available: "134217728"
      capacity: "134217728"
      name: hugepages-2Mi
    - allocatable: "262391033856"
      available: "262391033856"
      capacity: "270146301952"
      name: memory
    type: Node
  - costs:
    - name: node-0
      value: 21
    - name: node-1
      value: 10
    name: node-1
    resources:
    - allocatable: "40"
      available: "40"
      capacity: "40"
      name: cpu
    - allocatable: "1073741824"
      available: "1073741824"
      capacity: "1073741824"
      name: hugepages-1Gi
    - allocatable: "268435456"
      available: "268435456"
      capacity: "268435456"
      name: hugepages-2Mi
    - allocatable: "269192085504"
      available: "269192085504"
      capacity: "270534262784"
      name: memory
    type: Node
kind: List
metadata:
  resourceVersion: ""
  selfLink: ""

1: Each stanza under zones describes the resources for a single NUMA zone.
2: resources describes the current state of the NUMA zone resources. Check that resources listed under items.zones.resources.available correspond to the exclusive NUMA zone resources allocated to each guaranteed pod.

6.6.1. Checking the NUMA-aware scheduler logs

Troubleshoot problems with the NUMA-aware scheduler by reviewing the logs. If required, you can increase the scheduler log level by modifying the spec.logLevel field of the NUMAResourcesScheduler resource. Acceptable values are Normal, Debug, and Trace, with Trace being the most verbose option.

Note

To change the log level of the secondary scheduler, delete the running scheduler resource and re-deploy it with the changed log level. The scheduler is unavailable for scheduling new workloads during this downtime.

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.

Procedure

Delete the currently running NUMAResourcesScheduler resource:
1. Get the active NUMAResourcesScheduler by running the following command:
```
$ oc get NUMAResourcesScheduler
```
  Example output
```
NAME                     AGE
numaresourcesscheduler   90m
```
2. Delete the secondary scheduler resource by running the following command:
```
$ oc delete NUMAResourcesScheduler numaresourcesscheduler
```
  Example output
```
numaresourcesscheduler.nodetopology.openshift.io "numaresourcesscheduler" deleted
```

Save the following YAML in the file nro-scheduler-debug.yaml. This example changes the log level to Debug:

apiVersion: nodetopology.openshift.io/v1alpha1
kind: NUMAResourcesScheduler
metadata:
  name: numaresourcesscheduler
spec:
  imageSpec: "registry.redhat.io/openshift4/noderesourcetopology-scheduler-container-rhel8:v4.10"
  logLevel: Debug

Create the updated Debug logging NUMAResourcesScheduler resource by running the following command:

$ oc create -f nro-scheduler-debug.yaml

Example output

numaresourcesscheduler.nodetopology.openshift.io/numaresourcesscheduler created

Verification steps

Check that the NUMA-aware scheduler was successfully deployed:

Run the following command to check that the CRD is created succesfully:

$ oc get crd | grep numaresourcesschedulers

Example output

NAME                                                              CREATED AT
numaresourcesschedulers.nodetopology.openshift.io                 2022-02-25T11:57:03Z

Check that the new custom scheduler is available by running the following command:

$ oc get numaresourcesschedulers.nodetopology.openshift.io

Example output

NAME                     AGE
numaresourcesscheduler   3h26m

Check that the logs for the scheduler shows the increased log level:

Get the list of pods running in the openshift-numaresources namespace by running the following command:

$ oc get pods -n openshift-numaresources

Example output

NAME                                               READY   STATUS    RESTARTS   AGE
numaresources-controller-manager-d87d79587-76mrm   1/1     Running   0          46h
numaresourcesoperator-worker-5wm2k                 2/2     Running   0          45h
numaresourcesoperator-worker-pb75c                 2/2     Running   0          45h
secondary-scheduler-7976c4d466-qm4sc               1/1     Running   0          21m

Get the logs for the secondary scheduler pod by running the following command:

$ oc logs secondary-scheduler-7976c4d466-qm4sc -n openshift-numaresources

Example output

...
I0223 11:04:55.614788       1 reflector.go:535] k8s.io/client-go/informers/factory.go:134: Watch close - *v1.Namespace total 11 items received
I0223 11:04:56.609114       1 reflector.go:535] k8s.io/client-go/informers/factory.go:134: Watch close - *v1.ReplicationController total 10 items received
I0223 11:05:22.626818       1 reflector.go:535] k8s.io/client-go/informers/factory.go:134: Watch close - *v1.StorageClass total 7 items received
I0223 11:05:31.610356       1 reflector.go:535] k8s.io/client-go/informers/factory.go:134: Watch close - *v1.PodDisruptionBudget total 7 items received
I0223 11:05:31.713032       1 eventhandlers.go:186] "Add event for scheduled pod" pod="openshift-marketplace/certified-operators-thtvq"
I0223 11:05:53.461016       1 eventhandlers.go:244] "Delete event for scheduled pod" pod="openshift-marketplace/certified-operators-thtvq"

6.6.2. Troubleshooting the resource topology exporter

Troubleshoot noderesourcetopologies objects where unexpected results are occurring by inspecting the corresponding resource-topology-exporter logs.

Note

It is recommended that NUMA resource topology exporter instances in the cluster are named for nodes they refer to. For example, a worker node with the name worker should have a corresponding noderesourcetopologies object called worker.

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.

Procedure

Get the daemonsets managed by the NUMA Resources Operator. Each daemonset has a corresponding nodeGroup in the NUMAResourcesOperator CR. Run the following command:

$ oc get numaresourcesoperators.nodetopology.openshift.io numaresourcesoperator -o jsonpath="{.status.daemonsets[0]}"

Example output

{"name":"numaresourcesoperator-worker","namespace":"openshift-numaresources"}

Get the label for the daemonset of interest using the value for name from the previous step:

$ oc get ds -n openshift-numaresources numaresourcesoperator-worker -o jsonpath="{.spec.selector.matchLabels}"

Example output

{"name":"resource-topology"}

Get the pods using the resource-topology label by running the following command:

$ oc get pods -n openshift-numaresources -l name=resource-topology -o wide

Example output

NAME                                 READY   STATUS    RESTARTS   AGE    IP            NODE
numaresourcesoperator-worker-5wm2k   2/2     Running   0          2d1h   10.135.0.64   compute-0.example.com
numaresourcesoperator-worker-pb75c   2/2     Running   0          2d1h   10.132.2.33   compute-1.example.com

Examine the logs of the resource-topology-exporter container running on the worker pod that corresponds to the node you are troubleshooting. Run the following command:

$ oc logs -n openshift-numaresources -c resource-topology-exporter numaresourcesoperator-worker-pb75c

Example output

I0221 13:38:18.334140       1 main.go:206] using sysinfo:
reservedCpus: 0,1
reservedMemory:
  "0": 1178599424
I0221 13:38:18.334370       1 main.go:67] === System information ===
I0221 13:38:18.334381       1 sysinfo.go:231] cpus: reserved "0-1"
I0221 13:38:18.334493       1 sysinfo.go:237] cpus: online "0-103"
I0221 13:38:18.546750       1 main.go:72]
cpus: allocatable "2-103"
hugepages-1Gi:
  numa cell 0 -> 6
  numa cell 1 -> 1
hugepages-2Mi:
  numa cell 0 -> 64
  numa cell 1 -> 128
memory:
  numa cell 0 -> 45758Mi
  numa cell 1 -> 48372Mi

6.6.3. Correcting a missing resource topology exporter config map

If you install the NUMA Resources Operator in a cluster with misconfigured cluster settings, in some circumstances, the Operator is shown as active but the logs of the resource topology exporter (RTE) daemon set pods show that the configuration for the RTE is missing, for example:

Info: couldn't find configuration in "/etc/resource-topology-exporter/config.yaml"

This log message indicates that the kubeletconfig with the required configuration was not properly applied in the cluster, resulting in a missing RTE configmap. For example, the following cluster is missing a numaresourcesoperator-worker configmap custom resource (CR):

$ oc get configmap

Example output

NAME                           DATA   AGE
0e2a6bd3.openshift-kni.io      0      6d21h
kube-root-ca.crt               1      6d21h
openshift-service-ca.crt       1      6d21h
topo-aware-scheduler-config    1      6d18h

In a correctly configured cluster, oc get configmap also returns a numaresourcesoperator-worker configmap CR.

Prerequisites

Install the OpenShift Container Platform CLI (oc).
Log in as a user with cluster-admin privileges.
Install the NUMA Resources Operator and deploy the NUMA-aware secondary scheduler.

Procedure

Compare the values for spec.machineConfigPoolSelector.matchLabels in kubeletconfig and metadata.labels in the MachineConfigPool (mcp) worker CR using the following commands:
1. Check the kubeletconfig labels by running the following command:
```
$ oc get kubeletconfig -o yaml
```
  Example output
```
machineConfigPoolSelector:
  matchLabels:
    cnf-worker-tuning: enabled
```
2. Check the mcp labels by running the following command:
```
$ oc get mcp worker -o yaml
```
  Example output
```
labels:
  machineconfiguration.openshift.io/mco-built-in: ""
  pools.operator.machineconfiguration.openshift.io/worker: ""
```
  The cnf-worker-tuning: enabled label is not present in the MachineConfigPool object.

Edit the MachineConfigPool CR to include the missing label, for example:

$ oc edit mcp worker -o yaml

Example output

labels:
  machineconfiguration.openshift.io/mco-built-in: ""
  pools.operator.machineconfiguration.openshift.io/worker: ""
  cnf-worker-tuning: enabled

Apply the label changes and wait for the cluster to apply the updated configuration. Run the following command:

Verification

Check that the missing numaresourcesoperator-worker configmap CR is applied:

$ oc get configmap

Example output

NAME                           DATA   AGE
0e2a6bd3.openshift-kni.io      0      6d21h
kube-root-ca.crt               1      6d21h
numaresourcesoperator-worker   1      5m
openshift-service-ca.crt       1      6d21h
topo-aware-scheduler-config    1      6d18h

Chapter 7. Scaling the Cluster Monitoring Operator

OpenShift Container Platform exposes metrics that the Cluster Monitoring Operator collects and stores in the Prometheus-based monitoring stack. As an administrator, you can view system resources, containers, and components metrics in one dashboard interface, Grafana.

7.1. Prometheus database storage requirements

Red Hat performed various tests for different scale sizes.

Note

The Prometheus storage requirements below are not prescriptive. Higher resource consumption might be observed in your cluster depending on workload activity and resource use.

Table 7.1. Prometheus Database storage requirements based on number of nodes/pods in the cluster

Number of Nodes	Number of pods	Prometheus storage growth per day	Prometheus storage growth per 15 days	RAM Space (per scale size)	Network (per tsdb chunk)
50	1800	6.3 GB	94 GB	6 GB	16 MB
100	3600	13 GB	195 GB	10 GB	26 MB
150	5400	19 GB	283 GB	12 GB	36 MB
200	7200	25 GB	375 GB	14 GB	46 MB

Approximately 20 percent of the expected size was added as overhead to ensure that the storage requirements do not exceed the calculated value.

The above calculation is for the default OpenShift Container Platform Cluster Monitoring Operator.

Note

CPU utilization has minor impact. The ratio is approximately 1 core out of 40 per 50 nodes and 1800 pods.

Recommendations for OpenShift Container Platform

Use at least three infrastructure (infra) nodes.
Use at least three openshift-container-storage nodes with non-volatile memory express (NVMe) drives.

7.2. Configuring cluster monitoring

You can increase the storage capacity for the Prometheus component in the cluster monitoring stack.

Procedure

To increase the storage capacity for Prometheus:

Create a YAML configuration file, cluster-monitoring-config.yaml. For example:

apiVersion: v1
kind: ConfigMap
data:
  config.yaml: |
    prometheusK8s:
      retention: {{PROMETHEUS_RETENTION_PERIOD}} 1
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      volumeClaimTemplate:
        spec:
          storageClassName: {{STORAGE_CLASS}} 2
          resources:
            requests:
              storage: {{PROMETHEUS_STORAGE_SIZE}} 3
    alertmanagerMain:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      volumeClaimTemplate:
        spec:
          storageClassName: {{STORAGE_CLASS}} 4
          resources:
            requests:
              storage: {{ALERTMANAGER_STORAGE_SIZE}} 5
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring

1: A typical value is PROMETHEUS_RETENTION_PERIOD=15d. Units are measured in time using one of these suffixes: s, m, h, d.
2 4: The storage class for your cluster.
3: A typical value is PROMETHEUS_STORAGE_SIZE=2000Gi. Storage values can be a plain integer or as a fixed-point integer using one of these suffixes: E, P, T, G, M, K. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki.
5: A typical value is ALERTMANAGER_STORAGE_SIZE=20Gi. Storage values can be a plain integer or as a fixed-point integer using one of these suffixes: E, P, T, G, M, K. You can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki.

Add values for the retention period, storage class, and storage sizes.
Save the file.

Apply the changes by running:

$ oc create -f cluster-monitoring-config.yaml

Chapter 8. Planning your environment according to object maximums

Consider the following tested object maximums when you plan your OpenShift Container Platform cluster.

These guidelines are based on the largest possible cluster. For smaller clusters, the maximums are lower. There are many factors that influence the stated thresholds, including the etcd version or storage data format.

Important

These guidelines apply to OpenShift Container Platform with software-defined networking (SDN), not Open Virtual Network (OVN).

In most cases, exceeding these numbers results in lower overall performance. It does not necessarily mean that the cluster will fail.

Warning

Clusters that experience rapid change, such as those with many starting and stopping pods, can have a lower practical maximum size than documented.

8.1. OpenShift Container Platform tested cluster maximums for major releases

Tested Cloud Platforms for OpenShift Container Platform 3.x: Red Hat OpenStack Platform (RHOSP), Amazon Web Services and Microsoft Azure. Tested Cloud Platforms for OpenShift Container Platform 4.x: Amazon Web Services, Microsoft Azure and Google Cloud Platform.

Maximum type	3.x tested maximum	4.x tested maximum
Number of nodes	2,000	2,000 ^[1]
Number of pods ^[2]	150,000	150,000
Number of pods per node	250	500 ^[3]
Number of pods per core	There is no default value.	There is no default value.
Number of namespaces ^[4]	10,000	10,000
Number of builds	10,000 (Default pod RAM 512 Mi) - Pipeline Strategy	10,000 (Default pod RAM 512 Mi) - Source-to-Image (S2I) build strategy
Number of pods per namespace ^[5]	25,000	25,000
Number of routes and back ends per Ingress Controller	2,000 per router	2,000 per router
Number of secrets	80,000	80,000
Number of config maps	90,000	90,000
Number of services ^[6]	10,000	10,000
Number of services per namespace	5,000	5,000
Number of back-ends per service	5,000	5,000
Number of deployments per namespace ^[5]	2,000	2,000
Number of build configs	12,000	12,000
Number of custom resource definitions (CRD)	There is no default value.	512 ^[7]

Pause pods were deployed to stress the control plane components of OpenShift Container Platform at 2000 node scale.
The pod count displayed here is the number of test pods. The actual number of pods depends on the application’s memory, CPU, and storage requirements.
This was tested on a cluster with 100 worker nodes with 500 pods per worker node. The default maxPods is still 250. To get to 500 maxPods, the cluster must be created with a maxPods set to 500 using a custom kubelet config. If you need 500 user pods, you need a hostPrefix of 22 because there are 10-15 system pods already running on the node. The maximum number of pods with attached persistent volume claims (PVC) depends on storage backend from where PVC are allocated. In our tests, only OpenShift Data Foundation v4 (OCS v4) was able to satisfy the number of pods per node discussed in this document.
When there are a large number of active projects, etcd might suffer from poor performance if the keyspace grows excessively large and exceeds the space quota. Periodic maintenance of etcd, including defragmentation, is highly recommended to free etcd storage.
There are a number of control loops in the system that must iterate over all objects in a given namespace as a reaction to some changes in state. Having a large number of objects of a given type in a single namespace can make those loops expensive and slow down processing given state changes. The limit assumes that the system has enough CPU, memory, and disk to satisfy the application requirements.
Each service port and each service back-end has a corresponding entry in iptables. The number of back-ends of a given service impact the size of the endpoints objects, which impacts the size of data that is being sent all over the system.
OpenShift Container Platform has a limit of 512 total custom resource definitions (CRD), including those installed by OpenShift Container Platform, products integrating with OpenShift Container Platform and user created CRDs. If there are more than 512 CRDs created, then there is a possibility that oc commands requests may be throttled.

Note

Red Hat does not provide direct guidance on sizing your OpenShift Container Platform cluster. This is because determining whether your cluster is within the supported bounds of OpenShift Container Platform requires careful consideration of all the multidimensional factors that limit the cluster scale.

8.2. OpenShift Container Platform environment and configuration on which the cluster maximums are tested

8.2.1. AWS cloud platform

Node	Flavor	vCPU	RAM(GiB)	Disk type	Disk size(GiB)/IOS	Count	Region
Control plane/etcd ^[1]	r5.4xlarge	16	128	gp3	220	3	us-west-2
Infra ^[2]	m5.12xlarge	48	192	gp3	100	3	us-west-2
Workload ^[3]	m5.4xlarge	16	64	gp3	500 ^[4]	1	us-west-2
Compute	m5.2xlarge	8	32	gp3	100	3/25/250/500 ^[5]	us-west-2

gp3 disks with a baseline performance of 3000 IOPS and 125 MiB per second are used for control plane/etcd nodes because etcd is latency sensitive. gp3 volumes do not use burst performance.
Infra nodes are used to host Monitoring, Ingress, and Registry components to ensure they have enough resources to run at large scale.
Workload node is dedicated to run performance and scalability workload generators.
Larger disk size is used so that there is enough space to store the large amounts of data that is collected during the performance and scalability test run.
Cluster is scaled in iterations and performance and scalability tests are executed at the specified node counts.

8.2.2. IBM Power platform

Node	vCPU	RAM(GiB)	Disk type	Disk size(GiB)/IOS	Count
Control plane/etcd ^[1]	16	32	io1	120 / 10 IOPS per GiB	3
Infra ^[2]	16	64	gp2	120	2
Workload ^[3]	16	256	gp2	120 ^[4]	1
Compute	16	64	gp2	120	2 to 100 ^[5]

io1 disks with 120 / 10 IOPS per GiB are used for control plane/etcd nodes as etcd is I/O intensive and latency sensitive.
Infra nodes are used to host Monitoring, Ingress, and Registry components to ensure they have enough resources to run at large scale.
Workload node is dedicated to run performance and scalability workload generators.
Larger disk size is used so that there is enough space to store the large amounts of data that is collected during the performance and scalability test run.
Cluster is scaled in iterations.

8.2.3. IBM Z platform

Node	vCPU ^[4]	RAM(GiB)^[5]	Disk type	Disk size(GiB)/IOS	Count
Control plane/etcd ^[1,2]	8	32	ds8k	300 / LCU 1	3
Compute ^[1,3]	8	32	ds8k	150 / LCU 2	4 nodes (scaled to 100/250/500 pods per node)

Nodes are distributed between two logical control units (LCUs) to optimize disk I/O load of the control plane/etcd nodes as etcd is I/O intensive and latency sensitive. Etcd I/O demand should not interfere with other workloads.
Four compute nodes are used for the tests running several iterations with 100/250/500 pods at the same time. First, idling pods were used to evaluate if pods can be instanced. Next, a network and CPU demanding client/server workload were used to evaluate the stability of the system under stress. Client and server pods were pairwise deployed and each pair was spread over two compute nodes.
No separate workload node was used. The workload simulates a microservice workload between two compute nodes.
Physical number of processors used is six Integrated Facilities for Linux (IFLs).
Total physical memory used is 512 GiB.

8.3. How to plan your environment according to tested cluster maximums

Important

Oversubscribing the physical resources on a node affects resource guarantees the Kubernetes scheduler makes during pod placement. Learn what measures you can take to avoid memory swapping.

Some of the tested maximums are stretched only in a single dimension. They will vary when many objects are running on the cluster.

The numbers noted in this documentation are based on Red Hat’s test methodology, setup, configuration, and tunings. These numbers can vary based on your own individual setup and environments.

While planning your environment, determine how many pods are expected to fit per node:

required pods per cluster / pods per node = total number of nodes needed

The current maximum number of pods per node is 250. However, the number of pods that fit on a node is dependent on the application itself. Consider the application’s memory, CPU, and storage requirements, as described in How to plan your environment according to application requirements.

Example scenario

If you want to scope your cluster for 2200 pods per cluster, you would need at least five nodes, assuming that there are 500 maximum pods per node:

2200 / 500 = 4.4

If you increase the number of nodes to 20, then the pod distribution changes to 110 pods per node:

2200 / 20 = 110

Where:

required pods per cluster / total number of nodes = expected pods per node

8.4. How to plan your environment according to application requirements

Consider an example application environment:

Pod type	Pod quantity	Max memory	CPU cores	Persistent storage
apache	100	500 MB	0.5	1 GB
node.js	200	1 GB	1	1 GB
postgresql	100	1 GB	2	10 GB
JBoss EAP	100	1 GB	1	1 GB

Extrapolated requirements: 550 CPU cores, 450GB RAM, and 1.4TB storage.

Instance size for nodes can be modulated up or down, depending on your preference. Nodes are often resource overcommitted. In this deployment scenario, you can choose to run additional smaller nodes or fewer larger nodes to provide the same amount of resources. Factors such as operational agility and cost-per-instance should be considered.

Node type	Quantity	CPUs	RAM (GB)
Nodes (option 1)	100	4	16
Nodes (option 2)	50	8	32
Nodes (option 3)	25	16	64

Some applications lend themselves well to overcommitted environments, and some do not. Most Java applications and applications that use huge pages are examples of applications that would not allow for overcommitment. That memory can not be used for other applications. In the example above, the environment would be roughly 30 percent overcommitted, a common ratio.

The application pods can access a service either by using environment variables or DNS. If using environment variables, for each active service the variables are injected by the kubelet when a pod is run on a node. A cluster-aware DNS server watches the Kubernetes API for new services and creates a set of DNS records for each one. If DNS is enabled throughout your cluster, then all pods should automatically be able to resolve services by their DNS name. Service discovery using DNS can be used in case you must go beyond 5000 services. When using environment variables for service discovery, the argument list exceeds the allowed length after 5000 services in a namespace, then the pods and deployments will start failing. Disable the service links in the deployment’s service specification file to overcome this:

---
apiVersion: template.openshift.io/v1
kind: Template
metadata:
  name: deployment-config-template
  creationTimestamp:
  annotations:
    description: This template will create a deploymentConfig with 1 replica, 4 env vars and a service.
    tags: ''
objects:
- apiVersion: apps.openshift.io/v1
  kind: DeploymentConfig
  metadata:
    name: deploymentconfig${IDENTIFIER}
  spec:
    template:
      metadata:
        labels:
          name: replicationcontroller${IDENTIFIER}
      spec:
        enableServiceLinks: false
        containers:
        - name: pause${IDENTIFIER}
          image: "${IMAGE}"
          ports:
          - containerPort: 8080
            protocol: TCP
          env:
          - name: ENVVAR1_${IDENTIFIER}
            value: "${ENV_VALUE}"
          - name: ENVVAR2_${IDENTIFIER}
            value: "${ENV_VALUE}"
          - name: ENVVAR3_${IDENTIFIER}
            value: "${ENV_VALUE}"
          - name: ENVVAR4_${IDENTIFIER}
            value: "${ENV_VALUE}"
          resources: {}
          imagePullPolicy: IfNotPresent
          capabilities: {}
          securityContext:
            capabilities: {}
            privileged: false
        restartPolicy: Always
        serviceAccount: ''
    replicas: 1
    selector:
      name: replicationcontroller${IDENTIFIER}
    triggers:
    - type: ConfigChange
    strategy:
      type: Rolling
- apiVersion: v1
  kind: Service
  metadata:
    name: service${IDENTIFIER}
  spec:
    selector:
      name: replicationcontroller${IDENTIFIER}
    ports:
    - name: serviceport${IDENTIFIER}
      protocol: TCP
      port: 80
      targetPort: 8080
    clusterIP: ''
    type: ClusterIP
    sessionAffinity: None
  status:
    loadBalancer: {}
parameters:
- name: IDENTIFIER
  description: Number to append to the name of resources
  value: '1'
  required: true
- name: IMAGE
  description: Image to use for deploymentConfig
  value: gcr.io/google-containers/pause-amd64:3.0
  required: false
- name: ENV_VALUE
  description: Value to use for environment variables
  generate: expression
  from: "[A-Za-z0-9]{255}"
  required: false
labels:
  template: deployment-config-template

The number of application pods that can run in a namespace is dependent on the number of services and the length of the service name when the environment variables are used for service discovery. ARG_MAX on the system defines the maximum argument length for a new process and it is set to 2097152 KiB by default. The Kubelet injects environment variables in to each pod scheduled to run in the namespace including:

<SERVICE_NAME>_SERVICE_HOST=<IP>
<SERVICE_NAME>_SERVICE_PORT=<PORT>
<SERVICE_NAME>_PORT=tcp://<IP>:<PORT>
<SERVICE_NAME>_PORT_<PORT>_TCP=tcp://<IP>:<PORT>
<SERVICE_NAME>_PORT_<PORT>_TCP_PROTO=tcp
<SERVICE_NAME>_PORT_<PORT>_TCP_PORT=<PORT>
<SERVICE_NAME>_PORT_<PORT>_TCP_ADDR=<ADDR>

The pods in the namespace will start to fail if the argument length exceeds the allowed value and the number of characters in a service name impacts it. For example, in a namespace with 5000 services, the limit on the service name is 33 characters, which enables you to run 5000 pods in the namespace.

Chapter 9. Optimizing storage

Optimizing storage helps to minimize storage use across all resources. By optimizing storage, administrators help ensure that existing storage resources are working in an efficient manner.

9.1. Available persistent storage options

Understand your persistent storage options so that you can optimize your OpenShift Container Platform environment.

Table 9.1. Available storage options

Storage type	Description	Examples
Block	Presented to the operating system (OS) as a block device Suitable for applications that need full control of storage and operate at a low level on files bypassing the file system Also referred to as a Storage Area Network (SAN) Non-shareable, which means that only one client at a time can mount an endpoint of this type	AWS EBS and VMware vSphere support dynamic persistent volume (PV) provisioning natively in OpenShift Container Platform.
File	Presented to the OS as a file system export to be mounted Also referred to as Network Attached Storage (NAS) Concurrency, latency, file locking mechanisms, and other capabilities vary widely between protocols, implementations, vendors, and scales.	RHEL NFS, NetApp NFS ^[1], and Vendor NFS
Object	Accessible through a REST API endpoint Configurable for use in the OpenShift image registry Applications must build their drivers into the application and/or container.	AWS S3

NetApp NFS supports dynamic PV provisioning when using the Trident plugin.

Important

Currently, CNS is not supported in OpenShift Container Platform 4.10.

9.2. Recommended configurable storage technology

The following table summarizes the recommended and configurable storage technologies for the given OpenShift Container Platform cluster application.

Table 9.2. Recommended and configurable storage technology

Storage type	ROX¹	RWX²	Registry	Scaled registry	Metrics³	Logging	Apps
¹ `ReadOnlyMany` ² `ReadWriteMany` ³ Prometheus is the underlying technology used for metrics. ⁴ This does not apply to physical disk, VM physical disk, VMDK, loopback over NFS, AWS EBS, and Azure Disk. ⁵ For metrics, using file storage with the `ReadWriteMany` (RWX) access mode is unreliable. If you use file storage, do not configure the RWX access mode on any persistent volume claims (PVCs) that are configured for use with metrics. ⁶ For logging, using any shared storage would be an anti-pattern. One volume per elasticsearch is required. ⁷ Object storage is not consumed through OpenShift Container Platform’s PVs or PVCs. Apps must integrate with the object storage REST API.
Block	Yes⁴	No	Configurable	Not configurable	Recommended	Recommended	Recommended
File	Yes⁴	Yes	Configurable	Configurable	Configurable⁵	Configurable⁶	Recommended
Object	Yes	Yes	Recommended	Recommended	Not configurable	Not configurable	Not configurable⁷

Note

A scaled registry is an OpenShift image registry where two or more pod replicas are running.

9.2.1. Specific application storage recommendations

Important

Testing shows issues with using the NFS server on Red Hat Enterprise Linux (RHEL) as storage backend for core services. This includes the OpenShift Container Registry and Quay, Prometheus for monitoring storage, and Elasticsearch for logging storage. Therefore, using RHEL NFS to back PVs used by core services is not recommended.

Other NFS implementations on the marketplace might not have these issues. Contact the individual NFS implementation vendor for more information on any testing that was possibly completed against these OpenShift Container Platform core components.

9.2.1.1. Registry

In a non-scaled/high-availability (HA) OpenShift image registry cluster deployment:

The storage technology does not have to support RWX access mode.
The storage technology must ensure read-after-write consistency.
The preferred storage technology is object storage followed by block storage.
File storage is not recommended for OpenShift image registry cluster deployment with production workloads.

9.2.1.2. Scaled registry

In a scaled/HA OpenShift image registry cluster deployment:

The storage technology must support RWX access mode.
The storage technology must ensure read-after-write consistency.
The preferred storage technology is object storage.
Red Hat OpenShift Data Foundation (ODF), Amazon Simple Storage Service (Amazon S3), Google Cloud Storage (GCS), Microsoft Azure Blob Storage, and OpenStack Swift are supported.
Object storage should be S3 or Swift compliant.
For non-cloud platforms, such as vSphere and bare metal installations, the only configurable technology is file storage.
Block storage is not configurable.

9.2.1.3. Metrics

In an OpenShift Container Platform hosted metrics cluster deployment:

The preferred storage technology is block storage.
Object storage is not configurable.

Important

It is not recommended to use file storage for a hosted metrics cluster deployment with production workloads.

9.2.1.4. Logging

In an OpenShift Container Platform hosted logging cluster deployment:

The preferred storage technology is block storage.
Object storage is not configurable.

9.2.1.5. Applications

Application use cases vary from application to application, as described in the following examples:

Storage technologies that support dynamic PV provisioning have low mount time latencies, and are not tied to nodes to support a healthy cluster.
Application developers are responsible for knowing and understanding the storage requirements for their application, and how it works with the provided storage to ensure that issues do not occur when an application scales or interacts with the storage layer.

9.2.2. Other specific application storage recommendations

Important

It is not recommended to use RAID configurations on Write intensive workloads, such as etcd. If you are running etcd with a RAID configuration, you might be at risk of encountering performance issues with your workloads.

Red Hat OpenStack Platform (RHOSP) Cinder: RHOSP Cinder tends to be adept in ROX access mode use cases.
Databases: Databases (RDBMSs, NoSQL DBs, etc.) tend to perform best with dedicated block storage.
The etcd database must have enough storage and adequate performance capacity to enable a large cluster. Information about monitoring and benchmarking tools to establish ample storage and a high-performance environment is described in Recommended etcd practices.

9.3. Data storage management

The following table summarizes the main directories that OpenShift Container Platform components write data to.

Table 9.3. Main directories for storing OpenShift Container Platform data

Directory	Notes	Sizing	Expected growth
*/var/log*	Log files for all components.	10 to 30 GB.	Log files can grow quickly; size can be managed by growing disks or by using log rotate.
*/var/lib/etcd*	Used for etcd storage when storing the database.	Less than 20 GB. Database can grow up to 8 GB.	Will grow slowly with the environment. Only storing metadata. Additional 20-25 GB for every additional 8 GB of memory.
*/var/lib/containers*	This is the mount point for the CRI-O runtime. Storage used for active container runtimes, including pods, and storage of local images. Not used for registry storage.	50 GB for a node with 16 GB memory. Note that this sizing should not be used to determine minimum cluster requirements. Additional 20-25 GB for every additional 8 GB of memory.	Growth is limited by capacity for running containers.
*/var/lib/kubelet*	Ephemeral volume storage for pods. This includes anything external that is mounted into a container at runtime. Includes environment variables, kube secrets, and data volumes not backed by persistent volumes.	Varies	Minimal if pods requiring storage are using persistent volumes. If using ephemeral storage, this can grow quickly.

9.4. Optimizing storage performance for Microsoft Azure

OpenShift Container Platform and Kubernetes are sensitive to disk performance, and faster storage is recommended, particularly for etcd on the control plane nodes.

For production Azure clusters and clusters with intensive workloads, the virtual machine operating system disk for control plane machines should be able to sustain a tested and recommended minimum throughput of 5000 IOPS / 200MBps. This throughput can be provided by having a minimum of 1 TiB Premium SSD (P30). In Azure and Azure Stack Hub, disk performance is directly dependent on SSD disk sizes. To achieve the throughput supported by a Standard_D8s_v3 virtual machine, or other similar machine types, and the target of 5000 IOPS, at least a P30 disk is required.

Host caching must be set to ReadOnly for low latency and high IOPS and throughput when reading data. Reading data from the cache, which is present either in the VM memory or in the local SSD disk, is much faster than reading from the disk, which is in the blob storage.

Chapter 10. Optimizing routing

The OpenShift Container Platform HAProxy router can be scaled or configured to optimize performance.

10.1. Baseline Ingress Controller (router) performance

The OpenShift Container Platform Ingress Controller, or router, is the ingress point for ingress traffic for applications and services that are configured using routes and ingresses.

When evaluating a single HAProxy router performance in terms of HTTP requests handled per second, the performance varies depending on many factors. In particular:

HTTP keep-alive/close mode
Route type
TLS session resumption client support
Number of concurrent connections per target route
Number of target routes
Back end server page size
Underlying infrastructure (network/SDN solution, CPU, and so on)

While performance in your specific environment will vary, Red Hat lab tests on a public cloud instance of size 4 vCPU/16GB RAM. A single HAProxy router handling 100 routes terminated by backends serving 1kB static pages is able to handle the following number of transactions per second.

In HTTP keep-alive mode scenarios:

Encryption	LoadBalancerService	HostNetwork
none	21515	29622
edge	16743	22913
passthrough	36786	53295
re-encrypt	21583	25198

In HTTP close (no keep-alive) scenarios:

Encryption	LoadBalancerService	HostNetwork
none	5719	8273
edge	2729	4069
passthrough	4121	5344
re-encrypt	2320	2941

The default Ingress Controller configuration was used with the spec.tuningOptions.threadCount field set to 4. Two different endpoint publishing strategies were tested: Load Balancer Service and Host Network. TLS session resumption was used for encrypted routes. With HTTP keep-alive, a single HAProxy router is capable of saturating a 1 Gbit NIC at page sizes as small as 8 kB.

When running on bare metal with modern processors, you can expect roughly twice the performance of the public cloud instance above. This overhead is introduced by the virtualization layer in place on public clouds and holds mostly true for private cloud-based virtualization as well. The following table is a guide to how many applications to use behind the router:

Number of applications	Application type
5-10	static file/web server or caching proxy
100-1000	applications generating dynamic content

In general, HAProxy can support routes for up to 1000 applications, depending on the technology in use. Ingress Controller performance might be limited by the capabilities and performance of the applications behind it, such as language or static versus dynamic content.

Ingress, or router, sharding should be used to serve more routes towards applications and help horizontally scale the routing tier.

For more information on Ingress sharding, see Configuring Ingress Controller sharding by using route labels and Configuring Ingress Controller sharding by using namespace labels.

For more information on tuningOptions, see Ingress Controller configuration parameters.

You can modify the Ingress Controller deployment using the information provided in Setting Ingress Controller thread count for threads and Ingress Controller configuration parameters for timeouts, and other tuning configurations in the Ingress Controller specification.

Chapter 11. Optimizing networking

The OpenShift SDN uses OpenvSwitch, virtual extensible LAN (VXLAN) tunnels, OpenFlow rules, and iptables. This network can be tuned by using jumbo frames, network interface controllers (NIC) offloads, multi-queue, and ethtool settings.

OVN-Kubernetes uses Geneve (Generic Network Virtualization Encapsulation) instead of VXLAN as the tunnel protocol.

VXLAN provides benefits over VLANs, such as an increase in networks from 4096 to over 16 million, and layer 2 connectivity across physical networks. This allows for all pods behind a service to communicate with each other, even if they are running on different systems.

VXLAN encapsulates all tunneled traffic in user datagram protocol (UDP) packets. However, this leads to increased CPU utilization. Both these outer- and inner-packets are subject to normal checksumming rules to guarantee data is not corrupted during transit. Depending on CPU performance, this additional processing overhead can cause a reduction in throughput and increased latency when compared to traditional, non-overlay networks.

Cloud, VM, and bare metal CPU performance can be capable of handling much more than one Gbps network throughput. When using higher bandwidth links such as 10 or 40 Gbps, reduced performance can occur. This is a known issue in VXLAN-based environments and is not specific to containers or OpenShift Container Platform. Any network that relies on VXLAN tunnels will perform similarly because of the VXLAN implementation.

If you are looking to push beyond one Gbps, you can:

Evaluate network plugins that implement different routing techniques, such as border gateway protocol (BGP).
Use VXLAN-offload capable network adapters. VXLAN-offload moves the packet checksum calculation and associated CPU overhead off of the system CPU and onto dedicated hardware on the network adapter. This frees up CPU cycles for use by pods and applications, and allows users to utilize the full bandwidth of their network infrastructure.

VXLAN-offload does not reduce latency. However, CPU utilization is reduced even in latency tests.

11.1. Optimizing the MTU for your network

There are two important maximum transmission units (MTUs): the network interface controller (NIC) MTU and the cluster network MTU.

The NIC MTU is only configured at the time of OpenShift Container Platform installation. The MTU must be less than or equal to the maximum supported value of the NIC of your network. If you are optimizing for throughput, choose the largest possible value. If you are optimizing for lowest latency, choose a lower value.

The OpenShift SDN network plugin overlay MTU must be less than the NIC MTU by 50 bytes at a minimum. This accounts for the SDN overlay header. So, on a normal ethernet network, this should be set to 1450. On a jumbo frame ethernet network, this should be set to 8950. These values should be set automatically by the Cluster Network Operator based on the NIC’s configured MTU. Therefore, cluster administrators do not typically update these values. Amazon Web Services (AWS) and bare-metal environments support jumbo frame ethernet networks. This setting will help throughput, especially with transmission control protocol (TCP).

For OVN and Geneve, the MTU must be less than the NIC MTU by 100 bytes at a minimum.

Note

This 50 byte overlay header is relevant to the OpenShift SDN network plugin. Other SDN solutions might require the value to be more or less.

11.2. Recommended practices for installing large scale clusters

When installing large clusters or scaling the cluster to larger node counts, set the cluster network cidr accordingly in your install-config.yaml file before you install the cluster:

networking:
  clusterNetwork:
  - cidr: 10.128.0.0/14
    hostPrefix: 23
  machineNetwork:
  - cidr: 10.0.0.0/16
  networkType: OpenShiftSDN
  serviceNetwork:
  - 172.30.0.0/16

The default cluster network cidr 10.128.0.0/14 cannot be used if the cluster size is more than 500 nodes. It must be set to 10.128.0.0/12 or 10.128.0.0/10 to get to larger node counts beyond 500 nodes.

11.3. Impact of IPsec

Because encrypting and decrypting node hosts uses CPU power, performance is affected both in throughput and CPU usage on the nodes when encryption is enabled, regardless of the IP security system being used.

IPSec encrypts traffic at the IP payload level, before it hits the NIC, protecting fields that would otherwise be used for NIC offloading. This means that some NIC acceleration features might not be usable when IPSec is enabled and will lead to decreased throughput and increased CPU usage.

Additional resources

Chapter 12. Managing bare metal hosts

When you install OpenShift Container Platform on a bare metal cluster, you can provision and manage bare metal nodes using machine and machineset custom resources (CRs) for bare metal hosts that exist in the cluster.

12.1. About bare metal hosts and nodes

To provision a Red Hat Enterprise Linux CoreOS (RHCOS) bare metal host as a node in your cluster, first create a MachineSet custom resource (CR) object that corresponds to the bare metal host hardware. Bare metal host machine sets describe infrastructure components specific to your configuration. You apply specific Kubernetes labels to these machine sets and then update the infrastructure components to run on only those machines.

Machine CR’s are created automatically when you scale up the relevant MachineSet containing a metal3.io/autoscale-to-hosts annotation. OpenShift Container Platform uses Machine CR’s to provision the bare metal node that corresponds to the host as specified in the MachineSet CR.

12.2. Maintaining bare metal hosts

You can maintain the details of the bare metal hosts in your cluster from the OpenShift Container Platform web console. Navigate to Compute → Bare Metal Hosts, and select a task from the Actions drop down menu. Here you can manage items such as BMC details, boot MAC address for the host, enable power management, and so on. You can also review the details of the network interfaces and drives for the host.

You can move a bare metal host into maintenance mode. When you move a host into maintenance mode, the scheduler moves all managed workloads off the corresponding bare metal node. No new workloads are scheduled while in maintenance mode.

You can deprovision a bare metal host in the web console. Deprovisioning a host does the following actions:

Annotates the bare metal host CR with cluster.k8s.io/delete-machine: true
Scales down the related machine set

Note

Powering off the host without first moving the daemon set and unmanaged static pods to another node can cause service disruption and loss of data.

Additional resources

Adding compute machines to bare metal

12.2.1. Adding a bare metal host to the cluster using the web console

You can add bare metal hosts to the cluster in the web console.

Prerequisites

Install an RHCOS cluster on bare metal.
Log in as a user with cluster-admin privileges.

Procedure

In the web console, navigate to Compute → Bare Metal Hosts.
Select Add Host → New with Dialog.
Specify a unique name for the new bare metal host.
Set the Boot MAC address.
Set the Baseboard Management Console (BMC) Address.
Enter the user credentials for the host’s baseboard management controller (BMC).
Select to power on the host after creation, and select Create.
Scale up the number of replicas to match the number of available bare metal hosts. Navigate to Compute → MachineSets, and increase the number of machine replicas in the cluster by selecting Edit Machine count from the Actions drop-down menu.

Note

You can also manage the number of bare metal nodes using the oc scale command and the appropriate bare metal machine set.

12.2.2. Adding a bare metal host to the cluster using YAML in the web console

You can add bare metal hosts to the cluster in the web console using a YAML file that describes the bare metal host.

Prerequisites

Install a RHCOS compute machine on bare metal infrastructure for use in the cluster.
Log in as a user with cluster-admin privileges.
Create a Secret CR for the bare metal host.

Procedure

In the web console, navigate to Compute → Bare Metal Hosts.
Select Add Host → New from YAML.
Copy and paste the below YAML, modifying the relevant fields with the details of your host:
```
apiVersion: metal3.io/v1alpha1
kind: BareMetalHost
metadata:
  name: <bare_metal_host_name>
spec:
  online: true
  bmc:
    address: <bmc_address>
    credentialsName: <secret_credentials_name>  1
    disableCertificateVerification: True 2
  bootMACAddress: <host_boot_mac_address>
```
1
credentialsName must reference a valid Secret CR. The baremetal-operator cannot manage the bare metal host without a valid Secret referenced in the credentialsName. For more information about secrets and how to create them, see Understanding secrets.
2
Setting disableCertificateVerification to true disables TLS host validation between the cluster and the baseboard management controller (BMC).
Select Create to save the YAML and create the new bare metal host.
Scale up the number of replicas to match the number of available bare metal hosts. Navigate to Compute → MachineSets, and increase the number of machines in the cluster by selecting Edit Machine count from the Actions drop-down menu.
Note
You can also manage the number of bare metal nodes using the oc scale command and the appropriate bare metal machine set.

12.2.3. Automatically scaling machines to the number of available bare metal hosts

To automatically create the number of Machine objects that matches the number of available BareMetalHost objects, add a metal3.io/autoscale-to-hosts annotation to the MachineSet object.

Prerequisites

Install RHCOS bare metal compute machines for use in the cluster, and create corresponding BareMetalHost objects.
Install the OpenShift Container Platform CLI (oc).
Log in as a user with cluster-admin privileges.

Procedure

Annotate the machine set that you want to configure for automatic scaling by adding the metal3.io/autoscale-to-hosts annotation. Replace <machineset> with the name of the machine set.
```
$ oc annotate machineset <machineset> -n openshift-machine-api 'metal3.io/autoscale-to-hosts=<any_value>'
```
Wait for the new scaled machines to start.

Note

When you use a BareMetalHost object to create a machine in the cluster and labels or selectors are subsequently changed on the BareMetalHost, the BareMetalHost object continues be counted against the MachineSet that the Machine object was created from.

12.2.4. Removing bare metal hosts from the provisioner node

In certain circumstances, you might want to temporarily remove bare metal hosts from the provisioner node. For example, during provisioning when a bare metal host reboot is triggered by using the OpenShift Container Platform administration console or as a result of a Machine Config Pool update, OpenShift Container Platform logs into the integrated Dell Remote Access Controller (iDrac) and issues a delete of the job queue.

To prevent the management of the number of Machine objects that matches the number of available BareMetalHost objects, add a baremetalhost.metal3.io/detached annotation to the MachineSet object.

Note

This annotation has an effect for only BareMetalHost objects that are in either Provisioned, ExternallyProvisioned or Ready/Available state.

Prerequisites

Install RHCOS bare metal compute machines for use in the cluster and create corresponding BareMetalHost objects.
Install the OpenShift Container Platform CLI (oc).
Log in as a user with cluster-admin privileges.

Procedure

Annotate the compute machine set that you want to remove from the provisioner node by adding the baremetalhost.metal3.io/detached annotation.
```
$ oc annotate machineset <machineset> -n openshift-machine-api 'baremetalhost.metal3.io/detached'
```
Wait for the new machines to start.
Note
When you use a BareMetalHost object to create a machine in the cluster and labels or selectors are subsequently changed on the BareMetalHost, the BareMetalHost object continues be counted against the MachineSet that the Machine object was created from.
In the provisioning use case, remove the annotation after the reboot is complete by using the following command:
```
$ oc annotate machineset <machineset> -n openshift-machine-api 'baremetalhost.metal3.io/detached-'
```

Additional resources

Chapter 13. What huge pages do and how they are consumed by applications

13.1. What huge pages do

Memory is managed in blocks known as pages. On most systems, a page is 4Ki. 1Mi of memory is equal to 256 pages; 1Gi of memory is 256,000 pages, and so on. CPUs have a built-in memory management unit that manages a list of these pages in hardware. The Translation Lookaside Buffer (TLB) is a small hardware cache of virtual-to-physical page mappings. If the virtual address passed in a hardware instruction can be found in the TLB, the mapping can be determined quickly. If not, a TLB miss occurs, and the system falls back to slower, software-based address translation, resulting in performance issues. Since the size of the TLB is fixed, the only way to reduce the chance of a TLB miss is to increase the page size.

A huge page is a memory page that is larger than 4Ki. On x86_64 architectures, there are two common huge page sizes: 2Mi and 1Gi. Sizes vary on other architectures. To use huge pages, code must be written so that applications are aware of them. Transparent Huge Pages (THP) attempt to automate the management of huge pages without application knowledge, but they have limitations. In particular, they are limited to 2Mi page sizes. THP can lead to performance degradation on nodes with high memory utilization or fragmentation due to defragmenting efforts of THP, which can lock memory pages. For this reason, some applications may be designed to (or recommend) usage of pre-allocated huge pages instead of THP.

In OpenShift Container Platform, applications in a pod can allocate and consume pre-allocated huge pages.

13.2. How huge pages are consumed by apps

Nodes must pre-allocate huge pages in order for the node to report its huge page capacity. A node can only pre-allocate huge pages for a single size.

Huge pages can be consumed through container-level resource requirements using the resource name hugepages-<size>, where size is the most compact binary notation using integer values supported on a particular node. For example, if a node supports 2048KiB page sizes, it exposes a schedulable resource hugepages-2Mi. Unlike CPU or memory, huge pages do not support over-commitment.

apiVersion: v1
kind: Pod
metadata:
  generateName: hugepages-volume-
spec:
  containers:
  - securityContext:
      privileged: true
    image: rhel7:latest
    command:
    - sleep
    - inf
    name: example
    volumeMounts:
    - mountPath: /dev/hugepages
      name: hugepage
    resources:
      limits:
        hugepages-2Mi: 100Mi 1
        memory: "1Gi"
        cpu: "1"
  volumes:
  - name: hugepage
    emptyDir:
      medium: HugePages

1: Specify the amount of memory for hugepages as the exact amount to be allocated. Do not specify this value as the amount of memory for hugepages multiplied by the size of the page. For example, given a huge page size of 2MB, if you want to use 100MB of huge-page-backed RAM for your application, then you would allocate 50 huge pages. OpenShift Container Platform handles the math for you. As in the above example, you can specify 100MB directly.

Allocating huge pages of a specific size

Some platforms support multiple huge page sizes. To allocate huge pages of a specific size, precede the huge pages boot command parameters with a huge page size selection parameter hugepagesz=<size>. The <size> value must be specified in bytes with an optional scale suffix [kKmMgG]. The default huge page size can be defined with the default_hugepagesz=<size> boot parameter.

Huge page requirements

Huge page requests must equal the limits. This is the default if limits are specified, but requests are not.
Huge pages are isolated at a pod scope. Container isolation is planned in a future iteration.
EmptyDir volumes backed by huge pages must not consume more huge page memory than the pod request.
Applications that consume huge pages via shmget() with SHM_HUGETLB must run with a supplemental group that matches proc/sys/vm/hugetlb_shm_group.

13.3. Consuming huge pages resources using the Downward API

You can use the Downward API to inject information about the huge pages resources that are consumed by a container.

You can inject the resource allocation as environment variables, a volume plugin, or both. Applications that you develop and run in the container can determine the resources that are available by reading the environment variables or files in the specified volumes.

Procedure

Create a hugepages-volume-pod.yaml file that is similar to the following example:

apiVersion: v1
kind: Pod
metadata:
  generateName: hugepages-volume-
  labels:
    app: hugepages-example
spec:
  containers:
  - securityContext:
      capabilities:
        add: [ "IPC_LOCK" ]
    image: rhel7:latest
    command:
    - sleep
    - inf
    name: example
    volumeMounts:
    - mountPath: /dev/hugepages
      name: hugepage
    - mountPath: /etc/podinfo
      name: podinfo
    resources:
      limits:
        hugepages-1Gi: 2Gi
        memory: "1Gi"
        cpu: "1"
      requests:
        hugepages-1Gi: 2Gi
    env:
    - name: REQUESTS_HUGEPAGES_1GI <.>
      valueFrom:
        resourceFieldRef:
          containerName: example
          resource: requests.hugepages-1Gi
  volumes:
  - name: hugepage
    emptyDir:
      medium: HugePages
  - name: podinfo
    downwardAPI:
      items:
        - path: "hugepages_1G_request" <.>
          resourceFieldRef:
            containerName: example
            resource: requests.hugepages-1Gi
            divisor: 1Gi

<.> Specifies to read the resource use from requests.hugepages-1Gi and expose the value as the REQUESTS_HUGEPAGES_1GI environment variable. <.> Specifies to read the resource use from requests.hugepages-1Gi and expose the value as the file /etc/podinfo/hugepages_1G_request.

Create the pod from the hugepages-volume-pod.yaml file:
```
$ oc create -f hugepages-volume-pod.yaml
```

Verification

Check the value of the REQUESTS_HUGEPAGES_1GI environment variable:

$ oc exec -it $(oc get pods -l app=hugepages-example -o jsonpath='{.items[0].metadata.name}') \
     -- env | grep REQUESTS_HUGEPAGES_1GI

Example output

REQUESTS_HUGEPAGES_1GI=2147483648

Check the value of the /etc/podinfo/hugepages_1G_request file:

$ oc exec -it $(oc get pods -l app=hugepages-example -o jsonpath='{.items[0].metadata.name}') \
     -- cat /etc/podinfo/hugepages_1G_request

Example output

Additional resources

Allowing containers to consume Downward API objects

13.4. Configuring huge pages

Nodes must pre-allocate huge pages used in an OpenShift Container Platform cluster. There are two ways of reserving huge pages: at boot time and at run time. Reserving at boot time increases the possibility of success because the memory has not yet been significantly fragmented. The Node Tuning Operator currently supports boot time allocation of huge pages on specific nodes.

13.4.1. At boot time

Procedure

To minimize node reboots, the order of the steps below needs to be followed:

Label all nodes that need the same huge pages setting by a label.

$ oc label node <node_using_hugepages> node-role.kubernetes.io/worker-hp=

Create a file with the following content and name it hugepages-tuned-boottime.yaml:

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: hugepages 1
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile: 2
  - data: |
      [main]
      summary=Boot time configuration for hugepages
      include=openshift-node
      [bootloader]
      cmdline_openshift_node_hugepages=hugepagesz=2M hugepages=50 3
    name: openshift-node-hugepages

  recommend:
  - machineConfigLabels: 4
      machineconfiguration.openshift.io/role: "worker-hp"
    priority: 30
    profile: openshift-node-hugepages

1: Set the name of the Tuned resource to hugepages.
2: Set the profile section to allocate huge pages.
3: Note the order of parameters is important as some platforms support huge pages of various sizes.
4: Enable machine config pool based matching.

Create the Tuned hugepages object

$ oc create -f hugepages-tuned-boottime.yaml

Create a file with the following content and name it hugepages-mcp.yaml:

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
  name: worker-hp
  labels:
    worker-hp: ""
spec:
  machineConfigSelector:
    matchExpressions:
      - {key: machineconfiguration.openshift.io/role, operator: In, values: [worker,worker-hp]}
  nodeSelector:
    matchLabels:
      node-role.kubernetes.io/worker-hp: ""

Create the machine config pool:
```
$ oc create -f hugepages-mcp.yaml
```

Given enough non-fragmented memory, all the nodes in the worker-hp machine config pool should now have 50 2Mi huge pages allocated.

$ oc get node <node_using_hugepages> -o jsonpath="{.status.allocatable.hugepages-2Mi}"
100Mi

Note

The TuneD bootloader plugin only supports Red Hat Enterprise Linux CoreOS (RHCOS) worker nodes.

13.5. Disabling Transparent Huge Pages

Procedure

Create a file with the following content and name it thp-disable-tuned.yaml:

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: thp-workers-profile
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - data: |
      [main]
      summary=Custom tuned profile for OpenShift to turn off THP on worker nodes
      include=openshift-node

      [vm]
      transparent_hugepages=never
    name: openshift-thp-never-worker

  recommend:
  - match:
    - label: node-role.kubernetes.io/worker
    priority: 25
    profile: openshift-thp-never-worker

Create the Tuned object:
```
$ oc create -f thp-disable-tuned.yaml
```

Check the list of active profiles:

$ oc get profile -n openshift-cluster-node-tuning-operator

Verification

Log in to one of the nodes and do a regular THP check to verify if the nodes applied the profile successfully:
```
$ cat /sys/kernel/mm/transparent_hugepage/enabled
```
Example output
```
always madvise [never]
```

Chapter 14. Performance Addon Operator for low latency nodes

14.1. Understanding low latency

The emergence of Edge computing in the area of Telco / 5G plays a key role in reducing latency and congestion problems and improving application performance.

Simply put, latency determines how fast data (packets) moves from the sender to receiver and returns to the sender after processing by the receiver. Obviously, maintaining a network architecture with the lowest possible delay of latency speeds is key for meeting the network performance requirements of 5G. Compared to 4G technology, with an average latency of 50ms, 5G is targeted to reach latency numbers of 1ms or less. This reduction in latency boosts wireless throughput by a factor of 10.

Many of the deployed applications in the Telco space require low latency that can only tolerate zero packet loss. Tuning for zero packet loss helps mitigate the inherent issues that degrade network performance. For more information, see Tuning for Zero Packet Loss in Red Hat OpenStack Platform (RHOSP).

The Edge computing initiative also comes in to play for reducing latency rates. Think of it as literally being on the edge of the cloud and closer to the user. This greatly reduces the distance between the user and distant data centers, resulting in reduced application response times and performance latency.

Administrators must be able to manage their many Edge sites and local services in a centralized way so that all of the deployments can run at the lowest possible management cost. They also need an easy way to deploy and configure certain nodes of their cluster for real-time low latency and high-performance purposes. Low latency nodes are useful for applications such as Cloud-native Network Functions (CNF) and Data Plane Development Kit (DPDK).

OpenShift Container Platform currently provides mechanisms to tune software on an OpenShift Container Platform cluster for real-time running and low latency (around <20 microseconds reaction time). This includes tuning the kernel and OpenShift Container Platform set values, installing a kernel, and reconfiguring the machine. But this method requires setting up four different Operators and performing many configurations that, when done manually, is complex and could be prone to mistakes.

OpenShift Container Platform provides a Performance Addon Operator to implement automatic tuning to achieve low latency performance for OpenShift applications. The cluster administrator uses this performance profile configuration that makes it easier to make these changes in a more reliable way. The administrator can specify whether to update the kernel to kernel-rt, reserve CPUs for cluster and operating system housekeeping duties, including pod infra containers, and isolate CPUs for application containers to run the workloads.

14.1.1. About hyperthreading for low latency and real-time applications

Hyperthreading is an Intel processor technology that allows a physical CPU processor core to function as two logical cores, executing two independent threads simultaneously. Hyperthreading allows for better system throughput for certain workload types where parallel processing is beneficial. The default OpenShift Container Platform configuration expects hyperthreading to be enabled by default.

For telecommunications applications, it is important to design your application infrastructure to minimize latency as much as possible. Hyperthreading can slow performance times and negatively affect throughput for compute intensive workloads that require low latency. Disabling hyperthreading ensures predictable performance and can decrease processing times for these workloads.

Note

Hyperthreading implementation and configuration differs depending on the hardware you are running OpenShift Container Platform on. Consult the relevant host hardware tuning information for more details of the hyperthreading implementation specific to that hardware. Disabling hyperthreading can increase the cost per core of the cluster.

Additional resources

Configuring hyperthreading for a cluster

14.2. Installing the Performance Addon Operator

Performance Addon Operator provides the ability to enable advanced node performance tunings on a set of nodes. As a cluster administrator, you can install Performance Addon Operator using the OpenShift Container Platform CLI or the web console.

14.2.1. Installing the Operator using the CLI

As a cluster administrator, you can install the Operator using the CLI.

Prerequisites

A cluster installed on bare-metal hardware.
Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.

Procedure

Create a namespace for the Performance Addon Operator by completing the following actions:
1. Create the following Namespace Custom Resource (CR) that defines the openshift-performance-addon-operator namespace, and then save the YAML in the pao-namespace.yaml file:
```
apiVersion: v1
kind: Namespace
metadata:
  name: openshift-performance-addon-operator
  annotations:
    workload.openshift.io/allowed: management
```
2. Create the namespace by running the following command:
```
$ oc create -f pao-namespace.yaml
```
Install the Performance Addon Operator in the namespace you created in the previous step by creating the following objects:
1. Create the following OperatorGroup CR and save the YAML in the pao-operatorgroup.yaml file:
```
apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: openshift-performance-addon-operator
  namespace: openshift-performance-addon-operator
```
2. Create the OperatorGroup CR by running the following command:
```
$ oc create -f pao-operatorgroup.yaml
```
3. Run the following command to get the channel value required for the next step.
```
$ oc get packagemanifest performance-addon-operator -n openshift-marketplace -o jsonpath='{.status.defaultChannel}'
```
  Example output
```
4.10
```
4. Create the following Subscription CR and save the YAML in the pao-sub.yaml file:
  Example Subscription
```
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: openshift-performance-addon-operator-subscription
  namespace: openshift-performance-addon-operator
spec:
  channel: "<channel>" 1
  name: performance-addon-operator
  source: redhat-operators 2
  sourceNamespace: openshift-marketplace
```
  1
  Specify the value from you obtained in the previous step for the .status.defaultChannel parameter.
  2
  You must specify the redhat-operators value.
5. Create the Subscription object by running the following command:
```
$ oc create -f pao-sub.yaml
```
6. Change to the openshift-performance-addon-operator project:
```
$ oc project openshift-performance-addon-operator
```

14.2.2. Installing the Performance Addon Operator using the web console

As a cluster administrator, you can install the Performance Addon Operator using the web console.

Note

You must create the Namespace CR and OperatorGroup CR as mentioned in the previous section.

Procedure

Install the Performance Addon Operator using the OpenShift Container Platform web console:
1. In the OpenShift Container Platform web console, click Operators → OperatorHub.
2. Choose Performance Addon Operator from the list of available Operators, and then click Install.
3. On the Install Operator page, select All namespaces on the cluster. Then, click Install.
Optional: Verify that the performance-addon-operator installed successfully:
1. Switch to the Operators → Installed Operators page.
2. Ensure that Performance Addon Operator is listed in the openshift-operators project with a Status of Succeeded.
  Note
  During installation an Operator might display a Failed status. If the installation later succeeds with a Succeeded message, you can ignore the Failed message.
  If the Operator does not appear as installed, you can troubleshoot further:
  - Go to the Operators → Installed Operators page and inspect the Operator Subscriptions and Install Plans tabs for any failure or errors under Status.
  - Go to the Workloads → Pods page and check the logs for pods in the openshift-operators project.

14.3. Upgrading Performance Addon Operator

You can manually upgrade to the next minor version of Performance Addon Operator and monitor the status of an update by using the web console.

14.3.1. About upgrading Performance Addon Operator

You can upgrade to the next minor version of Performance Addon Operator by using the OpenShift Container Platform web console to change the channel of your Operator subscription.
You can enable automatic z-stream updates during Performance Addon Operator installation.
Updates are delivered via the Marketplace Operator, which is deployed during OpenShift Container Platform installation.The Marketplace Operator makes external Operators available to your cluster.
The amount of time an update takes to complete depends on your network connection. Most automatic updates complete within fifteen minutes.

14.3.1.1. How Performance Addon Operator upgrades affect your cluster

Neither the low latency tuning nor huge pages are affected.
Updating the Operator should not cause any unexpected reboots.

14.3.1.2. Upgrading Performance Addon Operator to the next minor version

You can manually upgrade Performance Addon Operator to the next minor version by using the OpenShift Container Platform web console to change the channel of your Operator subscription.

Prerequisites

Access to the cluster as a user with the cluster-admin role.

Procedure

Access the web console and navigate to Operators → Installed Operators.
Click Performance Addon Operator to open the Operator details page.
Click the Subscription tab to open the Subscription details page.
In the Update channel pane, click the pencil icon on the right side of the version number to open the Change Subscription update channel window.
Select the next minor version. For example, if you want to upgrade to Performance Addon Operator 4.10, select 4.10.
Click Save.
Check the status of the upgrade by navigating to Operators → Installed Operators. You can also check the status by running the following oc command:
```
$ oc get csv -n openshift-performance-addon-operator
```

14.3.1.3. Upgrading Performance Addon Operator when previously installed to a specific namespace

If you previously installed the Performance Addon Operator to a specific namespace on the cluster, for example openshift-performance-addon-operator, modify the OperatorGroup object to remove the targetNamespaces entry before upgrading.

Prerequisites

Install the OpenShift Container Platform CLI (oc).
Log in to the OpenShift cluster as a user with cluster-admin privileges.

Procedure

Edit the Performance Addon Operator OperatorGroup CR and remove the spec element that contains the targetNamespaces entry by running the following command:

$ oc patch operatorgroup -n openshift-performance-addon-operator openshift-performance-addon-operator --type json -p '[{ "op": "remove", "path": "/spec" }]'

Wait until the Operator Lifecycle Manager (OLM) processes the change.
Verify that the OperatorGroup CR change has been successfully applied. Check that the OperatorGroup CR spec element has been removed:
```
$ oc describe -n openshift-performance-addon-operator og openshift-performance-addon-operator
```
Proceed with the Performance Addon Operator upgrade.

14.3.2. Monitoring upgrade status

The best way to monitor Performance Addon Operator upgrade status is to watch the ClusterServiceVersion (CSV) PHASE. You can also monitor the CSV conditions in the web console or by running the oc get csv command.

Note

The PHASE and conditions values are approximations that are based on available information.

Prerequisites

Access to the cluster as a user with the cluster-admin role.
Install the OpenShift CLI (oc).

Procedure

Run the following command:
```
$ oc get csv
```

Review the output, checking the PHASE field. For example:

VERSION    REPLACES                                         PHASE
4.10.0      performance-addon-operator.v4.10.0                Installing
4.8.0                                                       Replacing

Run get csv again to verify the output:

# oc get csv

Example output

NAME                                DISPLAY                      VERSION   REPLACES                            PHASE
performance-addon-operator.v4.10.0   Performance Addon Operator   4.10.0     performance-addon-operator.v4.8.0   Succeeded

14.4. Provisioning real-time and low latency workloads

Many industries and organizations need extremely high performance computing and might require low and predictable latency, especially in the financial and telecommunications industries. For these industries, with their unique requirements, OpenShift Container Platform provides a Performance Addon Operator to implement automatic tuning to achieve low latency performance and consistent response time for OpenShift Container Platform applications.

The cluster administrator can use this performance profile configuration to make these changes in a more reliable way. The administrator can specify whether to update the kernel to kernel-rt (real-time), reserve CPUs for cluster and operating system housekeeping duties, including pod infra containers, and isolate CPUs for application containers to run the workloads.

Warning

The usage of execution probes in conjunction with applications that require guaranteed CPUs can cause latency spikes. It is recommended to use other probes, such as a properly configured set of network probes, as an alternative.

14.4.1. Known limitations for real-time

Note

In most deployments, kernel-rt is supported only on worker nodes when you use a standard cluster with three control plane nodes and three worker nodes. There are exceptions for compact and single nodes on OpenShift Container Platform deployments. For installations on a single node, kernel-rt is supported on the single control plane node.

To fully utilize the real-time mode, the containers must run with elevated privileges. See Set capabilities for a Container for information on granting privileges.

OpenShift Container Platform restricts the allowed capabilities, so you might need to create a SecurityContext as well.

Note

This procedure is fully supported with bare metal installations using Red Hat Enterprise Linux CoreOS (RHCOS) systems.

Establishing the right performance expectations refers to the fact that the real-time kernel is not a panacea. Its objective is consistent, low-latency determinism offering predictable response times. There is some additional kernel overhead associated with the real-time kernel. This is due primarily to handling hardware interruptions in separately scheduled threads. The increased overhead in some workloads results in some degradation in overall throughput. The exact amount of degradation is very workload dependent, ranging from 0% to 30%. However, it is the cost of determinism.

14.4.2. Provisioning a worker with real-time capabilities

Install Performance Addon Operator to the cluster.
Optional: Add a node to the OpenShift Container Platform cluster. See Setting BIOS parameters.
Add the label worker-rt to the worker nodes that require the real-time capability by using the oc command.

Create a new machine config pool for real-time nodes:

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
  name: worker-rt
  labels:
    machineconfiguration.openshift.io/role: worker-rt
spec:
  machineConfigSelector:
    matchExpressions:
      - {
           key: machineconfiguration.openshift.io/role,
           operator: In,
           values: [worker, worker-rt],
        }
  paused: false
  nodeSelector:
    matchLabels:
      node-role.kubernetes.io/worker-rt: ""

Note that a machine config pool worker-rt is created for group of nodes that have the label worker-rt.

Add the node to the proper machine config pool by using node role labels.
Note
You must decide which nodes are configured with real-time workloads. You could configure all of the nodes in the cluster, or a subset of the nodes. The Performance Addon Operator that expects all of the nodes are part of a dedicated machine config pool. If you use all of the nodes, you must point the Performance Addon Operator to the worker node role label. If you use a subset, you must group the nodes into a new machine config pool.
Create the PerformanceProfile with the proper set of housekeeping cores and realTimeKernel: enabled: true.

You must set machineConfigPoolSelector in PerformanceProfile:

  apiVersion: performance.openshift.io/v2
  kind: PerformanceProfile
  metadata:
   name: example-performanceprofile
  spec:
  ...
    realTimeKernel:
      enabled: true
    nodeSelector:
       node-role.kubernetes.io/worker-rt: ""
    machineConfigPoolSelector:
       machineconfiguration.openshift.io/role: worker-rt

Verify that a matching machine config pool exists with a label:

$ oc describe mcp/worker-rt

Example output

Name:         worker-rt
Namespace:
Labels:       machineconfiguration.openshift.io/role=worker-rt

OpenShift Container Platform will start configuring the nodes, which might involve multiple reboots. Wait for the nodes to settle. This can take a long time depending on the specific hardware you use, but 20 minutes per node is expected.
Verify everything is working as expected.

14.4.3. Verifying the real-time kernel installation

Use this command to verify that the real-time kernel is installed:

$ oc get node -o wide

Note the worker with the role worker-rt that contains the string 4.18.0-305.30.1.rt7.102.el8_4.x86_64 cri-o://1.23.0-99.rhaos4.10.gitc3131de.el8:

NAME                               	STATUS   ROLES           	AGE 	VERSION                  	INTERNAL-IP
EXTERNAL-IP   OS-IMAGE                                       	KERNEL-VERSION
CONTAINER-RUNTIME
rt-worker-0.example.com	          Ready	 worker,worker-rt   5d17h   v1.23.0
128.66.135.107   <none>    	        Red Hat Enterprise Linux CoreOS 46.82.202008252340-0 (Ootpa)
4.18.0-305.30.1.rt7.102.el8_4.x86_64   cri-o://1.23.0-99.rhaos4.10.gitc3131de.el8
[...]

14.4.4. Creating a workload that works in real-time

Use the following procedures for preparing a workload that will use real-time capabilities.

Procedure

Create a pod with a QoS class of Guaranteed.
Optional: Disable CPU load balancing for DPDK.
Assign a proper node selector.

When writing your applications, follow the general recommendations described in Application tuning and deployment.

14.4.5. Creating a pod with a QoS class of `Guaranteed`

Keep the following in mind when you create a pod that is given a QoS class of Guaranteed:

Every container in the pod must have a memory limit and a memory request, and they must be the same.
Every container in the pod must have a CPU limit and a CPU request, and they must be the same.

The following example shows the configuration file for a pod that has one container. The container has a memory limit and a memory request, both equal to 200 MiB. The container has a CPU limit and a CPU request, both equal to 1 CPU.

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-ctr
    image: <image-pull-spec>
    resources:
      limits:
        memory: "200Mi"
        cpu: "1"
      requests:
        memory: "200Mi"
        cpu: "1"

Create the pod:

$ oc  apply -f qos-pod.yaml --namespace=qos-example

View detailed information about the pod:
```
$ oc get pod qos-demo --namespace=qos-example --output=yaml
```
Example output
```
spec:
  containers:
    ...
status:
  qosClass: Guaranteed
```
Note
If a container specifies its own memory limit, but does not specify a memory request, OpenShift Container Platform automatically assigns a memory request that matches the limit. Similarly, if a container specifies its own CPU limit, but does not specify a CPU request, OpenShift Container Platform automatically assigns a CPU request that matches the limit.

14.4.6. Optional: Disabling CPU load balancing for DPDK

Functionality to disable or enable CPU load balancing is implemented on the CRI-O level. The code under the CRI-O disables or enables CPU load balancing only when the following requirements are met.

The pod must use the performance-<profile-name> runtime class. You can get the proper name by looking at the status of the performance profile, as shown here:
```
apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
...
status:
  ...
  runtimeClass: performance-manual
```
The pod must have the cpu-load-balancing.crio.io: true annotation.

The Performance Addon Operator is responsible for the creation of the high-performance runtime handler config snippet under relevant nodes and for creation of the high-performance runtime class under the cluster. It will have the same content as default runtime handler except it enables the CPU load balancing configuration functionality.

To disable the CPU load balancing for the pod, the Pod specification must include the following fields:

apiVersion: v1
kind: Pod
metadata:
  ...
  annotations:
    ...
    cpu-load-balancing.crio.io: "disable"
    ...
  ...
spec:
  ...
  runtimeClassName: performance-<profile_name>
  ...

Note

Only disable CPU load balancing when the CPU manager static policy is enabled and for pods with guaranteed QoS that use whole CPUs. Otherwise, disabling CPU load balancing can affect the performance of other containers in the cluster.

14.4.7. Assigning a proper node selector

The preferred way to assign a pod to nodes is to use the same node selector the performance profile used, as shown here:

apiVersion: v1
kind: Pod
metadata:
  name: example
spec:
  # ...
  nodeSelector:
    node-role.kubernetes.io/worker-rt: ""

For more information, see Placing pods on specific nodes using node selectors.

14.4.8. Scheduling a workload onto a worker with real-time capabilities

Use label selectors that match the nodes attached to the machine config pool that was configured for low latency by the Performance Addon Operator. For more information, see Assigning pods to nodes.

14.4.9. Managing device interrupt processing for guaranteed pod isolated CPUs

The Performance Addon Operator can manage host CPUs by dividing them into reserved CPUs for cluster and operating system housekeeping duties, including pod infra containers, and isolated CPUs for application containers to run the workloads. This allows you to set CPUs for low latency workloads as isolated.

Device interrupts are load balanced between all isolated and reserved CPUs to avoid CPUs being overloaded, with the exception of CPUs where there is a guaranteed pod running. Guaranteed pod CPUs are prevented from processing device interrupts when the relevant annotations are set for the pod.

In the performance profile, globallyDisableIrqLoadBalancing is used to manage whether device interrupts are processed or not. For certain workloads the reserved CPUs are not always sufficient for dealing with device interrupts, and for this reason, device interrupts are not globally disabled on the isolated CPUs. By default, Performance Addon Operator does not disable device interrupts on isolated CPUs.

To achieve low latency for workloads, some (but not all) pods require the CPUs they are running on to not process device interrupts. A pod annotation, irq-load-balancing.crio.io, is used to define whether device interrupts are processed or not. When configured, CRI-O disables device interrupts only as long as the pod is running.

14.4.9.1. Disabling CPU CFS quota

To reduce CPU throttling for individual guaranteed pods, create a pod specification with the annotation cpu-quota.crio.io: "disable". This annotation disables the CPU completely fair scheduler (CFS) quota at the pod run time. The following pod specification contains this annotation:

apiVersion: performance.openshift.io/v2
kind: Pod
metadata:
  annotations:
      cpu-quota.crio.io: "disable"
spec:
    runtimeClassName: performance-<profile_name>
...

Note

Only disable CPU CFS quota when the CPU manager static policy is enabled and for pods with guaranteed QoS that use whole CPUs. Otherwise, disabling CPU CFS quota can affect the performance of other containers in the cluster.

14.4.9.2. Disabling global device interrupts handling in Performance Addon Operator

To configure Performance Addon Operator to disable global device interrupts for the isolated CPU set, set the globallyDisableIrqLoadBalancing field in the performance profile to true. When true, conflicting pod annotations are ignored. When false, IRQ loads are balanced across all CPUs.

A performance profile snippet illustrates this setting:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: manual
spec:
  globallyDisableIrqLoadBalancing: true
...

14.4.9.3. Disabling interrupt processing for individual pods

To disable interrupt processing for individual pods, ensure that globallyDisableIrqLoadBalancing is set to false in the performance profile. Then, in the pod specification, set the irq-load-balancing.crio.io pod annotation to disable. The following pod specification contains this annotation:

apiVersion: performance.openshift.io/v2
kind: Pod
metadata:
  annotations:
      irq-load-balancing.crio.io: "disable"
spec:
    runtimeClassName: performance-<profile_name>
...

14.4.10. Upgrading the performance profile to use device interrupt processing

When you upgrade the Performance Addon Operator performance profile custom resource definition (CRD) from v1 or v1alpha1 to v2, globallyDisableIrqLoadBalancing is set to true on existing profiles.

Note

globallyDisableIrqLoadBalancing toggles whether IRQ load balancing will be disabled for the Isolated CPU set. When the option is set to true it disables IRQ load balancing for the Isolated CPU set. Setting the option to false allows the IRQs to be balanced across all CPUs.

14.4.10.1. Supported API Versions

The Performance Addon Operator supports v2, v1, and v1alpha1 for the performance profile apiVersion field. The v1 and v1alpha1 APIs are identical. The v2 API includes an optional boolean field globallyDisableIrqLoadBalancing with a default value of false.

14.4.10.1.1. Upgrading Performance Addon Operator API from v1alpha1 to v1

When upgrading Performance Addon Operator API version from v1alpha1 to v1, the v1alpha1 performance profiles are converted on-the-fly using a "None" Conversion strategy and served to the Performance Addon Operator with API version v1.

14.4.10.1.2. Upgrading Performance Addon Operator API from v1alpha1 or v1 to v2

When upgrading from an older Performance Addon Operator API version, the existing v1 and v1alpha1 performance profiles are converted using a conversion webhook that injects the globallyDisableIrqLoadBalancing field with a value of true.

14.4.11. Configuring a node for IRQ dynamic load balancing

To configure a cluster node to handle IRQ dynamic load balancing, do the following:

Log in to the OpenShift Container Platform cluster as a user with cluster-admin privileges.
Set the performance profile apiVersion to use performance.openshift.io/v2.
Remove the globallyDisableIrqLoadBalancing field or set it to false.
Set the appropriate isolated and reserved CPUs. The following snippet illustrates a profile that reserves 2 CPUs. IRQ load-balancing is enabled for pods running on the isolated CPU set:
```
apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: dynamic-irq-profile
spec:
  cpu:
    isolated: 2-5
    reserved: 0-1
...
```
Note
When you configure reserved and isolated CPUs, the infra containers in pods use the reserved CPUs and the application containers use the isolated CPUs.

Create the pod that uses exclusive CPUs, and set irq-load-balancing.crio.io and cpu-quota.crio.io annotations to disable. For example:

apiVersion: v1
kind: Pod
metadata:
  name: dynamic-irq-pod
  annotations:
     irq-load-balancing.crio.io: "disable"
     cpu-quota.crio.io: "disable"
spec:
  containers:
  - name: dynamic-irq-pod
    image: "registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10"
    command: ["sleep", "10h"]
    resources:
      requests:
        cpu: 2
        memory: "200M"
      limits:
        cpu: 2
        memory: "200M"
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""
  runtimeClassName: performance-dynamic-irq-profile
...

Enter the pod runtimeClassName in the form performance-<profile_name>, where <profile_name> is the name from the PerformanceProfile YAML, in this example, performance-dynamic-irq-profile.
Set the node selector to target a cnf-worker.

Ensure the pod is running correctly. Status should be running, and the correct cnf-worker node should be set:

$ oc get pod -o wide

Expected output

NAME              READY   STATUS    RESTARTS   AGE     IP             NODE          NOMINATED NODE   READINESS GATES
dynamic-irq-pod   1/1     Running   0          5h33m   <ip-address>   <node-name>   <none>           <none>

Get the CPUs that the pod configured for IRQ dynamic load balancing runs on:

$ oc exec -it dynamic-irq-pod -- /bin/bash -c "grep Cpus_allowed_list /proc/self/status | awk '{print $2}'"

Expected output

Cpus_allowed_list:  2-3

Ensure the node configuration is applied correctly. SSH into the node to verify the configuration.

$ oc debug node/<node-name>

Expected output

Starting pod/<node-name>-debug ...
To use host binaries, run `chroot /host`

Pod IP: <ip-address>
If you don't see a command prompt, try pressing enter.

sh-4.4#

Verify that you can use the node file system:
```
sh-4.4# chroot /host
```
Expected output
```
sh-4.4#
```
Ensure the default system CPU affinity mask does not include the dynamic-irq-pod CPUs, for example, CPUs 2 and 3.
```
$ cat /proc/irq/default_smp_affinity
```
Example output
```
33
```

Ensure the system IRQs are not configured to run on the dynamic-irq-pod CPUs:

find /proc/irq/ -name smp_affinity_list -exec sh -c 'i="$1"; mask=$(cat $i); file=$(echo $i); echo $file: $mask' _ {} \;

Example output

/proc/irq/0/smp_affinity_list: 0-5
/proc/irq/1/smp_affinity_list: 5
/proc/irq/2/smp_affinity_list: 0-5
/proc/irq/3/smp_affinity_list: 0-5
/proc/irq/4/smp_affinity_list: 0
/proc/irq/5/smp_affinity_list: 0-5
/proc/irq/6/smp_affinity_list: 0-5
/proc/irq/7/smp_affinity_list: 0-5
/proc/irq/8/smp_affinity_list: 4
/proc/irq/9/smp_affinity_list: 4
/proc/irq/10/smp_affinity_list: 0-5
/proc/irq/11/smp_affinity_list: 0
/proc/irq/12/smp_affinity_list: 1
/proc/irq/13/smp_affinity_list: 0-5
/proc/irq/14/smp_affinity_list: 1
/proc/irq/15/smp_affinity_list: 0
/proc/irq/24/smp_affinity_list: 1
/proc/irq/25/smp_affinity_list: 1
/proc/irq/26/smp_affinity_list: 1
/proc/irq/27/smp_affinity_list: 5
/proc/irq/28/smp_affinity_list: 1
/proc/irq/29/smp_affinity_list: 0
/proc/irq/30/smp_affinity_list: 0-5

Some IRQ controllers do not support IRQ re-balancing and will always expose all online CPUs as the IRQ mask. These IRQ controllers effectively run on CPU 0. For more information on the host configuration, SSH into the host and run the following, replacing <irq-num> with the CPU number that you want to query:

$ cat /proc/irq/<irq-num>/effective_affinity

14.4.12. Configuring hyperthreading for a cluster

To configure hyperthreading for an OpenShift Container Platform cluster, set the CPU threads in the performance profile to the same cores that are configured for the reserved or isolated CPU pools.

Note

If you configure a performance profile, and subsequently change the hyperthreading configuration for the host, ensure that you update the CPU isolated and reserved fields in the PerformanceProfile YAML to match the new configuration.

Warning

Disabling a previously enabled host hyperthreading configuration can cause the CPU core IDs listed in the PerformanceProfile YAML to be incorrect. This incorrect configuration can cause the node to become unavailable because the listed CPUs can no longer be found.

Prerequisites

Access to the cluster as a user with the cluster-admin role.
Install the OpenShift CLI (oc).

Procedure

Ascertain which threads are running on what CPUs for the host you want to configure.

You can view which threads are running on the host CPUs by logging in to the cluster and running the following command:

$ lscpu --all --extended

Example output

CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE MAXMHZ    MINMHZ
0   0    0      0    0:0:0:0       yes    4800.0000 400.0000
1   0    0      1    1:1:1:0       yes    4800.0000 400.0000
2   0    0      2    2:2:2:0       yes    4800.0000 400.0000
3   0    0      3    3:3:3:0       yes    4800.0000 400.0000
4   0    0      0    0:0:0:0       yes    4800.0000 400.0000
5   0    0      1    1:1:1:0       yes    4800.0000 400.0000
6   0    0      2    2:2:2:0       yes    4800.0000 400.0000
7   0    0      3    3:3:3:0       yes    4800.0000 400.0000

In this example, there are eight logical CPU cores running on four physical CPU cores. CPU0 and CPU4 are running on physical Core0, CPU1 and CPU5 are running on physical Core 1, and so on.

Alternatively, to view the threads that are set for a particular physical CPU core (cpu0 in the example below), open a command prompt and run the following:

$ cat /sys/devices/system/cpu/cpu0/topology/thread_siblings_list

Example output

0-4

Apply the isolated and reserved CPUs in the PerformanceProfile YAML. For example, you can set logical cores CPU0 and CPU4 as isolated, and logical cores CPU1 to CPU3 and CPU5 to CPU7 as reserved. When you configure reserved and isolated CPUs, the infra containers in pods use the reserved CPUs and the application containers use the isolated CPUs.
```
...
  cpu:
    isolated: 0,4
    reserved: 1-3,5-7
...
```
Note
The reserved and isolated CPU pools must not overlap and together must span all available cores in the worker node.

Important

Hyperthreading is enabled by default on most Intel processors. If you enable hyperthreading, all threads processed by a particular core must be isolated or processed on the same core.

14.4.12.1. Disabling hyperthreading for low latency applications

When configuring clusters for low latency processing, consider whether you want to disable hyperthreading before you deploy the cluster. To disable hyperthreading, do the following:

Create a performance profile that is appropriate for your hardware and topology.

Set nosmt as an additional kernel argument. The following example performance profile illustrates this setting:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: example-performanceprofile
spec:
  additionalKernelArgs:
    - nmi_watchdog=0
    - audit=0
    - mce=off
    - processor.max_cstate=1
    - idle=poll
    - intel_idle.max_cstate=0
    - nosmt
  cpu:
    isolated: 2-3
    reserved: 0-1
  hugepages:
    defaultHugepagesSize: 1G
    pages:
      - count: 2
        node: 0
        size: 1G
  nodeSelector:
    node-role.kubernetes.io/performance: ''
  realTimeKernel:
    enabled: true

Note

When you configure reserved and isolated CPUs, the infra containers in pods use the reserved CPUs and the application containers use the isolated CPUs.

14.5. Tuning nodes for low latency with the performance profile

The performance profile lets you control latency tuning aspects of nodes that belong to a certain machine config pool. After you specify your settings, the PerformanceProfile object is compiled into multiple objects that perform the actual node level tuning:

A MachineConfig file that manipulates the nodes.
A KubeletConfig file that configures the Topology Manager, the CPU Manager, and the OpenShift Container Platform nodes.
The Tuned profile that configures the Node Tuning Operator.

You can use a performance profile to specify whether to update the kernel to kernel-rt, to allocate huge pages, and to partition the CPUs for performing housekeeping duties or running workloads.

Note

You can manually create the PerformanceProfile object or use the Performance Profile Creator (PPC) to generate a performance profile. See the additional resources below for more information on the PPC.

Sample performance profile

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
 name: performance
spec:
 cpu:
  isolated: "4-15" 1
  reserved: "0-3" 2
 hugepages:
  defaultHugepagesSize: "1G"
  pages:
  - size: "1G"
    count: 16
    node: 0
 realTimeKernel:
  enabled: true  3
 numa:  4
  topologyPolicy: "best-effort"
 nodeSelector:
  node-role.kubernetes.io/worker-cnf: "" 5

1: Use this field to isolate specific CPUs to use with application containers for workloads. Set an even number of isolated CPUs to enable the pods to run without errors when hyperthreading is enabled.
2: Use this field to reserve specific CPUs to use with infra containers for housekeeping.
3: Use this field to install the real-time kernel on the node. Valid values are true or false. Setting the true value installs the real-time kernel.
4: Use this field to configure the topology manager policy. Valid values are none (default), best-effort, restricted, and single-numa-node. For more information, see Topology Manager Policies.
5: Use this field to specify a node selector to apply the performance profile to specific nodes.

Additional resources

For information on using the Performance Profile Creator (PPC) to generate a performance profile, see Creating a performance profile.

14.5.1. Configuring huge pages

Nodes must pre-allocate huge pages used in an OpenShift Container Platform cluster. Use the Performance Addon Operator to allocate huge pages on a specific node.

OpenShift Container Platform provides a method for creating and allocating huge pages. Performance Addon Operator provides an easier method for doing this using the performance profile.

For example, in the hugepages pages section of the performance profile, you can specify multiple blocks of size, count, and, optionally, node:

hugepages:
   defaultHugepagesSize: "1G"
   pages:
   - size:  "1G"
     count:  4
     node:  0 1

1: node is the NUMA node in which the huge pages are allocated. If you omit node, the pages are evenly spread across all NUMA nodes.

Note

Wait for the relevant machine config pool status that indicates the update is finished.

These are the only configuration steps you need to do to allocate huge pages.

Verification

To verify the configuration, see the /proc/meminfo file on the node:

$ oc debug node/ip-10-0-141-105.ec2.internal

# grep -i huge /proc/meminfo

Example output

AnonHugePages:    ###### ##
ShmemHugePages:        0 kB
HugePages_Total:       2
HugePages_Free:        2
HugePages_Rsvd:        0
HugePages_Surp:        0
Hugepagesize:       #### ##
Hugetlb:            #### ##

Use oc describe to report the new size:

$ oc describe node worker-0.ocp4poc.example.com | grep -i huge

Example output

                                   hugepages-1g=true
 hugepages-###:  ###
 hugepages-###:  ###

14.5.2. Allocating multiple huge page sizes

You can request huge pages with different sizes under the same container. This allows you to define more complicated pods consisting of containers with different huge page size needs.

For example, you can define sizes 1G and 2M and the Performance Addon Operator will configure both sizes on the node, as shown here:

spec:
  hugepages:
    defaultHugepagesSize: 1G
    pages:
    - count: 1024
      node: 0
      size: 2M
    - count: 4
      node: 1
      size: 1G

14.5.3. Restricting CPUs for infra and application containers

Generic housekeeping and workload tasks use CPUs in a way that may impact latency-sensitive processes. By default, the container runtime uses all online CPUs to run all containers together, which can result in context switches and spikes in latency. Partitioning the CPUs prevents noisy processes from interfering with latency-sensitive processes by separating them from each other. The following table describes how processes run on a CPU after you have tuned the node using the Performance Add-On Operator:

Table 14.1. Process' CPU assignments

Process type	Details
`Burstable` and `BestEffort` pods	Runs on any CPU except where low latency workload is running
Infrastructure pods	Runs on any CPU except where low latency workload is running
Interrupts	Redirects to reserved CPUs (optional in OpenShift Container Platform 4.7 and later)
Kernel processes	Pins to reserved CPUs
Latency-sensitive workload pods	Pins to a specific set of exclusive CPUs from the isolated pool
OS processes/systemd services	Pins to reserved CPUs

The allocatable capacity of cores on a node for pods of all QoS process types, Burstable, BestEffort, or Guaranteed, is equal to the capacity of the isolated pool. The capacity of the reserved pool is removed from the node’s total core capacity for use by the cluster and operating system housekeeping duties.

Example 1

A node features a capacity of 100 cores. Using a performance profile, the cluster administrator allocates 50 cores to the isolated pool and 50 cores to the reserved pool. The cluster administrator assigns 25 cores to QoS Guaranteed pods and 25 cores for BestEffort or Burstable pods. This matches the capacity of the isolated pool.

Example 2

A node features a capacity of 100 cores. Using a performance profile, the cluster administrator allocates 50 cores to the isolated pool and 50 cores to the reserved pool. The cluster administrator assigns 50 cores to QoS Guaranteed pods and one core for BestEffort or Burstable pods. This exceeds the capacity of the isolated pool by one core. Pod scheduling fails because of insufficient CPU capacity.

The exact partitioning pattern to use depends on many factors like hardware, workload characteristics and the expected system load. Some sample use cases are as follows:

If the latency-sensitive workload uses specific hardware, such as a network interface controller (NIC), ensure that the CPUs in the isolated pool are as close as possible to this hardware. At a minimum, you should place the workload in the same Non-Uniform Memory Access (NUMA) node.
The reserved pool is used for handling all interrupts. When depending on system networking, allocate a sufficiently-sized reserve pool to handle all the incoming packet interrupts. In 4.10 and later versions, workloads can optionally be labeled as sensitive.

The decision regarding which specific CPUs should be used for reserved and isolated partitions requires detailed analysis and measurements. Factors like NUMA affinity of devices and memory play a role. The selection also depends on the workload architecture and the specific use case.

Important

The reserved and isolated CPU pools must not overlap and together must span all available cores in the worker node.

To ensure that housekeeping tasks and workloads do not interfere with each other, specify two groups of CPUs in the spec section of the performance profile.

isolated - Specifies the CPUs for the application container workloads. These CPUs have the lowest latency. Processes in this group have no interruptions and can, for example, reach much higher DPDK zero packet loss bandwidth.
reserved - Specifies the CPUs for the cluster and operating system housekeeping duties. Threads in the reserved group are often busy. Do not run latency-sensitive applications in the reserved group. Latency-sensitive applications run in the isolated group.

Procedure

Create a performance profile appropriate for the environment’s hardware and topology.
Add the reserved and isolated parameters with the CPUs you want reserved and isolated for the infra and application containers:
```
apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: infra-cpus
spec:
  cpu:
    reserved: "0-4,9" 1
    isolated: "5-8" 2
  nodeSelector: 3
    node-role.kubernetes.io/worker: ""
```
1
Specify which CPUs are for infra containers to perform cluster and operating system housekeeping duties.
2
Specify which CPUs are for application containers to run workloads.
3
Optional: Specify a node selector to apply the performance profile to specific nodes.

Additional resources

14.6. Reducing NIC queues using the Performance Addon Operator

The Performance Addon Operator allows you to adjust the network interface controller (NIC) queue count for each network device by configuring the performance profile. Device network queues allows the distribution of packets among different physical queues and each queue gets a separate thread for packet processing.

In real-time or low latency systems, all the unnecessary interrupt request lines (IRQs) pinned to the isolated CPUs must be moved to reserved or housekeeping CPUs.

In deployments with applications that require system, OpenShift Container Platform networking or in mixed deployments with Data Plane Development Kit (DPDK) workloads, multiple queues are needed to achieve good throughput and the number of NIC queues should be adjusted or remain unchanged. For example, to achieve low latency the number of NIC queues for DPDK based workloads should be reduced to just the number of reserved or housekeeping CPUs.

Too many queues are created by default for each CPU and these do not fit into the interrupt tables for housekeeping CPUs when tuning for low latency. Reducing the number of queues makes proper tuning possible. Smaller number of queues means a smaller number of interrupts that then fit in the IRQ table.

14.6.1. Adjusting the NIC queues with the performance profile

The performance profile lets you adjust the queue count for each network device.

Supported network devices:

Non-virtual network devices
Network devices that support multiple queues (channels)

Unsupported network devices:

Pure software network interfaces
Block devices
Intel DPDK virtual functions

Prerequisites

Access to the cluster as a user with the cluster-admin role.
Install the OpenShift CLI (oc).

Procedure

Log in to the OpenShift Container Platform cluster running the Performance Addon Operator as a user with cluster-admin privileges.
Create and apply a performance profile appropriate for your hardware and topology. For guidance on creating a profile, see the "Creating a performance profile" section.
Edit this created performance profile:
```
$ oc edit -f <your_profile_name>.yaml
```
Populate the spec field with the net object. The object list can contain two fields:
- userLevelNetworking is a required field specified as a boolean flag. If userLevelNetworking is true, the queue count is set to the reserved CPU count for all supported devices. The default is false.
- devices is an optional field specifying a list of devices that will have the queues set to the reserved CPU count. If the device list is empty, the configuration applies to all network devices. The configuration is as follows:
  - interfaceName: This field specifies the interface name, and it supports shell-style wildcards, which can be positive or negative.
    Example wildcard syntax is as follows: <string> .*
    Negative rules are prefixed with an exclamation mark. To apply the net queue changes to all devices other than the excluded list, use !<device>, for example, !eno1.
  - vendorID: The network device vendor ID represented as a 16-bit hexadecimal number with a 0x prefix.
  - deviceID: The network device ID (model) represented as a 16-bit hexadecimal number with a 0x prefix.
    Note
    When a deviceID is specified, the vendorID must also be defined. A device that matches all of the device identifiers specified in a device entry interfaceName, vendorID, or a pair of vendorID plus deviceID qualifies as a network device. This network device then has its net queues count set to the reserved CPU count.
    When two or more devices are specified, the net queues count is set to any net device that matches one of them.

Set the queue count to the reserved CPU count for all devices by using this example performance profile:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: manual
spec:
  cpu:
    isolated: 3-51,54-103
    reserved: 0-2,52-54
  net:
    userLevelNetworking: true
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""

Set the queue count to the reserved CPU count for all devices matching any of the defined device identifiers by using this example performance profile:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: manual
spec:
  cpu:
    isolated: 3-51,54-103
    reserved: 0-2,52-54
  net:
    userLevelNetworking: true
    devices:
    - interfaceName: “eth0”
    - interfaceName: “eth1”
    - vendorID: “0x1af4”
    - deviceID: “0x1000”
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""

Set the queue count to the reserved CPU count for all devices starting with the interface name eth by using this example performance profile:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: manual
spec:
  cpu:
    isolated: 3-51,54-103
    reserved: 0-2,52-54
  net:
    userLevelNetworking: true
    devices:
    - interfaceName: “eth*”
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""

Set the queue count to the reserved CPU count for all devices with an interface named anything other than eno1 by using this example performance profile:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: manual
spec:
  cpu:
    isolated: 3-51,54-103
    reserved: 0-2,52-54
  net:
    userLevelNetworking: true
    devices:
    - interfaceName: “!eno1”
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""

Set the queue count to the reserved CPU count for all devices that have an interface name eth0, vendorID of 0x1af4, and deviceID of 0x1000 by using this example performance profile:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: manual
spec:
  cpu:
    isolated: 3-51,54-103
    reserved: 0-2,52-54
  net:
    userLevelNetworking: true
    devices:
    - interfaceName: “eth0”
    - vendorID: “0x1af4”
    - deviceID: “0x1000”
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""

Apply the updated performance profile:
```
$ oc apply -f <your_profile_name>.yaml
```

Additional resources

Creating a performance profile.

14.6.2. Verifying the queue status

In this section, a number of examples illustrate different performance profiles and how to verify the changes are applied.

Example 1

In this example, the net queue count is set to the reserved CPU count (2) for all supported devices.

The relevant section from the performance profile is:

apiVersion: performance.openshift.io/v2
metadata:
  name: performance
spec:
  kind: PerformanceProfile
  spec:
    cpu:
      reserved: 0-1  #total = 2
      isolated: 2-8
    net:
      userLevelNetworking: true
# ...

Display the status of the queues associated with a device using the following command:
Note
Run this command on the node where the performance profile was applied.
```
$ ethtool -l <device>
```

Verify the queue status before the profile is applied:

$ ethtool -l ens4

Example output

Channel parameters for ens4:
Pre-set maximums:
RX:         0
TX:         0
Other:      0
Combined:   4
Current hardware settings:
RX:         0
TX:         0
Other:      0
Combined:   4

Verify the queue status after the profile is applied:

$ ethtool -l ens4

Example output

Channel parameters for ens4:
Pre-set maximums:
RX:         0
TX:         0
Other:      0
Combined:   4
Current hardware settings:
RX:         0
TX:         0
Other:      0
Combined:   2 1

1: The combined channel shows that the total count of reserved CPUs for all supported devices is 2. This matches what is configured in the performance profile.

Example 2

In this example, the net queue count is set to the reserved CPU count (2) for all supported network devices with a specific vendorID.

The relevant section from the performance profile is:

apiVersion: performance.openshift.io/v2
metadata:
  name: performance
spec:
  kind: PerformanceProfile
  spec:
    cpu:
      reserved: 0-1  #total = 2
      isolated: 2-8
    net:
      userLevelNetworking: true
      devices:
      - vendorID = 0x1af4
# ...

Display the status of the queues associated with a device using the following command:
Note
Run this command on the node where the performance profile was applied.
```
$ ethtool -l <device>
```

Verify the queue status after the profile is applied:

$ ethtool -l ens4

Example output

Channel parameters for ens4:
Pre-set maximums:
RX:         0
TX:         0
Other:      0
Combined:   4
Current hardware settings:
RX:         0
TX:         0
Other:      0
Combined:   2 1

1: The total count of reserved CPUs for all supported devices with vendorID=0x1af4 is 2. For example, if there is another network device ens2 with vendorID=0x1af4 it will also have total net queues of 2. This matches what is configured in the performance profile.

Example 3

In this example, the net queue count is set to the reserved CPU count (2) for all supported network devices that match any of the defined device identifiers.

The command udevadm info provides a detailed report on a device. In this example the devices are:

# udevadm info -p /sys/class/net/ens4
...
E: ID_MODEL_ID=0x1000
E: ID_VENDOR_ID=0x1af4
E: INTERFACE=ens4
...

# udevadm info -p /sys/class/net/eth0
...
E: ID_MODEL_ID=0x1002
E: ID_VENDOR_ID=0x1001
E: INTERFACE=eth0
...

Set the net queues to 2 for a device with interfaceName equal to eth0 and any devices that have a vendorID=0x1af4 with the following performance profile:

apiVersion: performance.openshift.io/v2
metadata:
  name: performance
spec:
  kind: PerformanceProfile
    spec:
      cpu:
        reserved: 0-1  #total = 2
        isolated: 2-8
      net:
        userLevelNetworking: true
        devices:
        - interfaceName = eth0
        - vendorID = 0x1af4
...

Verify the queue status after the profile is applied:
```
$ ethtool -l ens4
```
Example output
```
Channel parameters for ens4:
Pre-set maximums:
RX:         0
TX:         0
Other:      0
Combined:   4
Current hardware settings:
RX:         0
TX:         0
Other:      0
Combined:   2 1
```
1
The total count of reserved CPUs for all supported devices with vendorID=0x1af4 is set to 2. For example, if there is another network device ens2 with vendorID=0x1af4, it will also have the total net queues set to 2. Similarly, a device with interfaceName equal to eth0 will have total net queues set to 2.

14.6.3. Logging associated with adjusting NIC queues

Log messages detailing the assigned devices are recorded in the respective Tuned daemon logs. The following messages might be recorded to the /var/log/tuned/tuned.log file:

An INFO message is recorded detailing the successfully assigned devices:

INFO tuned.plugins.base: instance net_test (net): assigning devices ens1, ens2, ens3

A WARNING message is recorded if none of the devices can be assigned:

WARNING  tuned.plugins.base: instance net_test: no matching devices available

14.7. Debugging low latency CNF tuning status

The PerformanceProfile custom resource (CR) contains status fields for reporting tuning status and debugging latency degradation issues. These fields report on conditions that describe the state of the operator’s reconciliation functionality.

A typical issue can arise when the status of machine config pools that are attached to the performance profile are in a degraded state, causing the PerformanceProfile status to degrade. In this case, the machine config pool issues a failure message.

The Performance Addon Operator contains the performanceProfile.spec.status.Conditions status field:

Status:
  Conditions:
    Last Heartbeat Time:   2020-06-02T10:01:24Z
    Last Transition Time:  2020-06-02T10:01:24Z
    Status:                True
    Type:                  Available
    Last Heartbeat Time:   2020-06-02T10:01:24Z
    Last Transition Time:  2020-06-02T10:01:24Z
    Status:                True
    Type:                  Upgradeable
    Last Heartbeat Time:   2020-06-02T10:01:24Z
    Last Transition Time:  2020-06-02T10:01:24Z
    Status:                False
    Type:                  Progressing
    Last Heartbeat Time:   2020-06-02T10:01:24Z
    Last Transition Time:  2020-06-02T10:01:24Z
    Status:                False
    Type:                  Degraded

The Status field contains Conditions that specify Type values that indicate the status of the performance profile:

Available

All machine configs and Tuned profiles have been created successfully and are available for cluster components are responsible to process them (NTO, MCO, Kubelet).

Upgradeable

Indicates whether the resources maintained by the Operator are in a state that is safe to upgrade.

Progressing

Indicates that the deployment process from the performance profile has started.

Degraded

Indicates an error if:

Validation of the performance profile has failed.
Creation of all relevant components did not complete successfully.

Each of these types contain the following fields:

Status: The state for the specific type (true or false).
Timestamp: The transaction timestamp.
Reason string: The machine readable reason.
Message string: The human readable reason describing the state and error details, if any.

14.7.1. Machine config pools

A performance profile and its created products are applied to a node according to an associated machine config pool (MCP). The MCP holds valuable information about the progress of applying the machine configurations created by performance addons that encompass kernel args, kube config, huge pages allocation, and deployment of rt-kernel. The performance addons controller monitors changes in the MCP and updates the performance profile status accordingly.

The only conditions returned by the MCP to the performance profile status is when the MCP is Degraded, which leads to performaceProfile.status.condition.Degraded = true.

Example

The following example is for a performance profile with an associated machine config pool (worker-cnf) that was created for it:

The associated machine config pool is in a degraded state:

# oc get mcp

Example output

NAME         CONFIG                                                 UPDATED   UPDATING   DEGRADED   MACHINECOUNT   READYMACHINECOUNT   UPDATEDMACHINECOUNT   DEGRADEDMACHINECOUNT   AGE
master       rendered-master-2ee57a93fa6c9181b546ca46e1571d2d       True      False      False      3              3                   3                     0                      2d21h
worker       rendered-worker-d6b2bdc07d9f5a59a6b68950acf25e5f       True      False      False      2              2                   2                     0                      2d21h
worker-cnf   rendered-worker-cnf-6c838641b8a08fff08dbd8b02fb63f7c   False     True       True       2              1                   1                     1                      2d20h

The describe section of the MCP shows the reason:

# oc describe mcp worker-cnf

Example output

  Message:               Node node-worker-cnf is reporting: "prepping update:
  machineconfig.machineconfiguration.openshift.io \"rendered-worker-cnf-40b9996919c08e335f3ff230ce1d170\" not
  found"
    Reason:                1 nodes are reporting degraded status on sync

The degraded state should also appear under the performance profile status field marked as degraded = true:

# oc describe performanceprofiles performance

Example output

Message: Machine config pool worker-cnf Degraded Reason: 1 nodes are reporting degraded status on sync.
Machine config pool worker-cnf Degraded Message: Node yquinn-q8s5v-w-b-z5lqn.c.openshift-gce-devel.internal is
reporting: "prepping update: machineconfig.machineconfiguration.openshift.io
\"rendered-worker-cnf-40b9996919c08e335f3ff230ce1d170\" not found".    Reason:  MCPDegraded
   Status:  True
   Type:    Degraded

14.8. Collecting low latency tuning debugging data for Red Hat Support

When opening a support case, it is helpful to provide debugging information about your cluster to Red Hat Support.

The must-gather tool enables you to collect diagnostic information about your OpenShift Container Platform cluster, including node tuning, NUMA topology, and other information needed to debug issues with low latency setup.

For prompt support, supply diagnostic information for both OpenShift Container Platform and low latency tuning.

14.8.1. About the must-gather tool

The oc adm must-gather CLI command collects the information from your cluster that is most likely needed for debugging issues, such as:

Resource definitions
Audit logs
Service logs

You can specify one or more images when you run the command by including the --image argument. When you specify an image, the tool collects data related to that feature or product. When you run oc adm must-gather, a new pod is created on the cluster. The data is collected on that pod and saved in a new directory that starts with must-gather.local. This directory is created in your current working directory.

14.8.2. About collecting low latency tuning data

Use the oc adm must-gather CLI command to collect information about your cluster, including features and objects associated with low latency tuning, including:

The Performance Addon Operator namespaces and child objects.
MachineConfigPool and associated MachineConfig objects.
The Node Tuning Operator and associated Tuned objects.
Linux Kernel command line options.
CPU and NUMA topology
Basic PCI device information and NUMA locality.

To collect Performance Addon Operator debugging information with must-gather, you must specify the Performance Addon Operator must-gather image:

--image=registry.redhat.io/openshift4/performance-addon-operator-must-gather-rhel8:v4.10.

14.8.3. Gathering data about specific features

You can gather debugging information about specific features by using the oc adm must-gather CLI command with the --image or --image-stream argument. The must-gather tool supports multiple images, so you can gather data about more than one feature by running a single command.

Note

To collect the default must-gather data in addition to specific feature data, add the --image-stream=openshift/must-gather argument.

Prerequisites

Access to the cluster as a user with the cluster-admin role.
The OpenShift Container Platform CLI (oc) installed.

Procedure

Navigate to the directory where you want to store the must-gather data.
Run the oc adm must-gather command with one or more --image or --image-stream arguments. For example, the following command gathers both the default cluster data and information specific to the Performance Addon Operator:
```
$ oc adm must-gather \
 --image-stream=openshift/must-gather \ 1

 --image=registry.redhat.io/openshift4/performance-addon-operator-must-gather-rhel8:v4.10 2
```
1
The default OpenShift Container Platform must-gather image.
2
The must-gather image for low latency tuning diagnostics.
Create a compressed file from the must-gather directory that was created in your working directory. For example, on a computer that uses a Linux operating system, run the following command:
```
 $ tar cvaf must-gather.tar.gz must-gather.local.5421342344627712289/ 1
```
1
Replace must-gather-local.5421342344627712289/ with the actual directory name.
Attach the compressed file to your support case on the Red Hat Customer Portal.

Additional resources

For more information about MachineConfig and KubeletConfig, see Managing nodes.
For more information about the Node Tuning Operator, see Using the Node Tuning Operator.
For more information about the PerformanceProfile, see Configuring huge pages.
For more information about consuming huge pages from your containers, see How huge pages are consumed by apps.

Chapter 15. Performing latency tests for platform verification

You can use the Cloud-native Network Functions (CNF) tests image to run latency tests on a CNF-enabled OpenShift Container Platform cluster, where all the components required for running CNF workloads are installed. Run the latency tests to validate node tuning for your workload.

The cnf-tests container image is available at registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10.

Important

The cnf-tests image also includes several tests that are not supported by Red Hat at this time. Only the latency tests are supported by Red Hat.

15.1. Prerequisites for running latency tests

Your cluster must meet the following requirements before you can run the latency tests:

You have configured a performance profile with the Performance Addon Operator.
You have applied all the required CNF configurations in the cluster.
You have a pre-existing MachineConfigPool CR applied in the cluster. The default worker pool is worker-cnf.

Additional resources

For more information about creating the cluster performance profile, see Provisioning real-time and low latency workloads.

15.2. About discovery mode for latency tests

Use discovery mode to validate the functionality of a cluster without altering its configuration. Existing environment configurations are used for the tests. The tests can find the configuration items needed and use those items to execute the tests. If resources needed to run a specific test are not found, the test is skipped, providing an appropriate message to the user. After the tests are finished, no cleanup of the pre-configured configuration items is done, and the test environment can be immediately used for another test run.

Important

When running the latency tests, always run the tests with -e DISCOVERY_MODE=true and -ginkgo.focus set to the appropriate latency test. If you do not run the latency tests in discovery mode, your existing live cluster performance profile configuration will be modified by the test run.

Limiting the nodes used during tests

The nodes on which the tests are executed can be limited by specifying a NODES_SELECTOR environment variable, for example, -e NODES_SELECTOR=node-role.kubernetes.io/worker-cnf. Any resources created by the test are limited to nodes with matching labels.

Note

If you want to override the default worker pool, pass the -e ROLE_WORKER_CNF=<custom_worker_pool> variable to the command specifying an appropriate label.

15.3. Measuring latency

The cnf-tests image uses three tools to measure the latency of the system:

hwlatdetect
cyclictest
oslat

Each tool has a specific use. Use the tools in sequence to achieve reliable test results.

hwlatdetect: Measures the baseline that the bare-metal hardware can achieve. Before proceeding with the next latency test, ensure that the latency reported by hwlatdetect meets the required threshold because you cannot fix hardware latency spikes by operating system tuning.
cyclictest: Verifies the real-time kernel scheduler latency after hwlatdetect passes validation. The cyclictest tool schedules a repeated timer and measures the difference between the desired and the actual trigger times. The difference can uncover basic issues with the tuning caused by interrupts or process priorities. The tool must run on a real-time kernel.
oslat: Behaves similarly to a CPU-intensive DPDK application and measures all the interruptions and disruptions to the busy loop that simulates CPU heavy data processing.

The tests introduce the following environment variables:

Table 15.1. Latency test environment variables

Environment variables	Description
`LATENCY_TEST_DELAY`	Specifies the amount of time in seconds after which the test starts running. You can use the variable to allow the CPU manager reconcile loop to update the default CPU pool. The default value is 0.
`LATENCY_TEST_CPUS`	Specifies the number of CPUs that the pod running the latency tests uses. If you do not set the variable, the default configuration includes all isolated CPUs.
`LATENCY_TEST_RUNTIME`	Specifies the amount of time in seconds that the latency test must run. The default value is 300 seconds.
`HWLATDETECT_MAXIMUM_LATENCY`	Specifies the maximum acceptable hardware latency in microseconds for the workload and operating system. If you do not set the value of `HWLATDETECT_MAXIMUM_LATENCY` or `MAXIMUM_LATENCY`, the tool compares the default expected threshold (20μs) and the actual maximum latency in the tool itself. Then, the test fails or succeeds accordingly.
`CYCLICTEST_MAXIMUM_LATENCY`	Specifies the maximum latency in microseconds that all threads expect before waking up during the `cyclictest` run. If you do not set the value of `CYCLICTEST_MAXIMUM_LATENCY` or `MAXIMUM_LATENCY`, the tool skips the comparison of the expected and the actual maximum latency.
`OSLAT_MAXIMUM_LATENCY`	Specifies the maximum acceptable latency in microseconds for the `oslat` test results. If you do not set the value of `OSLAT_MAXIMUM_LATENCY` or `MAXIMUM_LATENCY`, the tool skips the comparison of the expected and the actual maximum latency.
`MAXIMUM_LATENCY`	Unified variable that specifies the maximum acceptable latency in microseconds. Applicable for all available latency tools.
`LATENCY_TEST_RUN`	Boolean parameter that indicates whether the tests should run. `LATENCY_TEST_RUN` is set to `false` by default. To run the latency tests, set this value to `true`.

Note

Variables that are specific to a latency tool take precedence over unified variables. For example, if OSLAT_MAXIMUM_LATENCY is set to 30 microseconds and MAXIMUM_LATENCY is set to 10 microseconds, the oslat test will run with maximum acceptable latency of 30 microseconds.

15.4. Running the latency tests

Run the cluster latency tests to validate node tuning for your Cloud-native Network Functions (CNF) workload.

Important

Always run the latency tests with DISCOVERY_MODE=true set. If you don’t, the test suite will make changes to the running cluster configuration.

Note

When executing podman commands as a non-root or non-privileged user, mounting paths can fail with permission denied errors. To make the podman command work, append :Z to the volumes creation; for example, -v $(pwd)/:/kubeconfig:Z. This allows podman to do the proper SELinux relabeling.

Procedure

Open a shell prompt in the directory containing the kubeconfig file.
You provide the test image with a kubeconfig file in current directory and its related $KUBECONFIG environment variable, mounted through a volume. This allows the running container to use the kubeconfig file from inside the container.

Run the latency tests by entering the following command:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e LATENCY_TEST_RUN=true -e DISCOVERY_MODE=true registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh -ginkgo.focus="\[performance\]\ Latency\ Test"

Optional: Append -ginkgo.dryRun to run the latency tests in dry-run mode. This is useful for checking what the tests run.
Optional: Append -ginkgo.v to run the tests with increased verbosity.
Optional: To run the latency tests against a specific performance profile, run the following command, substituting appropriate values:
```
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e LATENCY_TEST_RUN=true -e LATENCY_TEST_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
-e PERF_TEST_PROFILE=<performance_profile> registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh -ginkgo.focus="[performance]\ Latency\ Test"
```
where:
<performance_profile>
Is the name of the performance profile you want to run the latency tests against.
Important
For valid latency tests results, run the tests for at least 12 hours.

15.4.1. Running hwlatdetect

The hwlatdetect tool is available in the rt-kernel package with a regular subscription of Red Hat Enterprise Linux (RHEL) 8.x.

Important

Always run the latency tests with DISCOVERY_MODE=true set. If you don’t, the test suite will make changes to the running cluster configuration.

Note

Prerequisites

You have installed the real-time kernel in the cluster.
You have logged in to registry.redhat.io with your Customer Portal credentials.

Procedure

To run the hwlatdetect tests, run the following command, substituting variable values as appropriate:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e LATENCY_TEST_RUN=true -e DISCOVERY_MODE=true -e ROLE_WORKER_CNF=worker-cnf \
-e LATENCY_TEST_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh -ginkgo.v -ginkgo.focus="hwlatdetect"

The hwlatdetect test runs for 10 minutes (600 seconds). The test runs successfully when the maximum observed latency is lower than MAXIMUM_LATENCY (20 μs).

If the results exceed the latency threshold, the test fails.

Important

For valid results, the test should run for at least 12 hours.

Example failure output

running /usr/bin/validationsuite -ginkgo.v -ginkgo.focus=hwlatdetect
I0210 17:08:38.607699       7 request.go:668] Waited for 1.047200253s due to client-side throttling, not priority and fairness, request: GET:https://api.ocp.demo.lab:6443/apis/apps.openshift.io/v1?timeout=32s
Running Suite: CNF Features e2e validation
==========================================
Random Seed: 1644512917
Will run 0 of 48 specs

SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
Ran 0 of 48 Specs in 0.001 seconds
SUCCESS! -- 0 Passed | 0 Failed | 0 Pending | 48 Skipped

PASS
Discovery mode enabled, skipping setup
running /usr/bin/cnftests -ginkgo.v -ginkgo.focus=hwlatdetect
I0210 17:08:41.179269      40 request.go:668] Waited for 1.046001096s due to client-side throttling, not priority and fairness, request: GET:https://api.ocp.demo.lab:6443/apis/storage.k8s.io/v1beta1?timeout=32s
Running Suite: CNF Features e2e integration tests
=================================================
Random Seed: 1644512920
Will run 1 of 151 specs

SSSSSSS
------------------------------
[performance] Latency Test with the hwlatdetect image
  should succeed
  /remote-source/app/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:221
STEP: Waiting two minutes to download the latencyTest image
STEP: Waiting another two minutes to give enough time for the cluster to move the pod to Succeeded phase
Feb 10 17:10:56.045: [INFO]: found mcd machine-config-daemon-dzpw7 for node ocp-worker-0.demo.lab
Feb 10 17:10:56.259: [INFO]: found mcd machine-config-daemon-dzpw7 for node ocp-worker-0.demo.lab
Feb 10 17:11:56.825: [ERROR]: timed out waiting for the condition

• Failure [193.903 seconds]
[performance] Latency Test
/remote-source/app/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:60
  with the hwlatdetect image
  /remote-source/app/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:213
    should succeed [It]
    /remote-source/app/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:221

    Log file created at: 2022/02/10 17:08:45
    Running on machine: hwlatdetect-cd8b6
    Binary: Built with gc go1.16.6 for linux/amd64
    Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg
    I0210 17:08:45.716288       1 node.go:37] Environment information: /proc/cmdline: BOOT_IMAGE=(hd0,gpt3)/ostree/rhcos-56fabc639a679b757ebae30e5f01b2ebd38e9fde9ecae91c41be41d3e89b37f8/vmlinuz-4.18.0-305.34.2.rt7.107.el8_4.x86_64 random.trust_cpu=on console=tty0 console=ttyS0,115200n8 ignition.platform.id=qemu ostree=/ostree/boot.0/rhcos/56fabc639a679b757ebae30e5f01b2ebd38e9fde9ecae91c41be41d3e89b37f8/0 root=UUID=56731f4f-f558-46a3-85d3-d1b579683385 rw rootflags=prjquota skew_tick=1 nohz=on rcu_nocbs=3-5 tuned.non_isolcpus=ffffffc7 intel_pstate=disable nosoftlockup tsc=nowatchdog intel_iommu=on iommu=pt isolcpus=managed_irq,3-5 systemd.cpu_affinity=0,1,2,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31 + +
    I0210 17:08:45.716782       1 node.go:44] Environment information: kernel version 4.18.0-305.34.2.rt7.107.el8_4.x86_64
    I0210 17:08:45.716861       1 main.go:50] running the hwlatdetect command with arguments [/usr/bin/hwlatdetect --threshold 1 --hardlimit 1 --duration 10 --window 10000000us --width 950000us]
    F0210 17:08:56.815204       1 main.go:53] failed to run hwlatdetect command; out: hwlatdetect:  test duration 10 seconds
       detector: tracer
       parameters:
            Latency threshold: 1us 1
            Sample window:     10000000us
            Sample width:      950000us
         Non-sampling period:  9050000us
            Output File:       None

    Starting test
    test finished
    Max Latency: 24us 2
    Samples recorded: 1
    Samples exceeding threshold: 1
    ts: 1644512927.163556381, inner:20, outer:24
    ; err: exit status 1
    goroutine 1 [running]:
    k8s.io/klog.stacks(0xc000010001, 0xc00012e000, 0x25b, 0x2710)
        /remote-source/app/vendor/k8s.io/klog/klog.go:875 +0xb9
    k8s.io/klog.(*loggingT).output(0x5bed00, 0xc000000003, 0xc0000121c0, 0x53ea81, 0x7, 0x35, 0x0)
        /remote-source/app/vendor/k8s.io/klog/klog.go:829 +0x1b0
    k8s.io/klog.(*loggingT).printf(0x5bed00, 0x3, 0x5082da, 0x33, 0xc000113f58, 0x2, 0x2)
        /remote-source/app/vendor/k8s.io/klog/klog.go:707 +0x153
    k8s.io/klog.Fatalf(...)
        /remote-source/app/vendor/k8s.io/klog/klog.go:1276
    main.main()
        /remote-source/app/cnf-tests/pod-utils/hwlatdetect-runner/main.go:53 +0x897

    goroutine 6 [chan receive]:
    k8s.io/klog.(*loggingT).flushDaemon(0x5bed00)
        /remote-source/app/vendor/k8s.io/klog/klog.go:1010 +0x8b
    created by k8s.io/klog.init.0
        /remote-source/app/vendor/k8s.io/klog/klog.go:411 +0xd8

    goroutine 7 [chan receive]:
    k8s.io/klog/v2.(*loggingT).flushDaemon(0x5bede0)
        /remote-source/app/vendor/k8s.io/klog/v2/klog.go:1169 +0x8b
    created by k8s.io/klog/v2.init.0
        /remote-source/app/vendor/k8s.io/klog/v2/klog.go:420 +0xdf
    Unexpected error:
        <*errors.errorString | 0xc000418ed0>: {
            s: "timed out waiting for the condition",
        }
        timed out waiting for the condition
    occurred

    /remote-source/app/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:433
------------------------------
SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
JUnit report was created: /junit.xml/cnftests-junit.xml


Summarizing 1 Failure:

[Fail] [performance] Latency Test with the hwlatdetect image [It] should succeed
/remote-source/app/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:433

Ran 1 of 151 Specs in 222.254 seconds
FAIL! -- 0 Passed | 1 Failed | 0 Pending | 150 Skipped

--- FAIL: TestTest (222.45s)
FAIL

1: You can configure the latency threshold by using the MAXIMUM_LATENCY or the HWLATDETECT_MAXIMUM_LATENCY environment variables.
2: The maximum latency value measured during the test.

Example hwlatdetect test results

You can capture the following types of results:

Rough results that are gathered after each run to create a history of impact on any changes made throughout the test.
The combined set of the rough tests with the best results and configuration settings.

Example of good results

hwlatdetect: test duration 3600 seconds
detector: tracer
parameters:
Latency threshold: 10us
Sample window: 1000000us
Sample width: 950000us
Non-sampling period: 50000us
Output File: None

Starting test
test finished
Max Latency: Below threshold
Samples recorded: 0

The hwlatdetect tool only provides output if the sample exceeds the specified threshold.

Example of bad results

hwlatdetect: test duration 3600 seconds
detector: tracer
parameters:Latency threshold: 10usSample window: 1000000us
Sample width: 950000usNon-sampling period: 50000usOutput File: None

Starting tests:1610542421.275784439, inner:78, outer:81
ts: 1610542444.330561619, inner:27, outer:28
ts: 1610542445.332549975, inner:39, outer:38
ts: 1610542541.568546097, inner:47, outer:32
ts: 1610542590.681548531, inner:13, outer:17
ts: 1610543033.818801482, inner:29, outer:30
ts: 1610543080.938801990, inner:90, outer:76
ts: 1610543129.065549639, inner:28, outer:39
ts: 1610543474.859552115, inner:28, outer:35
ts: 1610543523.973856571, inner:52, outer:49
ts: 1610543572.089799738, inner:27, outer:30
ts: 1610543573.091550771, inner:34, outer:28
ts: 1610543574.093555202, inner:116, outer:63

The output of hwlatdetect shows that multiple samples exceed the threshold. However, the same output can indicate different results based on the following factors:

The duration of the test
The number of CPU cores
The host firmware settings

Warning

Before proceeding with the next latency test, ensure that the latency reported by hwlatdetect meets the required threshold. Fixing latencies introduced by hardware might require you to contact the system vendor support.

Not all latency spikes are hardware related. Ensure that you tune the host firmware to meet your workload requirements. For more information, see Setting firmware parameters for system tuning.

15.4.2. Running cyclictest

The cyclictest tool measures the real-time kernel scheduler latency on the specified CPUs.

Important

Always run the latency tests with DISCOVERY_MODE=true set. If you don’t, the test suite will make changes to the running cluster configuration.

Note

Prerequisites

You have logged in to registry.redhat.io with your Customer Portal credentials.
You have installed the real-time kernel in the cluster.
You have applied a cluster performance profile by using Performance addon operator.

Procedure

To perform the cyclictest, run the following command, substituting variable values as appropriate:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e LATENCY_TEST_RUN=true -e DISCOVERY_MODE=true -e ROLE_WORKER_CNF=worker-cnf \
-e LATENCY_TEST_CPUS=10 -e LATENCY_TEST_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh -ginkgo.v -ginkgo.focus="cyclictest"

The command runs the cyclictest tool for 10 minutes (600 seconds). The test runs successfully when the maximum observed latency is lower than MAXIMUM_LATENCY (in this example, 20 μs). Latency spikes of 20 μs and above are generally not acceptable for telco RAN workloads.

If the results exceed the latency threshold, the test fails.

Important

For valid results, the test should run for at least 12 hours.

Example failure output

Discovery mode enabled, skipping setup
running /usr/bin//cnftests -ginkgo.v -ginkgo.focus=cyclictest
I0811 15:02:36.350033      20 request.go:668] Waited for 1.049965918s due to client-side throttling, not priority and fairness, request: GET:https://api.cnfdc8.t5g.lab.eng.bos.redhat.com:6443/apis/machineconfiguration.openshift.io/v1?timeout=32s
Running Suite: CNF Features e2e integration tests
=================================================
Random Seed: 1628694153
Will run 1 of 138 specs

SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
------------------------------
[performance] Latency Test with the cyclictest image
  should succeed
  /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:200
STEP: Waiting two minutes to download the latencyTest image
STEP: Waiting another two minutes to give enough time for the cluster to move the pod to Succeeded phase
Aug 11 15:03:06.826: [INFO]: found mcd machine-config-daemon-wf4w8 for node cnfdc8.clus2.t5g.lab.eng.bos.redhat.com

• Failure [22.527 seconds]
[performance] Latency Test
/go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:84
  with the cyclictest image
  /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:188
    should succeed [It]
    /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:200

    The current latency 27 is bigger than the expected one 20
    Expected
        <bool>: false
    to be true

    /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:219

Log file created at: 2021/08/11 15:02:51
Running on machine: cyclictest-knk7d
Binary: Built with gc go1.16.6 for linux/amd64
Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg
I0811 15:02:51.092254       1 node.go:37] Environment information: /proc/cmdline: BOOT_IMAGE=(hd0,gpt3)/ostree/rhcos-612d89f4519a53ad0b1a132f4add78372661bfb3994f5fe115654971aa58a543/vmlinuz-4.18.0-305.10.2.rt7.83.el8_4.x86_64 ip=dhcp random.trust_cpu=on console=tty0 console=ttyS0,115200n8 ostree=/ostree/boot.1/rhcos/612d89f4519a53ad0b1a132f4add78372661bfb3994f5fe115654971aa58a543/0 ignition.platform.id=openstack root=UUID=5a4ddf16-9372-44d9-ac4e-3ee329e16ab3 rw rootflags=prjquota skew_tick=1 nohz=on rcu_nocbs=1-3 tuned.non_isolcpus=000000ff,ffffffff,ffffffff,fffffff1 intel_pstate=disable nosoftlockup tsc=nowatchdog intel_iommu=on iommu=pt isolcpus=managed_irq,1-3 systemd.cpu_affinity=0,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103 default_hugepagesz=1G hugepagesz=2M hugepages=128 nmi_watchdog=0 audit=0 mce=off processor.max_cstate=1 idle=poll intel_idle.max_cstate=0
I0811 15:02:51.092427       1 node.go:44] Environment information: kernel version 4.18.0-305.10.2.rt7.83.el8_4.x86_64
I0811 15:02:51.092450       1 main.go:48] running the cyclictest command with arguments \
[-D 600 -95 1 -t 10 -a 2,4,6,8,10,54,56,58,60,62 -h 30 -i 1000 --quiet]
I0811 15:03:06.147253       1 main.go:54] succeeded to run the cyclictest command: # /dev/cpu_dma_latency set to 0us
# Histogram
000000 000000   000000  000000  000000  000000  000000  000000  000000  000000  000000
000001 000000   005561  027778  037704  011987  000000  120755  238981  081847  300186
000002 587440   581106  564207  554323  577416  590635  474442  357940  513895  296033
000003 011751   011441  006449  006761  008409  007904  002893  002066  003349  003089
000004 000527   001079  000914  000712  001451  001120  000779  000283  000350  000251

More histogram entries ...
# Min Latencies: 00002 00001 00001 00001 00001 00002 00001 00001 00001 00001
# Avg Latencies: 00002 00002 00002 00001 00002 00002 00001 00001 00001 00001
# Max Latencies: 00018 00465 00361 00395 00208 00301 02052 00289 00327 00114
# Histogram Overflows: 00000 00220 00159 00128 00202 00017 00069 00059 00045 00120
# Histogram Overflow at cycle number:
# Thread 0:
# Thread 1: 01142 01439 05305 … # 00190 others
# Thread 2: 20895 21351 30624 … # 00129 others
# Thread 3: 01143 17921 18334 … # 00098 others
# Thread 4: 30499 30622 31566 ... # 00172 others
# Thread 5: 145221 170910 171888 ...
# Thread 6: 01684 26291 30623 ...# 00039 others
# Thread 7: 28983 92112 167011 … 00029 others
# Thread 8: 45766 56169 56171 ...# 00015 others
# Thread 9: 02974 08094 13214 ... # 00090 others

Example cyclictest results

The same output can indicate different results for different workloads. For example, spikes up to 18μs are acceptable for 4G DU workloads, but not for 5G DU workloads.

Example of good results

running cmd: cyclictest -q -D 10m -p 1 -t 16 -a 2,4,6,8,10,12,14,16,54,56,58,60,62,64,66,68 -h 30 -i 1000 -m
# Histogram
000000 000000   000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000
000001 000000   000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000
000002 579506   535967  418614  573648  532870  529897  489306  558076  582350  585188  583793  223781  532480  569130  472250  576043
More histogram entries ...
# Total: 000600000 000600000 000600000 000599999 000599999 000599999 000599998 000599998 000599998 000599997 000599997 000599996 000599996 000599995 000599995 000599995
# Min Latencies: 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002
# Avg Latencies: 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002
# Max Latencies: 00005 00005 00004 00005 00004 00004 00005 00005 00006 00005 00004 00005 00004 00004 00005 00004
# Histogram Overflows: 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000
# Histogram Overflow at cycle number:
# Thread 0:
# Thread 1:
# Thread 2:
# Thread 3:
# Thread 4:
# Thread 5:
# Thread 6:
# Thread 7:
# Thread 8:
# Thread 9:
# Thread 10:
# Thread 11:
# Thread 12:
# Thread 13:
# Thread 14:
# Thread 15:

Example of bad results

running cmd: cyclictest -q -D 10m -p 1 -t 16 -a 2,4,6,8,10,12,14,16,54,56,58,60,62,64,66,68 -h 30 -i 1000 -m
# Histogram
000000 000000   000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000
000001 000000   000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000  000000
000002 564632   579686  354911  563036  492543  521983  515884  378266  592621  463547  482764  591976  590409  588145  589556  353518
More histogram entries ...
# Total: 000599999 000599999 000599999 000599997 000599997 000599998 000599998 000599997 000599997 000599996 000599995 000599996 000599995 000599995 000599995 000599993
# Min Latencies: 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002
# Avg Latencies: 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002 00002
# Max Latencies: 00493 00387 00271 00619 00541 00513 00009 00389 00252 00215 00539 00498 00363 00204 00068 00520
# Histogram Overflows: 00001 00001 00001 00002 00002 00001 00000 00001 00001 00001 00002 00001 00001 00001 00001 00002
# Histogram Overflow at cycle number:
# Thread 0: 155922
# Thread 1: 110064
# Thread 2: 110064
# Thread 3: 110063 155921
# Thread 4: 110063 155921
# Thread 5: 155920
# Thread 6:
# Thread 7: 110062
# Thread 8: 110062
# Thread 9: 155919
# Thread 10: 110061 155919
# Thread 11: 155918
# Thread 12: 155918
# Thread 13: 110060
# Thread 14: 110060
# Thread 15: 110059 155917

15.4.3. Running oslat

The oslat test simulates a CPU-intensive DPDK application and measures all the interruptions and disruptions to test how the cluster handles CPU heavy data processing.

Important

Always run the latency tests with DISCOVERY_MODE=true set. If you don’t, the test suite will make changes to the running cluster configuration.

Note

Prerequisites

You have logged in to registry.redhat.io with your Customer Portal credentials.
You have applied a cluster performance profile by using the Performance addon operator.

Procedure

To perform the oslat test, run the following command, substituting variable values as appropriate:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e LATENCY_TEST_RUN=true -e DISCOVERY_MODE=true -e ROLE_WORKER_CNF=worker-cnf \
-e LATENCY_TEST_CPUS=7 -e LATENCY_TEST_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh -ginkgo.v -ginkgo.focus="oslat"

LATENCY_TEST_CPUS specifices the list of CPUs to test with the oslat command.

The command runs the oslat tool for 10 minutes (600 seconds). The test runs successfully when the maximum observed latency is lower than MAXIMUM_LATENCY (20 μs).

If the results exceed the latency threshold, the test fails.

Important

For valid results, the test should run for at least 12 hours.

Example failure output

running /usr/bin//validationsuite -ginkgo.v -ginkgo.focus=oslat
I0829 12:36:55.386776       8 request.go:668] Waited for 1.000303471s due to client-side throttling, not priority and fairness, request: GET:https://api.cnfdc8.t5g.lab.eng.bos.redhat.com:6443/apis/authentication.k8s.io/v1?timeout=32s
Running Suite: CNF Features e2e validation
==========================================

Discovery mode enabled, skipping setup
running /usr/bin//cnftests -ginkgo.v -ginkgo.focus=oslat
I0829 12:37:01.219077      20 request.go:668] Waited for 1.050010755s due to client-side throttling, not priority and fairness, request: GET:https://api.cnfdc8.t5g.lab.eng.bos.redhat.com:6443/apis/snapshot.storage.k8s.io/v1beta1?timeout=32s
Running Suite: CNF Features e2e integration tests
=================================================
Random Seed: 1630240617
Will run 1 of 142 specs

SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
------------------------------
[performance] Latency Test with the oslat image
  should succeed
  /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:134
STEP: Waiting two minutes to download the latencyTest image
STEP: Waiting another two minutes to give enough time for the cluster to move the pod to Succeeded phase
Aug 29 12:37:59.324: [INFO]: found mcd machine-config-daemon-wf4w8 for node cnfdc8.clus2.t5g.lab.eng.bos.redhat.com

• Failure [49.246 seconds]
[performance] Latency Test
/go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:59
  with the oslat image
  /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:112
    should succeed [It]
    /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:134

    The current latency 27 is bigger than the expected one 20 1
    Expected
        <bool>: false
    to be true
 /go/src/github.com/openshift-kni/cnf-features-deploy/vendor/github.com/openshift-kni/performance-addon-operators/functests/4_latency/latency.go:168

Log file created at: 2021/08/29 13:25:21
Running on machine: oslat-57c2g
Binary: Built with gc go1.16.6 for linux/amd64
Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg
I0829 13:25:21.569182       1 node.go:37] Environment information: /proc/cmdline: BOOT_IMAGE=(hd0,gpt3)/ostree/rhcos-612d89f4519a53ad0b1a132f4add78372661bfb3994f5fe115654971aa58a543/vmlinuz-4.18.0-305.10.2.rt7.83.el8_4.x86_64 ip=dhcp random.trust_cpu=on console=tty0 console=ttyS0,115200n8 ostree=/ostree/boot.0/rhcos/612d89f4519a53ad0b1a132f4add78372661bfb3994f5fe115654971aa58a543/0 ignition.platform.id=openstack root=UUID=5a4ddf16-9372-44d9-ac4e-3ee329e16ab3 rw rootflags=prjquota skew_tick=1 nohz=on rcu_nocbs=1-3 tuned.non_isolcpus=000000ff,ffffffff,ffffffff,fffffff1 intel_pstate=disable nosoftlockup tsc=nowatchdog intel_iommu=on iommu=pt isolcpus=managed_irq,1-3 systemd.cpu_affinity=0,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103 default_hugepagesz=1G hugepagesz=2M hugepages=128 nmi_watchdog=0 audit=0 mce=off processor.max_cstate=1 idle=poll intel_idle.max_cstate=0
I0829 13:25:21.569345       1 node.go:44] Environment information: kernel version 4.18.0-305.10.2.rt7.83.el8_4.x86_64
I0829 13:25:21.569367       1 main.go:53] Running the oslat command with arguments \
[--duration 600 --rtprio 1 --cpu-list 4,6,52,54,56,58 --cpu-main-thread 2]
I0829 13:35:22.632263       1 main.go:59] Succeeded to run the oslat command: oslat V 2.00
Total runtime:    600 seconds
Thread priority:  SCHED_FIFO:1
CPU list:     4,6,52,54,56,58
CPU for main thread:  2
Workload:     no
Workload mem:     0 (KiB)
Preheat cores:    6

Pre-heat for 1 seconds...
Test starts...
Test completed.

        Core:  4 6 52 54 56 58
    CPU Freq:  2096 2096 2096 2096 2096 2096 (Mhz)
    001 (us):  19390720316 19141129810 20265099129 20280959461 19391991159 19119877333
    002 (us):  5304 5249 5777 5947 6829 4971
    003 (us):  28 14 434 47 208 21
    004 (us):  1388 853 123568 152817 5576 0
    005 (us):  207850 223544 103827 91812 227236 231563
    006 (us):  60770 122038 277581 323120 122633 122357
    007 (us):  280023 223992 63016 25896 214194 218395
    008 (us):  40604 25152 24368 4264 24440 25115
    009 (us):  6858 3065 5815 810 3286 2116
    010 (us):  1947 936 1452 151 474 361
  ...
     Minimum:  1 1 1 1 1 1 (us)
     Average:  1.000 1.000 1.000 1.000 1.000 1.000 (us)
     Maximum:  37 38 49 28 28 19 (us)
     Max-Min:  36 37 48 27 27 18 (us)
    Duration:  599.667 599.667 599.667 599.667 599.667 599.667 (sec)

1: In this example, the measured latency is outside the maximum allowed value.

15.5. Generating a latency test failure report

Use the following procedures to generate a JUnit latency test output and test failure report.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in as a user with cluster-admin privileges.

Procedure

Create a test failure report with information about the cluster state and resources for troubleshooting by passing the --report parameter with the path to where the report is dumped:

$ podman run -v $(pwd)/:/kubeconfig:Z -v $(pwd)/reportdest:<report_folder_path> \
-e KUBECONFIG=/kubeconfig/kubeconfig  -e DISCOVERY_MODE=true \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh --report <report_folder_path> \
-ginkgo.focus="\[performance\]\ Latency\ Test"

where:

<report_folder_path>: Is the path to the folder where the report is generated.

15.6. Generating a JUnit latency test report

Use the following procedures to generate a JUnit latency test output and test failure report.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in as a user with cluster-admin privileges.

Procedure

Create a JUnit-compliant XML report by passing the --junit parameter together with the path to where the report is dumped:

$ podman run -v $(pwd)/:/kubeconfig:Z -v $(pwd)/junitdest:<junit_folder_path> \
-e KUBECONFIG=/kubeconfig/kubeconfig  -e DISCOVERY_MODE=true \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh --junit <junit_folder_path> \
-ginkgo.focus="\[performance\]\ Latency\ Test"

where:

<junit_folder_path>: Is the path to the folder where the junit report is generated

15.7. Running latency tests on a single-node OpenShift cluster

You can run latency tests on single-node OpenShift clusters.

Important

Always run the latency tests with DISCOVERY_MODE=true set. If you don’t, the test suite will make changes to the running cluster configuration.

Note

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in as a user with cluster-admin privileges.

Procedure

To run the latency tests on a single-node OpenShift cluster, run the following command:
```
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e DISCOVERY_MODE=true -e ROLE_WORKER_CNF=master \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/test-run.sh -ginkgo.focus="\[performance\]\ Latency\ Test"
```
Note
ROLE_WORKER_CNF=master is required because master is the only machine pool to which the node belongs. For more information about setting the required MachineConfigPool for the latency tests, see "Prerequisites for running latency tests".
After running the test suite, all the dangling resources are cleaned up.

15.8. Running latency tests in a disconnected cluster

The CNF tests image can run tests in a disconnected cluster that is not able to reach external registries. This requires two steps:

Mirroring the cnf-tests image to the custom disconnected registry.
Instructing the tests to consume the images from the custom disconnected registry.

Mirroring the images to a custom registry accessible from the cluster

A mirror executable is shipped in the image to provide the input required by oc to mirror the test image to a local registry.

Run this command from an intermediate machine that has access to the cluster and registry.redhat.io:
```
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
/usr/bin/mirror -registry <disconnected_registry> | oc image mirror -f -
```
where:
<disconnected_registry>
Is the disconnected mirror registry you have configured, for example, my.local.registry:5000/.

When you have mirrored the cnf-tests image into the disconnected registry, you must override the original registry used to fetch the images when running the tests, for example:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e DISCOVERY_MODE=true -e IMAGE_REGISTRY="<disconnected_registry>" \
-e CNF_TESTS_IMAGE="cnf-tests-rhel8:v4.10" \
/usr/bin/test-run.sh -ginkgo.focus="\[performance\]\ Latency\ Test"

Configuring the tests to consume images from a custom registry

You can run the latency tests using a custom test image and image registry using CNF_TESTS_IMAGE and IMAGE_REGISTRY variables.

To configure the latency tests to use a custom test image and image registry, run the following command:
```
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e IMAGE_REGISTRY="<custom_image_registry>" \
-e CNF_TESTS_IMAGE="<custom_cnf-tests_image>" \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 /usr/bin/test-run.sh
```
where:
<custom_image_registry>
is the custom image registry, for example, custom.registry:5000/.
<custom_cnf-tests_image>
is the custom cnf-tests image, for example, custom-cnf-tests-image:latest.

Mirroring images to the cluster OpenShift image registry

OpenShift Container Platform provides a built-in container image registry, which runs as a standard workload on the cluster.

Procedure

Gain external access to the registry by exposing it with a route:

$ oc patch configs.imageregistry.operator.openshift.io/cluster --patch '{"spec":{"defaultRoute":true}}' --type=merge

Fetch the registry endpoint by running the following command:

$ REGISTRY=$(oc get route default-route -n openshift-image-registry --template='{{ .spec.host }}')

Create a namespace for exposing the images:
```
$ oc create ns cnftests
```

Make the image stream available to all the namespaces used for tests. This is required to allow the tests namespaces to fetch the images from the cnf-tests image stream. Run the following commands:

$ oc policy add-role-to-user system:image-puller system:serviceaccount:cnf-features-testing:default --namespace=cnftests

$ oc policy add-role-to-user system:image-puller system:serviceaccount:performance-addon-operators-testing:default --namespace=cnftests

Retrieve the docker secret name and auth token by running the following commands:

$ SECRET=$(oc -n cnftests get secret | grep builder-docker | awk {'print $1'}

$ TOKEN=$(oc -n cnftests get secret $SECRET -o jsonpath="{.data['\.dockercfg']}" | base64 --decode | jq '.["image-registry.openshift-image-registry.svc:5000"].auth')

Create a dockerauth.json file, for example:

$ echo "{\"auths\": { \"$REGISTRY\": { \"auth\": $TOKEN } }}" > dockerauth.json

Do the image mirroring:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
registry.redhat.io/openshift4/cnf-tests-rhel8:4.10 \
/usr/bin/mirror -registry $REGISTRY/cnftests |  oc image mirror --insecure=true \
-a=$(pwd)/dockerauth.json -f -

Run the tests:

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
-e DISCOVERY_MODE=true -e IMAGE_REGISTRY=image-registry.openshift-image-registry.svc:5000/cnftests \
cnf-tests-local:latest /usr/bin/test-run.sh -ginkgo.focus="\[performance\]\ Latency\ Test"

Mirroring a different set of test images

You can optionally change the default upstream images that are mirrored for the latency tests.

Procedure

The mirror command tries to mirror the upstream images by default. This can be overridden by passing a file with the following format to the image:
```
[
    {
        "registry": "public.registry.io:5000",
        "image": "imageforcnftests:4.10"
    }
]
```

Pass the file to the mirror command, for example saving it locally as images.json. With the following command, the local path is mounted in /kubeconfig inside the container and that can be passed to the mirror command.

$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 /usr/bin/mirror \
--registry "my.local.registry:5000/" --images "/kubeconfig/images.json" \
|  oc image mirror -f -

15.9. Troubleshooting errors with the cnf-tests container

To run latency tests, the cluster must be accessible from within the cnf-tests container.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in as a user with cluster-admin privileges.

Procedure

Verify that the cluster is accessible from inside the cnf-tests container by running the following command:
```
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
registry.redhat.io/openshift4/cnf-tests-rhel8:v4.10 \
oc get nodes
```
If this command does not work, an error related to spanning across DNS, MTU size, or firewall access might be occurring.

Chapter 16. Topology Aware Lifecycle Manager for cluster updates

You can use the Topology Aware Lifecycle Manager (TALM) to manage the software lifecycle of multiple single-node OpenShift clusters. TALM uses Red Hat Advanced Cluster Management (RHACM) policies to perform changes on the target clusters.

Important

Topology Aware Lifecycle Manager is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

16.1. About the Topology Aware Lifecycle Manager configuration

The Topology Aware Lifecycle Manager (TALM) manages the deployment of Red Hat Advanced Cluster Management (RHACM) policies for one or more OpenShift Container Platform clusters. Using TALM in a large network of clusters allows the phased rollout of policies to the clusters in limited batches. This helps to minimize possible service disruptions when updating. With TALM, you can control the following actions:

The timing of the update
The number of RHACM-managed clusters
The subset of managed clusters to apply the policies to
The update order of the clusters
The set of policies remediated to the cluster
The order of policies remediated to the cluster

TALM supports the orchestration of the OpenShift Container Platform y-stream and z-stream updates, and day-two operations on y-streams and z-streams.

16.2. About managed policies used with Topology Aware Lifecycle Manager

The Topology Aware Lifecycle Manager (TALM) uses RHACM policies for cluster updates.

TALM can be used to manage the rollout of any policy CR where the remediationAction field is set to inform. Supported use cases include the following:

Manual user creation of policy CRs
Automatically generated policies from the PolicyGenTemplate custom resource definition (CRD)

For policies that update an Operator subscription with manual approval, TALM provides additional functionality that approves the installation of the updated Operator.

For more information about managed policies, see Policy Overview in the RHACM documentation.

For more information about the PolicyGenTemplate CRD, see the "About the PolicyGenTemplate CRD" section in "Configuring managed clusters with policies and PolicyGenTemplate resources".

16.3. Installing the Topology Aware Lifecycle Manager by using the web console

You can use the OpenShift Container Platform web console to install the Topology Aware Lifecycle Manager.

Prerequisites

Install the latest version of the RHACM Operator.
Set up a hub cluster with disconnected regitry.
Log in as a user with cluster-admin privileges.

Procedure

In the OpenShift Container Platform web console, navigate to Operators → OperatorHub.
Search for the Topology Aware Lifecycle Manager from the list of available Operators, and then click Install.
Keep the default selection of Installation mode ["All namespaces on the cluster (default)"] and Installed Namespace ("openshift-operators") to ensure that the Operator is installed properly.
Click Install.

Verification

To confirm that the installation is successful:

Navigate to the Operators → Installed Operators page.
Check that the Operator is installed in the All Namespaces namespace and its status is Succeeded.

If the Operator is not installed successfully:

Navigate to the Operators → Installed Operators page and inspect the Status column for any errors or failures.
Navigate to the Workloads → Pods page and check the logs in any containers in the cluster-group-upgrades-controller-manager pod that are reporting issues.

16.4. Installing the Topology Aware Lifecycle Manager by using the CLI

You can use the OpenShift CLI (oc) to install the Topology Aware Lifecycle Manager (TALM).

Prerequisites

Install the OpenShift CLI (oc).
Install the latest version of the RHACM Operator.
Set up a hub cluster with disconnected registry.
Log in as a user with cluster-admin privileges.

Procedure

Create a Subscription CR:

Define the Subscription CR and save the YAML file, for example, talm-subscription.yaml:

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: openshift-topology-aware-lifecycle-manager-subscription
  namespace: openshift-operators
spec:
  channel: "stable"
  name: topology-aware-lifecycle-manager
  source: redhat-operators
  sourceNamespace: openshift-marketplace

Create the Subscription CR by running the following command:
```
$ oc create -f talm-subscription.yaml
```

Verification

Verify that the installation succeeded by inspecting the CSV resource:

$ oc get csv -n openshift-operators

Example output

NAME                                                      DISPLAY                            VERSION               REPLACES   PHASE
topology-aware-lifecycle-manager.4.10.0-202206301927      Topology Aware Lifecycle Manager   4.10.0-202206301927              Succeeded

Verify that the TALM is up and running:

$ oc get deploy -n openshift-operators

Example output

NAMESPACE                                          NAME                                             READY   UP-TO-DATE   AVAILABLE   AGE
openshift-operators                                cluster-group-upgrades-controller-manager        1/1     1            1           14s

16.5. About the ClusterGroupUpgrade CR

The Topology Aware Lifecycle Manager (TALM) builds the remediation plan from the ClusterGroupUpgrade CR for a group of clusters. You can define the following specifications in a ClusterGroupUpgrade CR:

Clusters in the group
Blocking ClusterGroupUpgrade CRs
Applicable list of managed policies
Number of concurrent updates
Applicable canary updates
Actions to perform before and after the update
Update timing

As TALM works through remediation of the policies to the specified clusters, the ClusterGroupUpgrade CR can have the following states:

UpgradeNotStarted
UpgradeCannotStart
UpgradeNotComplete
UpgradeTimedOut
UpgradeCompleted
PrecachingRequired

Note

After TALM completes a cluster update, the cluster does not update again under the control of the same ClusterGroupUpgrade CR. You must create a new ClusterGroupUpgrade CR in the following cases:

When you need to update the cluster again
When the cluster changes to non-compliant with the inform policy after being updated

16.5.1. The UpgradeNotStarted state

The initial state of the ClusterGroupUpgrade CR is UpgradeNotStarted.

TALM builds a remediation plan based on the following fields:

The clusterSelector field specifies the labels of the clusters that you want to update.
The clusters field specifies a list of clusters to update.
The canaries field specifies the clusters for canary updates.
The maxConcurrency field specifies the number of clusters to update in a batch.

You can use the clusters and the clusterSelector fields together to create a combined list of clusters.

The remediation plan starts with the clusters listed in the canaries field. Each canary cluster forms a single-cluster batch.

Note

Any failures during the update of a canary cluster stops the update process.

The ClusterGroupUpgrade CR transitions to the UpgradeNotCompleted state after the remediation plan is successfully created and after the enable field is set to true. At this point, TALM starts to update the non-compliant clusters with the specified managed policies.

Note

You can only make changes to the spec fields if the ClusterGroupUpgrade CR is either in the UpgradeNotStarted or the UpgradeCannotStart state.

Sample ClusterGroupUpgrade CR in the UpgradeNotStarted state

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-upgrade-complete
  namespace: default
spec:
  clusters: 1
  - spoke1
  enable: false
  managedPolicies: 2
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  remediationStrategy: 3
    canaries: 4
      - spoke1
    maxConcurrency: 1 5
    timeout: 240
status: 6
  conditions:
  - message: The ClusterGroupUpgrade CR is not enabled
    reason: UpgradeNotStarted
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-upgrade-complete-policy1-common-cluster-version-policy
  - cgu-upgrade-complete-policy2-common-pao-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  placementBindings:
  - cgu-upgrade-complete-policy1-common-cluster-version-policy
  - cgu-upgrade-complete-policy2-common-pao-sub-policy
  placementRules:
  - cgu-upgrade-complete-policy1-common-cluster-version-policy
  - cgu-upgrade-complete-policy2-common-pao-sub-policy
  remediationPlan:
  - - spoke1

1: Defines the list of clusters to update.
2: Lists the user-defined set of policies to remediate.
3: Defines the specifics of the cluster updates.
4: Defines the clusters for canary updates.
5: Defines the maximum number of concurrent updates in a batch. The number of remediation batches is the number of canary clusters, plus the number of clusters, except the canary clusters, divided by the maxConcurrency value. The clusters that are already compliant with all the managed policies are excluded from the remediation plan.
6: Displays information about the status of the updates.

16.5.2. The UpgradeCannotStart state

In the UpgradeCannotStart state, the update cannot start because of the following reasons:

Blocking CRs are missing from the system
Blocking CRs have not yet finished

16.5.3. The UpgradeNotCompleted state

In the UpgradeNotCompleted state, TALM enforces the policies following the remediation plan defined in the UpgradeNotStarted state.

Enforcing the policies for subsequent batches starts immediately after all the clusters of the current batch are compliant with all the managed policies. If the batch times out, TALM moves on to the next batch. The timeout value of a batch is the spec.timeout field divided by the number of batches in the remediation plan.

Note

The managed policies apply in the order that they are listed in the managedPolicies field in the ClusterGroupUpgrade CR. One managed policy is applied to the specified clusters at a time. After the specified clusters comply with the current policy, the next managed policy is applied to the next non-compliant cluster.

Sample ClusterGroupUpgrade CR in the UpgradeNotCompleted state

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-upgrade-complete
  namespace: default
spec:
  clusters:
  - spoke1
  enable: true 1
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240
status: 2
  conditions:
  - message: The ClusterGroupUpgrade CR has upgrade policies that are still non compliant
    reason: UpgradeNotCompleted
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-upgrade-complete-policy1-common-cluster-version-policy
  - cgu-upgrade-complete-policy2-common-pao-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  placementBindings:
  - cgu-upgrade-complete-policy1-common-cluster-version-policy
  - cgu-upgrade-complete-policy2-common-pao-sub-policy
  placementRules:
  - cgu-upgrade-complete-policy1-common-cluster-version-policy
  - cgu-upgrade-complete-policy2-common-pao-sub-policy
  remediationPlan:
  - - spoke1
  status:
    currentBatch: 1
    remediationPlanForBatch: 3
      spoke1: 0

1: The update starts when the value of the spec.enable field is true.
2: The status fields change accordingly when the update begins.
3: Lists the clusters in the batch and the index of the policy that is being currently applied to each cluster. The index of the policies starts with 0 and the index follows the order of the status.managedPoliciesForUpgrade list.

16.5.4. The UpgradeTimedOut state

In the UpgradeTimedOut state, TALM checks every hour if all the policies for the ClusterGroupUpgrade CR are compliant. The checks continue until the ClusterGroupUpgrade CR is deleted or the updates are completed. The periodic checks allow the updates to complete if they get prolonged due to network, CPU, or other issues.

TALM transitions to the UpgradeTimedOut state in two cases:

When the current batch contains canary updates and the cluster in the batch does not comply with all the managed policies within the batch timeout.
When the clusters do not comply with the managed policies within the timeout value specified in the remediationStrategy field.

If the policies are compliant, TALM transitions to the UpgradeCompleted state.

16.5.5. The UpgradeCompleted state

In the UpgradeCompleted state, the cluster updates are complete.

Sample ClusterGroupUpgrade CR in the UpgradeCompleted state

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-upgrade-complete
  namespace: default
spec:
  actions:
    afterCompletion:
      deleteObjects: true 1
  clusters:
  - spoke1
  enable: true
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240
status: 2
  conditions:
  - message: The ClusterGroupUpgrade CR has all clusters compliant with all the managed policies
    reason: UpgradeCompleted
    status: "True"
    type: Ready
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  remediationPlan:
  - - spoke1
  status:
    remediationPlanForBatch:
      spoke1: -2 3

1: The value of spec.action.afterCompletion.deleteObjects field is true by default. After the update is completed, TALM deletes the underlying RHACM objects that were created during the update. This option is to prevent the RHACM hub from continuously checking for compliance after a successful update.
2: The status fields show that the updates completed successfully.
3: Displays that all the policies are applied to the cluster.

<discreet><title>The PrecachingRequired state</title>

In the PrecachingRequired state, the clusters need to have images pre-cached before the update can start. For more information about pre-caching, see the "Using the container image pre-cache feature" section.

</discreet>

16.5.6. Blocking ClusterGroupUpgrade CRs

You can create multiple ClusterGroupUpgrade CRs and control their order of application.

For example, if you create ClusterGroupUpgrade CR C that blocks the start of ClusterGroupUpgrade CR A, then ClusterGroupUpgrade CR A cannot start until the status of ClusterGroupUpgrade CR C becomes UpgradeComplete.

One ClusterGroupUpgrade CR can have multiple blocking CRs. In this case, all the blocking CRs must complete before the upgrade for the current CR can start.

Prerequisites

Install the Topology Aware Lifecycle Manager (TALM).
Provision one or more managed clusters.
Log in as a user with cluster-admin privileges.
Create RHACM policies in the hub cluster.

Procedure

Save the content of the ClusterGroupUpgrade CRs in the cgu-a.yaml, cgu-b.yaml, and cgu-c.yaml files.

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-a
  namespace: default
spec:
  blockingCRs: 1
  - name: cgu-c
    namespace: default
  clusters:
  - spoke1
  - spoke2
  - spoke3
  enable: false
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  remediationStrategy:
    canaries:
    - spoke1
    maxConcurrency: 2
    timeout: 240
status:
  conditions:
  - message: The ClusterGroupUpgrade CR is not enabled
    reason: UpgradeNotStarted
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-a-policy1-common-cluster-version-policy
  - cgu-a-policy2-common-pao-sub-policy
  - cgu-a-policy3-common-ptp-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  - name: policy3-common-ptp-sub-policy
    namespace: default
  placementBindings:
  - cgu-a-policy1-common-cluster-version-policy
  - cgu-a-policy2-common-pao-sub-policy
  - cgu-a-policy3-common-ptp-sub-policy
  placementRules:
  - cgu-a-policy1-common-cluster-version-policy
  - cgu-a-policy2-common-pao-sub-policy
  - cgu-a-policy3-common-ptp-sub-policy
  remediationPlan:
  - - spoke1
  - - spoke2

1: Defines the blocking CRs. The cgu-a update cannot start until cgu-c is complete.

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-b
  namespace: default
spec:
  blockingCRs: 1
  - name: cgu-a
    namespace: default
  clusters:
  - spoke4
  - spoke5
  enable: false
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  - policy4-common-sriov-sub-policy
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240
status:
  conditions:
  - message: The ClusterGroupUpgrade CR is not enabled
    reason: UpgradeNotStarted
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-b-policy1-common-cluster-version-policy
  - cgu-b-policy2-common-pao-sub-policy
  - cgu-b-policy3-common-ptp-sub-policy
  - cgu-b-policy4-common-sriov-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  - name: policy3-common-ptp-sub-policy
    namespace: default
  - name: policy4-common-sriov-sub-policy
    namespace: default
  placementBindings:
  - cgu-b-policy1-common-cluster-version-policy
  - cgu-b-policy2-common-pao-sub-policy
  - cgu-b-policy3-common-ptp-sub-policy
  - cgu-b-policy4-common-sriov-sub-policy
  placementRules:
  - cgu-b-policy1-common-cluster-version-policy
  - cgu-b-policy2-common-pao-sub-policy
  - cgu-b-policy3-common-ptp-sub-policy
  - cgu-b-policy4-common-sriov-sub-policy
  remediationPlan:
  - - spoke4
  - - spoke5
  status: {}

1: The cgu-b update cannot start until cgu-a is complete.

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-c
  namespace: default
spec: 1
  clusters:
  - spoke6
  enable: false
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  - policy4-common-sriov-sub-policy
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240
status:
  conditions:
  - message: The ClusterGroupUpgrade CR is not enabled
    reason: UpgradeNotStarted
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-c-policy1-common-cluster-version-policy
  - cgu-c-policy4-common-sriov-sub-policy
  managedPoliciesCompliantBeforeUpgrade:
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy4-common-sriov-sub-policy
    namespace: default
  placementBindings:
  - cgu-c-policy1-common-cluster-version-policy
  - cgu-c-policy4-common-sriov-sub-policy
  placementRules:
  - cgu-c-policy1-common-cluster-version-policy
  - cgu-c-policy4-common-sriov-sub-policy
  remediationPlan:
  - - spoke6
  status: {}

1: The cgu-c update does not have any blocking CRs. TALM starts the cgu-c update when the enable field is set to true.

Create the ClusterGroupUpgrade CRs by running the following command for each relevant CR:
```
$ oc apply -f <name>.yaml
```

Start the update process by running the following command for each relevant CR:

$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/<name> \
--type merge -p '{"spec":{"enable":true}}'

The following examples show ClusterGroupUpgrade CRs where the enable field is set to true:

Example for cgu-a with blocking CRs

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-a
  namespace: default
spec:
  blockingCRs:
  - name: cgu-c
    namespace: default
  clusters:
  - spoke1
  - spoke2
  - spoke3
  enable: true
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  remediationStrategy:
    canaries:
    - spoke1
    maxConcurrency: 2
    timeout: 240
status:
  conditions:
  - message: 'The ClusterGroupUpgrade CR is blocked by other CRs that have not yet
      completed: [cgu-c]' 1
    reason: UpgradeCannotStart
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-a-policy1-common-cluster-version-policy
  - cgu-a-policy2-common-pao-sub-policy
  - cgu-a-policy3-common-ptp-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  - name: policy3-common-ptp-sub-policy
    namespace: default
  placementBindings:
  - cgu-a-policy1-common-cluster-version-policy
  - cgu-a-policy2-common-pao-sub-policy
  - cgu-a-policy3-common-ptp-sub-policy
  placementRules:
  - cgu-a-policy1-common-cluster-version-policy
  - cgu-a-policy2-common-pao-sub-policy
  - cgu-a-policy3-common-ptp-sub-policy
  remediationPlan:
  - - spoke1
  - - spoke2
  status: {}

1: Shows the list of blocking CRs.

Example for cgu-b with blocking CRs

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-b
  namespace: default
spec:
  blockingCRs:
  - name: cgu-a
    namespace: default
  clusters:
  - spoke4
  - spoke5
  enable: true
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  - policy4-common-sriov-sub-policy
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240
status:
  conditions:
  - message: 'The ClusterGroupUpgrade CR is blocked by other CRs that have not yet
      completed: [cgu-a]' 1
    reason: UpgradeCannotStart
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-b-policy1-common-cluster-version-policy
  - cgu-b-policy2-common-pao-sub-policy
  - cgu-b-policy3-common-ptp-sub-policy
  - cgu-b-policy4-common-sriov-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy2-common-pao-sub-policy
    namespace: default
  - name: policy3-common-ptp-sub-policy
    namespace: default
  - name: policy4-common-sriov-sub-policy
    namespace: default
  placementBindings:
  - cgu-b-policy1-common-cluster-version-policy
  - cgu-b-policy2-common-pao-sub-policy
  - cgu-b-policy3-common-ptp-sub-policy
  - cgu-b-policy4-common-sriov-sub-policy
  placementRules:
  - cgu-b-policy1-common-cluster-version-policy
  - cgu-b-policy2-common-pao-sub-policy
  - cgu-b-policy3-common-ptp-sub-policy
  - cgu-b-policy4-common-sriov-sub-policy
  remediationPlan:
  - - spoke4
  - - spoke5
  status: {}

1: Shows the list of blocking CRs.

Example for cgu-c with blocking CRs

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-c
  namespace: default
spec:
  clusters:
  - spoke6
  enable: true
  managedPolicies:
  - policy1-common-cluster-version-policy
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  - policy4-common-sriov-sub-policy
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240
status:
  conditions:
  - message: The ClusterGroupUpgrade CR has upgrade policies that are still non compliant 1
    reason: UpgradeNotCompleted
    status: "False"
    type: Ready
  copiedPolicies:
  - cgu-c-policy1-common-cluster-version-policy
  - cgu-c-policy4-common-sriov-sub-policy
  managedPoliciesCompliantBeforeUpgrade:
  - policy2-common-pao-sub-policy
  - policy3-common-ptp-sub-policy
  managedPoliciesForUpgrade:
  - name: policy1-common-cluster-version-policy
    namespace: default
  - name: policy4-common-sriov-sub-policy
    namespace: default
  placementBindings:
  - cgu-c-policy1-common-cluster-version-policy
  - cgu-c-policy4-common-sriov-sub-policy
  placementRules:
  - cgu-c-policy1-common-cluster-version-policy
  - cgu-c-policy4-common-sriov-sub-policy
  remediationPlan:
  - - spoke6
  status:
    currentBatch: 1
    remediationPlanForBatch:
      spoke6: 0

1: The cgu-c update does not have any blocking CRs.

16.6. Update policies on managed clusters

The Topology Aware Lifecycle Manager (TALM) remediates a set of inform policies for the clusters specified in the ClusterGroupUpgrade CR. TALM remediates inform policies by making enforce copies of the managed RHACM policies. Each copied policy has its own corresponding RHACM placement rule and RHACM placement binding.

One by one, TALM adds each cluster from the current batch to the placement rule that corresponds with the applicable managed policy. If a cluster is already compliant with a policy, TALM skips applying that policy on the compliant cluster. TALM then moves on to applying the next policy to the non-compliant cluster. After TALM completes the updates in a batch, all clusters are removed from the placement rules associated with the copied policies. Then, the update of the next batch starts.

If a spoke cluster does not report any compliant state to RHACM, the managed policies on the hub cluster can be missing status information that TALM needs. TALM handles these cases in the following ways:

If a policy’s status.compliant field is missing, TALM ignores the policy and adds a log entry. Then, TALM continues looking at the policy’s status.status field.
If a policy’s status.status is missing, TALM produces an error.
If a cluster’s compliance status is missing in the policy’s status.status field, TALM considers that cluster to be non-compliant with that policy.

For more information about RHACM policies, see Policy overview.

Additional resources

For more information about the PolicyGenTemplate CRD, see About the PolicyGenTemplate CRD.

16.6.1. Applying update policies to managed clusters

You can update your managed clusters by applying your policies.

Prerequisites

Install the Topology Aware Lifecycle Manager (TALM).
Provision one or more managed clusters.
Log in as a user with cluster-admin privileges.
Create RHACM policies in the hub cluster.

Procedure

Save the contents of the ClusterGroupUpgrade CR in the cgu-1.yaml file.

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-1
  namespace: default
spec:
  managedPolicies: 1
    - policy1-common-cluster-version-policy
    - policy2-common-pao-sub-policy
    - policy3-common-ptp-sub-policy
    - policy4-common-sriov-sub-policy
  enable: false
  clusters: 2
  - spoke1
  - spoke2
  - spoke5
  - spoke6
  remediationStrategy:
    maxConcurrency: 2 3
    timeout: 240 4

1: The name of the policies to apply.
2: The list of clusters to update.
3: The maxConcurrency field signifies the number of clusters updated at the same time.
4: The update timeout in minutes.

Create the ClusterGroupUpgrade CR by running the following command:

$ oc create -f cgu-1.yaml

Check if the ClusterGroupUpgrade CR was created in the hub cluster by running the following command:
```
$ oc get cgu --all-namespaces
```
Example output
```
NAMESPACE   NAME      AGE
default     cgu-1     8m55s
```

Check the status of the update by running the following command:

$ oc get cgu -n default cgu-1 -ojsonpath='{.status}' | jq

Example output

{
  "computedMaxConcurrency": 2,
  "conditions": [
    {
      "lastTransitionTime": "2022-02-25T15:34:07Z",
      "message": "The ClusterGroupUpgrade CR is not enabled", 1
      "reason": "UpgradeNotStarted",
      "status": "False",
      "type": "Ready"
    }
  ],
  "copiedPolicies": [
    "cgu-policy1-common-cluster-version-policy",
    "cgu-policy2-common-pao-sub-policy",
    "cgu-policy3-common-ptp-sub-policy",
    "cgu-policy4-common-sriov-sub-policy"
  ],
  "managedPoliciesContent": {
    "policy1-common-cluster-version-policy": "null",
    "policy2-common-pao-sub-policy": "[{\"kind\":\"Subscription\",\"name\":\"performance-addon-operator\",\"namespace\":\"openshift-performance-addon-operator\"}]",
    "policy3-common-ptp-sub-policy": "[{\"kind\":\"Subscription\",\"name\":\"ptp-operator-subscription\",\"namespace\":\"openshift-ptp\"}]",
    "policy4-common-sriov-sub-policy": "[{\"kind\":\"Subscription\",\"name\":\"sriov-network-operator-subscription\",\"namespace\":\"openshift-sriov-network-operator\"}]"
  },
  "managedPoliciesForUpgrade": [
    {
      "name": "policy1-common-cluster-version-policy",
      "namespace": "default"
    },
    {
      "name": "policy2-common-pao-sub-policy",
      "namespace": "default"
    },
    {
      "name": "policy3-common-ptp-sub-policy",
      "namespace": "default"
    },
    {
      "name": "policy4-common-sriov-sub-policy",
      "namespace": "default"
    }
  ],
  "managedPoliciesNs": {
    "policy1-common-cluster-version-policy": "default",
    "policy2-common-pao-sub-policy": "default",
    "policy3-common-ptp-sub-policy": "default",
    "policy4-common-sriov-sub-policy": "default"
  },
  "placementBindings": [
    "cgu-policy1-common-cluster-version-policy",
    "cgu-policy2-common-pao-sub-policy",
    "cgu-policy3-common-ptp-sub-policy",
    "cgu-policy4-common-sriov-sub-policy"
  ],
  "placementRules": [
    "cgu-policy1-common-cluster-version-policy",
    "cgu-policy2-common-pao-sub-policy",
    "cgu-policy3-common-ptp-sub-policy",
    "cgu-policy4-common-sriov-sub-policy"
  ],
  "precaching": {
    "spec": {}
  },
  "remediationPlan": [
    [
      "spoke1",
      "spoke2"
    ],
    [
      "spoke5",
      "spoke6"
    ]
  ],
  "status": {}
}

1: The spec.enable field in the ClusterGroupUpgrade CR is set to false.

Check the status of the policies by running the following command:

$ oc get policies -A

Example output

NAMESPACE   NAME                                                 REMEDIATION ACTION   COMPLIANCE STATE   AGE
default     cgu-policy1-common-cluster-version-policy            enforce                                 17m 1
default     cgu-policy2-common-pao-sub-policy                    enforce                                 17m
default     cgu-policy3-common-ptp-sub-policy                    enforce                                 17m
default     cgu-policy4-common-sriov-sub-policy                  enforce                                 17m
default     policy1-common-cluster-version-policy                inform               NonCompliant       15h
default     policy2-common-pao-sub-policy                        inform               NonCompliant       15h
default     policy3-common-ptp-sub-policy                        inform               NonCompliant       18m
default     policy4-common-sriov-sub-policy                      inform               NonCompliant       18m

1: The spec.remediationAction field of policies currently applied on the clusters is set to enforce. The managed policies in inform mode from the ClusterGroupUpgrade CR remain in inform mode during the update.

Change the value of the spec.enable field to true by running the following command:

$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-1 \
--patch '{"spec":{"enable":true}}' --type=merge

Verification

Check the status of the update again by running the following command:

$ oc get cgu -n default cgu-1 -ojsonpath='{.status}' | jq

Example output

{
  "computedMaxConcurrency": 2,
  "conditions": [ 1
    {
      "lastTransitionTime": "2022-02-25T15:34:07Z",
      "message": "The ClusterGroupUpgrade CR has upgrade policies that are still non compliant",
      "reason": "UpgradeNotCompleted",
      "status": "False",
      "type": "Ready"
    }
  ],
  "copiedPolicies": [
    "cgu-policy1-common-cluster-version-policy",
    "cgu-policy2-common-pao-sub-policy",
    "cgu-policy3-common-ptp-sub-policy",
    "cgu-policy4-common-sriov-sub-policy"
  ],
  "managedPoliciesContent": {
    "policy1-common-cluster-version-policy": "null",
    "policy2-common-pao-sub-policy": "[{\"kind\":\"Subscription\",\"name\":\"performance-addon-operator\",\"namespace\":\"openshift-performance-addon-operator\"}]",
    "policy3-common-ptp-sub-policy": "[{\"kind\":\"Subscription\",\"name\":\"ptp-operator-subscription\",\"namespace\":\"openshift-ptp\"}]",
    "policy4-common-sriov-sub-policy": "[{\"kind\":\"Subscription\",\"name\":\"sriov-network-operator-subscription\",\"namespace\":\"openshift-sriov-network-operator\"}]"
  },
  "managedPoliciesForUpgrade": [
    {
      "name": "policy1-common-cluster-version-policy",
      "namespace": "default"
    },
    {
      "name": "policy2-common-pao-sub-policy",
      "namespace": "default"
    },
    {
      "name": "policy3-common-ptp-sub-policy",
      "namespace": "default"
    },
    {
      "name": "policy4-common-sriov-sub-policy",
      "namespace": "default"
    }
  ],
  "managedPoliciesNs": {
    "policy1-common-cluster-version-policy": "default",
    "policy2-common-pao-sub-policy": "default",
    "policy3-common-ptp-sub-policy": "default",
    "policy4-common-sriov-sub-policy": "default"
  },
  "placementBindings": [
    "cgu-policy1-common-cluster-version-policy",
    "cgu-policy2-common-pao-sub-policy",
    "cgu-policy3-common-ptp-sub-policy",
    "cgu-policy4-common-sriov-sub-policy"
  ],
  "placementRules": [
    "cgu-policy1-common-cluster-version-policy",
    "cgu-policy2-common-pao-sub-policy",
    "cgu-policy3-common-ptp-sub-policy",
    "cgu-policy4-common-sriov-sub-policy"
  ],
  "precaching": {
    "spec": {}
  },
  "remediationPlan": [
    [
      "spoke1",
      "spoke2"
    ],
    [
      "spoke5",
      "spoke6"
    ]
  ],
  "status": {
    "currentBatch": 1,
    "currentBatchStartedAt": "2022-02-25T15:54:16Z",
    "remediationPlanForBatch": {
      "spoke1": 0,
      "spoke2": 1
    },
    "startedAt": "2022-02-25T15:54:16Z"
  }
}

1: Reflects the update progress of the current batch. Run this command again to receive updated information about the progress.

If the policies include Operator subscriptions, you can check the installation progress directly on the single-node cluster.
1. Export the KUBECONFIG file of the single-node cluster you want to check the installation progress for by running the following command:
```
$ export KUBECONFIG=<cluster_kubeconfig_absolute_path>
```
2. Check all the subscriptions present on the single-node cluster and look for the one in the policy you are trying to install through the ClusterGroupUpgrade CR by running the following command:
```
$ oc get subs -A | grep -i <subscription_name>
```
  Example output for cluster-logging policy
```
NAMESPACE                              NAME                         PACKAGE                      SOURCE             CHANNEL
openshift-logging                      cluster-logging              cluster-logging              redhat-operators   stable
```
If one of the managed policies includes a ClusterVersion CR, check the status of platform updates in the current batch by running the following command against the spoke cluster:
```
$ oc get clusterversion
```
Example output
```
NAME      VERSION   AVAILABLE   PROGRESSING   SINCE   STATUS
version   4.9.5     True        True          43s     Working towards 4.9.7: 71 of 735 done (9% complete)
```

Check the Operator subscription by running the following command:

$ oc get subs -n <operator-namespace> <operator-subscription> -ojsonpath="{.status}"

Check the install plans present on the single-node cluster that is associated with the desired subscription by running the following command:
```
$ oc get installplan -n <subscription_namespace>
```
Example output for cluster-logging Operator
```
NAMESPACE                              NAME            CSV                                 APPROVAL   APPROVED
openshift-logging                      install-6khtw   cluster-logging.5.3.3-4             Manual     true 1
```
1
The install plans have their Approval field set to Manual and their Approved field changes from false to true after TALM approves the install plan.
Note
When TALM is remediating a policy containing a subscription, it automatically approves any install plans attached to that subscription. Where multiple install plans are needed to get the operator to the latest known version, TALM might approve multiple install plans, upgrading through one or more intermediate versions to get to the final version.
Check if the cluster service version for the Operator of the policy that the ClusterGroupUpgrade is installing reached the Succeeded phase by running the following command:
```
$ oc get csv -n <operator_namespace>
```
Example output for OpenShift Logging Operator
```
NAME                    DISPLAY                     VERSION   REPLACES   PHASE
cluster-logging.5.4.2   Red Hat OpenShift Logging   5.4.2                Succeeded
```

16.7. Using the container image pre-cache feature

Clusters might have limited bandwidth to access the container image registry, which can cause a timeout before the updates are completed.

Note

The time of the update is not set by TALM. You can apply the ClusterGroupUpgrade CR at the beginning of the update by manual application or by external automation.

The container image pre-caching starts when the preCaching field is set to true in the ClusterGroupUpgrade CR. After a successful pre-caching process, you can start remediating policies. The remediation actions start when the enable field is set to true.

The pre-caching process can be in the following statuses:

PrecacheNotStarted

This is the initial state all clusters are automatically assigned to on the first reconciliation pass of the ClusterGroupUpgrade CR.

In this state, TALM deletes any pre-caching namespace and hub view resources of spoke clusters that remain from previous incomplete updates. TALM then creates a new ManagedClusterView resource for the spoke pre-caching namespace to verify its deletion in the PrecachePreparing state.

PrecachePreparing

Cleaning up any remaining resources from previous incomplete updates is in progress.

PrecacheStarting

Pre-caching job prerequisites and the job are created.

PrecacheActive

The job is in "Active" state.

PrecacheSucceeded

The pre-cache job has succeeded.

PrecacheTimeout

The artifact pre-caching has been partially done.

PrecacheUnrecoverableError

The job ends with a non-zero exit code.

16.7.1. Creating a ClusterGroupUpgrade CR with pre-caching

The pre-cache feature allows the required container images to be present on the spoke cluster before the update starts.

Prerequisites

Install the Topology Aware Lifecycle Manager (TALM).
Provision one or more managed clusters.
Log in as a user with cluster-admin privileges.

Procedure

Save the contents of the ClusterGroupUpgrade CR with the preCaching field set to true in the clustergroupupgrades-group-du.yaml file:

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: du-upgrade-4918
  namespace: ztp-group-du-sno
spec:
  preCaching: true 1
  clusters:
  - cnfdb1
  - cnfdb2
  enable: false
  managedPolicies:
  - du-upgrade-platform-upgrade
  remediationStrategy:
    maxConcurrency: 2
    timeout: 240

1: The preCaching field is set to true, which enables TALM to pull the container images before starting the update.

When you want to start the update, apply the ClusterGroupUpgrade CR by running the following command:
```
$ oc apply -f clustergroupupgrades-group-du.yaml
```

Verification

Check if the ClusterGroupUpgrade CR exists in the hub cluster by running the following command:
```
$ oc get cgu -A
```
Example output
```
NAMESPACE          NAME              AGE
ztp-group-du-sno   du-upgrade-4918   10s 1
```
1
The CR is created.

Check the status of the pre-caching task by running the following command:

$ oc get cgu -n ztp-group-du-sno du-upgrade-4918 -o jsonpath='{.status}'

Example output

{
  "conditions": [
    {
      "lastTransitionTime": "2022-01-27T19:07:24Z",
      "message": "Precaching is not completed (required)", 1
      "reason": "PrecachingRequired",
      "status": "False",
      "type": "Ready"
    },
    {
      "lastTransitionTime": "2022-01-27T19:07:24Z",
      "message": "Precaching is required and not done",
      "reason": "PrecachingNotDone",
      "status": "False",
      "type": "PrecachingDone"
    },
    {
      "lastTransitionTime": "2022-01-27T19:07:34Z",
      "message": "Pre-caching spec is valid and consistent",
      "reason": "PrecacheSpecIsWellFormed",
      "status": "True",
      "type": "PrecacheSpecValid"
    }
  ],
  "precaching": {
    "clusters": [
      "cnfdb1" 2
    ],
    "spec": {
      "platformImage": "image.example.io"},
    "status": {
      "cnfdb1": "Active"}
    }
}

1: Displays that the update is in progress.
2: Displays the list of identified clusters.

Check the status of the pre-caching job by running the following command on the spoke cluster:

$ oc get jobs,pods -n openshift-talm-pre-cache

Example output

NAME                  COMPLETIONS   DURATION   AGE
job.batch/pre-cache   0/1           3m10s      3m10s

NAME                     READY   STATUS    RESTARTS   AGE
pod/pre-cache--1-9bmlr   1/1     Running   0          3m10s

Check the status of the ClusterGroupUpgrade CR by running the following command:

$ oc get cgu -n ztp-group-du-sno du-upgrade-4918 -o jsonpath='{.status}'

Example output

"conditions": [
    {
      "lastTransitionTime": "2022-01-27T19:30:41Z",
      "message": "The ClusterGroupUpgrade CR has all clusters compliant with all the managed policies",
      "reason": "UpgradeCompleted",
      "status": "True",
      "type": "Ready"
    },
    {
      "lastTransitionTime": "2022-01-27T19:28:57Z",
      "message": "Precaching is completed",
      "reason": "PrecachingCompleted",
      "status": "True",
      "type": "PrecachingDone" 1
    }

1: The pre-cache tasks are done.

16.8. Troubleshooting the Topology Aware Lifecycle Manager

The Topology Aware Lifecycle Manager (TALM) is an OpenShift Container Platform Operator that remediates RHACM policies. When issues occur, use the oc adm must-gather command to gather details and logs and to take steps in debugging the issues.

For more information about related topics, see the following documentation:

Red Hat Advanced Cluster Management for Kubernetes 2.4 Support Matrix
Red Hat Advanced Cluster Management Troubleshooting
The "Troubleshooting Operator issues" section

16.8.1. General troubleshooting

You can determine the cause of the problem by reviewing the following questions:

Is the configuration that you are applying supported?
- Are the RHACM and the OpenShift Container Platform versions compatible?
- Are the TALM and RHACM versions compatible?
Which of the following components is causing the problem?

To ensure that the ClusterGroupUpgrade configuration is functional, you can do the following:

Create the ClusterGroupUpgrade CR with the spec.enable field set to false.
Wait for the status to be updated and go through the troubleshooting questions.
If everything looks as expected, set the spec.enable field to true in the ClusterGroupUpgrade CR.

Warning

After you set the spec.enable field to true in the ClusterUpgradeGroup CR, the update procedure starts and you cannot edit the CR’s spec fields anymore.

16.8.2. Cannot modify the ClusterUpgradeGroup CR

Issue

You cannot edit the ClusterUpgradeGroup CR after enabling the update.

Resolution

Restart the procedure by performing the following steps:

Remove the old ClusterGroupUpgrade CR by running the following command:

$ oc delete cgu -n <ClusterGroupUpgradeCR_namespace> <ClusterGroupUpgradeCR_name>

Check and fix the existing issues with the managed clusters and policies.
1. Ensure that all the clusters are managed clusters and available.
2. Ensure that all the policies exist and have the spec.remediationAction field set to inform.
Create a new ClusterGroupUpgrade CR with the correct configurations.
```
$ oc apply -f <ClusterGroupUpgradeCR_YAML>
```

16.8.3. Managed policies

Checking managed policies on the system

Issue

You want to check if you have the correct managed policies on the system.

Resolution

Run the following command:

$ oc get cgu lab-upgrade -ojsonpath='{.spec.managedPolicies}'

Example output

["group-du-sno-validator-du-validator-policy", "policy2-common-pao-sub-policy", "policy3-common-ptp-sub-policy"]

Checking remediationAction mode

Issue

You want to check if the remediationAction field is set to inform in the spec of the managed policies.

Resolution

Run the following command:

$ oc get policies --all-namespaces

Example output

NAMESPACE   NAME                                                 REMEDIATION ACTION   COMPLIANCE STATE   AGE
default     policy1-common-cluster-version-policy                inform               NonCompliant       5d21h
default     policy2-common-pao-sub-policy                        inform               Compliant          5d21h
default     policy3-common-ptp-sub-policy                        inform               NonCompliant       5d21h
default     policy4-common-sriov-sub-policy                      inform               NonCompliant       5d21h

Checking policy compliance state

Issue

You want to check the compliance state of policies.

Resolution

Run the following command:

$ oc get policies --all-namespaces

Example output

NAMESPACE   NAME                                                 REMEDIATION ACTION   COMPLIANCE STATE   AGE
default     policy1-common-cluster-version-policy                inform               NonCompliant       5d21h
default     policy2-common-pao-sub-policy                        inform               Compliant          5d21h
default     policy3-common-ptp-sub-policy                        inform               NonCompliant       5d21h
default     policy4-common-sriov-sub-policy                      inform               NonCompliant       5d21h

16.8.4. Clusters

Checking if managed clusters are present

Issue

You want to check if the clusters in the ClusterGroupUpgrade CR are managed clusters.

Resolution

Run the following command:

$ oc get managedclusters

Example output

NAME            HUB ACCEPTED   MANAGED CLUSTER URLS                    JOINED   AVAILABLE   AGE
local-cluster   true           https://api.hub.example.com:6443        True     Unknown     13d
spoke1          true           https://api.spoke1.example.com:6443     True     True        13d
spoke3          true           https://api.spoke3.example.com:6443     True     True        27h

Alternatively, check the TALM manager logs:

Get the name of the TALM manager by running the following command:

$ oc get pod -n openshift-operators

Example output

NAME                                                         READY   STATUS    RESTARTS   AGE
cluster-group-upgrades-controller-manager-75bcc7484d-8k8xp   2/2     Running   0          45m

Check the TALM manager logs by running the following command:

$ oc logs -n openshift-operators \
cluster-group-upgrades-controller-manager-75bcc7484d-8k8xp -c manager

Example output

ERROR	controller-runtime.manager.controller.clustergroupupgrade	Reconciler error	{"reconciler group": "ran.openshift.io", "reconciler kind": "ClusterGroupUpgrade", "name": "lab-upgrade", "namespace": "default", "error": "Cluster spoke5555 is not a ManagedCluster"} 1
sigs.k8s.io/controller-runtime/pkg/internal/controller.(*Controller).processNextWorkItem

1: The error message shows that the cluster is not a managed cluster.

Checking if managed clusters are available

Issue

You want to check if the managed clusters specified in the ClusterGroupUpgrade CR are available.

Resolution

Run the following command:

$ oc get managedclusters

Example output

NAME            HUB ACCEPTED   MANAGED CLUSTER URLS                    JOINED   AVAILABLE   AGE
local-cluster   true           https://api.hub.testlab.com:6443        True     Unknown     13d
spoke1          true           https://api.spoke1.testlab.com:6443     True     True        13d 1
spoke3          true           https://api.spoke3.testlab.com:6443     True     True        27h 2

1 2: The value of the AVAILABLE field is True for the managed clusters.

Checking clusterSelector

Issue

You want to check if the clusterSelector field is specified in the ClusterGroupUpgrade CR in at least one of the managed clusters.

Resolution

Run the following command:

$ oc get managedcluster --selector=upgrade=true 1

1: The label for the clusters you want to update is upgrade:true.

Example output

NAME            HUB ACCEPTED   MANAGED CLUSTER URLS                     JOINED    AVAILABLE   AGE
spoke1          true           https://api.spoke1.testlab.com:6443      True     True        13d
spoke3          true           https://api.spoke3.testlab.com:6443      True     True        27h

Checking if canary clusters are present

Issue

You want to check if the canary clusters are present in the list of clusters.

Example ClusterGroupUpgrade CR

spec:
    clusters:
    - spoke1
    - spoke3
    clusterSelector:
    - upgrade2=true
    remediationStrategy:
        canaries:
        - spoke3
        maxConcurrency: 2
        timeout: 240

Resolution

Run the following commands:

$ oc get cgu lab-upgrade -ojsonpath='{.spec.clusters}'

Example output

["spoke1", "spoke3"]

Check if the canary clusters are present in the list of clusters that match clusterSelector labels by running the following command:

$ oc get managedcluster --selector=upgrade=true

Example output

NAME            HUB ACCEPTED   MANAGED CLUSTER URLS   JOINED    AVAILABLE   AGE
spoke1          true           https://api.spoke1.testlab.com:6443   True     True        13d
spoke3          true           https://api.spoke3.testlab.com:6443   True     True        27h

Note

A cluster can be present in spec.clusters and also be matched by the spec.clusterSelecter label.

Checking the pre-caching status on spoke clusters

Check the status of pre-caching by running the following command on the spoke cluster:
```
$ oc get jobs,pods -n openshift-talo-pre-cache
```

16.8.5. Remediation Strategy

Checking if remediationStrategy is present in the ClusterGroupUpgrade CR

Issue

You want to check if the remediationStrategy is present in the ClusterGroupUpgrade CR.

Resolution

Run the following command:

$ oc get cgu lab-upgrade -ojsonpath='{.spec.remediationStrategy}'

Example output

{"maxConcurrency":2, "timeout":240}

Checking if maxConcurrency is specified in the ClusterGroupUpgrade CR

Issue

You want to check if the maxConcurrency is specified in the ClusterGroupUpgrade CR.

Resolution

Run the following command:

$ oc get cgu lab-upgrade -ojsonpath='{.spec.remediationStrategy.maxConcurrency}'

Example output

16.8.6. Topology Aware Lifecycle Manager

Checking condition message and status in the ClusterGroupUpgrade CR

Issue

You want to check the value of the status.conditions field in the ClusterGroupUpgrade CR.

Resolution

Run the following command:

$ oc get cgu lab-upgrade -ojsonpath='{.status.conditions}'

Example output

{"lastTransitionTime":"2022-02-17T22:25:28Z", "message":"The ClusterGroupUpgrade CR has managed policies that are missing:[policyThatDoesntExist]", "reason":"UpgradeCannotStart", "status":"False", "type":"Ready"}

Checking corresponding copied policies

Issue

You want to check if every policy from status.managedPoliciesForUpgrade has a corresponding policy in status.copiedPolicies.

Resolution

Run the following command:

$ oc get cgu lab-upgrade -oyaml

Example output

status:
  …
  copiedPolicies:
  - lab-upgrade-policy3-common-ptp-sub-policy
  managedPoliciesForUpgrade:
  - name: policy3-common-ptp-sub-policy
    namespace: default

Checking if status.remediationPlan was computed

Issue

You want to check if status.remediationPlan is computed.

Resolution

Run the following command:

$ oc get cgu lab-upgrade -ojsonpath='{.status.remediationPlan}'

Example output

[["spoke2", "spoke3"]]

Errors in the TALM manager container

Issue

You want to check the logs of the manager container of TALM.

Resolution

Run the following command:

$ oc logs -n openshift-operators \
cluster-group-upgrades-controller-manager-75bcc7484d-8k8xp -c manager

Example output

ERROR	controller-runtime.manager.controller.clustergroupupgrade	Reconciler error	{"reconciler group": "ran.openshift.io", "reconciler kind": "ClusterGroupUpgrade", "name": "lab-upgrade", "namespace": "default", "error": "Cluster spoke5555 is not a ManagedCluster"} 1
sigs.k8s.io/controller-runtime/pkg/internal/controller.(*Controller).processNextWorkItem

1: Displays the error.

Additional resources

For information about troubleshooting, see OpenShift Container Platform Troubleshooting Operator Issues.
For more information about using Topology Aware Lifecycle Manager in the ZTP workflow, see Updating managed policies with Topology Aware Lifecycle Manager.
For more information about the PolicyGenTemplate CRD, see About the PolicyGenTemplate CRD

Chapter 17. Creating a performance profile

Learn about the Performance Profile Creator (PPC) and how you can use it to create a performance profile.

17.1. About the Performance Profile Creator

The Performance Profile Creator (PPC) is a command-line tool, delivered with the Performance Addon Operator, used to create the performance profile. The tool consumes must-gather data from the cluster and several user-supplied profile arguments. The PPC generates a performance profile that is appropriate for your hardware and topology.

The tool is run by one of the following methods:

Invoking podman
Calling a wrapper script

17.1.1. Gathering data about your cluster using the must-gather command

The Performance Profile Creator (PPC) tool requires must-gather data. As a cluster administrator, run the must-gather command to capture information about your cluster.

Prerequisites

Access to the cluster as a user with the cluster-admin role.
Access to the Performance Addon Operator image.
The OpenShift CLI (oc) installed.

Procedure

Optional: Verify that a matching machine config pool exists with a label:

$ oc describe mcp/worker-rt

Example output

Name:         worker-rt
Namespace:
Labels:       machineconfiguration.openshift.io/role=worker-rt

If a matching label does not exist add a label for a machine config pool (MCP) that matches with the MCP name:
```
$ oc label mcp <mcp_name> <mcp_name>=""
```
Navigate to the directory where you want to store the must-gather data.
Run must-gather on your cluster:
```
$ oc adm must-gather --image=<PAO_image> --dest-dir=<dir>
```
Note
The must-gather command must be run with the performance-addon-operator-must-gather image. The output can optionally be compressed. Compressed output is required if you are running the Performance Profile Creator wrapper script.
Example
```
$ oc adm must-gather --image=registry.redhat.io/openshift4/performance-addon-operator-must-gather-rhel8:v4.10 --dest-dir=must-gather
```
Create a compressed file from the must-gather directory:
```
$ tar cvaf must-gather.tar.gz must-gather/
```

17.1.2. Running the Performance Profile Creator using `podman`

As a cluster administrator, you can run podman and the Performance Profile Creator to create a performance profile.

Prerequisites

Access to the cluster as a user with the cluster-admin role.
A cluster installed on bare metal hardware.
A node with podman and OpenShift CLI (oc) installed.

Procedure

Check the machine config pool:

$ oc get mcp

Example output

NAME         CONFIG                                                 UPDATED   UPDATING   DEGRADED   MACHINECOUNT   READYMACHINECOUNT   UPDATEDMACHINECOUNT   DEGRADEDMACHINECOUNT   AGE
master       rendered-master-acd1358917e9f98cbdb599aea622d78b       True      False      False      3              3                   3                     0                      22h
worker-cnf   rendered-worker-cnf-1d871ac76e1951d32b2fe92369879826   False     True       False      2              1                   1                     0                      22h

Use Podman to authenticate to registry.redhat.io:

$ podman login registry.redhat.io

Username: <username>
Password: <password>

Optional: Display help for the PPC tool:

$ podman run --entrypoint performance-profile-creator registry.redhat.io/openshift4/performance-addon-rhel8-operator:v4.10 -h

Example output

A tool that automates creation of Performance Profiles

Usage:
  performance-profile-creator [flags]

Flags:
      --disable-ht                        Disable Hyperthreading
  -h, --help                              help for performance-profile-creator
      --info string                       Show cluster information; requires --must-gather-dir-path, ignore the other arguments. [Valid values: log, json] (default "log")
      --mcp-name string                   MCP name corresponding to the target machines (required)
      --must-gather-dir-path string       Must gather directory path (default "must-gather")
      --power-consumption-mode string     The power consumption mode.  [Valid values: default, low-latency, ultra-low-latency] (default "default")
      --profile-name string               Name of the performance profile to be created (default "performance")
      --reserved-cpu-count int            Number of reserved CPUs (required)
      --rt-kernel                         Enable Real Time Kernel (required)
      --split-reserved-cpus-across-numa   Split the Reserved CPUs across NUMA nodes
      --topology-manager-policy string    Kubelet Topology Manager Policy of the performance profile to be created. [Valid values: single-numa-node, best-effort, restricted] (default "restricted")
      --user-level-networking             Run with User level Networking(DPDK) enabled

Run the Performance Profile Creator tool in discovery mode:
Note
Discovery mode inspects your cluster using the output from must-gather. The output produced includes information on:
- The NUMA cell partitioning with the allocated CPU ids
- Whether hyperthreading is enabled
Using this information you can set appropriate values for some of the arguments supplied to the Performance Profile Creator tool.
```
$ podman run --entrypoint performance-profile-creator -v /must-gather:/must-gather:z registry.redhat.io/openshift4/performance-addon-rhel8-operator:v4.10 --info log --must-gather-dir-path /must-gather
```
Note
This command uses the performance profile creator as a new entry point to podman. It maps the must-gather data for the host into the container image and invokes the required user-supplied profile arguments to produce the my-performance-profile.yaml file.
The -v option can be the path to either:
- The must-gather output directory
- An existing directory containing the must-gather decompressed tarball
The info option requires a value which specifies the output format. Possible values are log and JSON. The JSON format is reserved for debugging.

Run podman:

$ podman run --entrypoint performance-profile-creator -v /must-gather:/must-gather:z registry.redhat.io/openshift4/performance-addon-rhel8-operator:v4.10 --mcp-name=worker-cnf --reserved-cpu-count=20 --rt-kernel=true --split-reserved-cpus-across-numa=false --topology-manager-policy=single-numa-node --must-gather-dir-path /must-gather  --power-consumption-mode=ultra-low-latency > my-performance-profile.yaml

Note

The Performance Profile Creator arguments are shown in the Performance Profile Creator arguments table. The following arguments are required:

reserved-cpu-count
mcp-name
rt-kernel

The mcp-name argument in this example is set to worker-cnf based on the output of the command oc get mcp. For single-node OpenShift use --mcp-name=master.

Review the created YAML file:

$ cat my-performance-profile.yaml

Example output

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: performance
spec:
  additionalKernelArgs:
  - nmi_watchdog=0
  - audit=0
  - mce=off
  - processor.max_cstate=1
  - intel_idle.max_cstate=0
  - idle=poll
  cpu:
    isolated: 1,3,5,7,9,11,13,15,17,19-39,41,43,45,47,49,51,53,55,57,59-79
    reserved: 0,2,4,6,8,10,12,14,16,18,40,42,44,46,48,50,52,54,56,58
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""
  numa:
    topologyPolicy: single-numa-node
  realTimeKernel:
    enabled: true

Apply the generated profile:
Note
Install the Performance Addon Operator before applying the profile.
```
$ oc apply -f my-performance-profile.yaml
```

17.1.2.1. How to run `podman` to create a performance profile

The following example illustrates how to run podman to create a performance profile with 20 reserved CPUs that are to be split across the NUMA nodes.

Node hardware configuration:

80 CPUs
Hyperthreading enabled
Two NUMA nodes
Even numbered CPUs run on NUMA node 0 and odd numbered CPUs run on NUMA node 1

Run podman to create the performance profile:

$ podman run --entrypoint performance-profile-creator -v /must-gather:/must-gather:z registry.redhat.io/openshift4/performance-addon-rhel8-operator:v4.10 --mcp-name=worker-cnf --reserved-cpu-count=20 --rt-kernel=true --split-reserved-cpus-across-numa=true --must-gather-dir-path /must-gather > my-performance-profile.yaml

The created profile is described in the following YAML:

  apiVersion: performance.openshift.io/v2
  kind: PerformanceProfile
  metadata:
    name: performance
  spec:
    cpu:
      isolated: 10-39,50-79
      reserved: 0-9,40-49
    nodeSelector:
      node-role.kubernetes.io/worker-cnf: ""
    numa:
      topologyPolicy: restricted
    realTimeKernel:
      enabled: true

Note

In this case, 10 CPUs are reserved on NUMA node 0 and 10 are reserved on NUMA node 1.

17.1.3. Running the Performance Profile Creator wrapper script

The performance profile wrapper script simplifies the running of the Performance Profile Creator (PPC) tool. It hides the complexities associated with running podman and specifying the mapping directories and it enables the creation of the performance profile.

Prerequisites

Access to the Performance Addon Operator image.
Access to the must-gather tarball.

Procedure

Create a file on your local machine named, for example, run-perf-profile-creator.sh:
```
$ vi run-perf-profile-creator.sh
```

Paste the following code into the file:

#!/bin/bash

readonly CONTAINER_RUNTIME=${CONTAINER_RUNTIME:-podman}
readonly CURRENT_SCRIPT=$(basename "$0")
readonly CMD="${CONTAINER_RUNTIME} run --entrypoint performance-profile-creator"
readonly IMG_EXISTS_CMD="${CONTAINER_RUNTIME} image exists"
readonly IMG_PULL_CMD="${CONTAINER_RUNTIME} image pull"
readonly MUST_GATHER_VOL="/must-gather"

PAO_IMG="registry.redhat.io/openshift4/performance-addon-rhel8-operator:v4.10"
MG_TARBALL=""
DATA_DIR=""

usage() {
  print "Wrapper usage:"
  print "  ${CURRENT_SCRIPT} [-h] [-p image][-t path] -- [performance-profile-creator flags]"
  print ""
  print "Options:"
  print "   -h                 help for ${CURRENT_SCRIPT}"
  print "   -p                 Performance Addon Operator image"
  print "   -t                 path to a must-gather tarball"

  ${IMG_EXISTS_CMD} "${PAO_IMG}" && ${CMD} "${PAO_IMG}" -h
}

function cleanup {
  [ -d "${DATA_DIR}" ] && rm -rf "${DATA_DIR}"
}
trap cleanup EXIT

exit_error() {
  print "error: $*"
  usage
  exit 1
}

print() {
  echo  "$*" >&2
}

check_requirements() {
  ${IMG_EXISTS_CMD} "${PAO_IMG}" || ${IMG_PULL_CMD} "${PAO_IMG}" || \
      exit_error "Performance Addon Operator image not found"

  [ -n "${MG_TARBALL}" ] || exit_error "Must-gather tarball file path is mandatory"
  [ -f "${MG_TARBALL}" ] || exit_error "Must-gather tarball file not found"

  DATA_DIR=$(mktemp -d -t "${CURRENT_SCRIPT}XXXX") || exit_error "Cannot create the data directory"
  tar -zxf "${MG_TARBALL}" --directory "${DATA_DIR}" || exit_error "Cannot decompress the must-gather tarball"
  chmod a+rx "${DATA_DIR}"

  return 0
}

main() {
  while getopts ':hp:t:' OPT; do
    case "${OPT}" in
      h)
        usage
        exit 0
        ;;
      p)
        PAO_IMG="${OPTARG}"
        ;;
      t)
        MG_TARBALL="${OPTARG}"
        ;;
      ?)
        exit_error "invalid argument: ${OPTARG}"
        ;;
    esac
  done
  shift $((OPTIND - 1))

  check_requirements || exit 1

  ${CMD} -v "${DATA_DIR}:${MUST_GATHER_VOL}:z" "${PAO_IMG}" "$@" --must-gather-dir-path "${MUST_GATHER_VOL}"
  echo "" 1>&2
}

main "$@"

Add execute permissions for everyone on this script:
```
$ chmod a+x run-perf-profile-creator.sh
```

Optional: Display the run-perf-profile-creator.sh command usage:

$ ./run-perf-profile-creator.sh -h

Expected output

Wrapper usage:
  run-perf-profile-creator.sh [-h] [-p image][-t path] -- [performance-profile-creator flags]

Options:
   -h                 help for run-perf-profile-creator.sh
   -p                 Performance Addon Operator image 1
   -t                 path to a must-gather tarball 2

A tool that automates creation of Performance Profiles

   Usage:
     performance-profile-creator [flags]

   Flags:
         --disable-ht                        Disable Hyperthreading
     -h, --help                              help for performance-profile-creator
         --info string                       Show cluster information; requires --must-gather-dir-path, ignore the other arguments. [Valid values: log, json] (default "log")
         --mcp-name string                   MCP name corresponding to the target machines (required)
         --must-gather-dir-path string       Must gather directory path (default "must-gather")
         --power-consumption-mode string     The power consumption mode.  [Valid values: default, low-latency, ultra-low-latency] (default "default")
         --profile-name string               Name of the performance profile to be created (default "performance")
         --reserved-cpu-count int            Number of reserved CPUs (required)
         --rt-kernel                         Enable Real Time Kernel (required)
         --split-reserved-cpus-across-numa   Split the Reserved CPUs across NUMA nodes
         --topology-manager-policy string    Kubelet Topology Manager Policy of the performance profile to be created. [Valid values: single-numa-node, best-effort, restricted] (default "restricted")
         --user-level-networking             Run with User level Networking(DPDK) enabled

Note

There two types of arguments:

Wrapper arguments namely -h, -p and -t
PPC arguments

1: Optional: Specify the Performance Addon Operator image. If not set, the default upstream image is used: registry.redhat.io/openshift4/performance-addon-rhel8-operator:v4.10.
2: -t is a required wrapper script argument and specifies the path to a must-gather tarball.

Run the performance profile creator tool in discovery mode:
Note
Discovery mode inspects your cluster using the output from must-gather. The output produced includes information on:
- The NUMA cell partitioning with the allocated CPU IDs
- Whether hyperthreading is enabled
Using this information you can set appropriate values for some of the arguments supplied to the Performance Profile Creator tool.
```
$ ./run-perf-profile-creator.sh -t /must-gather/must-gather.tar.gz -- --info=log
```
Note
The info option requires a value which specifies the output format. Possible values are log and JSON. The JSON format is reserved for debugging.

Check the machine config pool:

$ oc get mcp

Example output

NAME         CONFIG                                                 UPDATED   UPDATING   DEGRADED   MACHINECOUNT   READYMACHINECOUNT   UPDATEDMACHINECOUNT   DEGRADEDMACHINECOUNT   AGE
master       rendered-master-acd1358917e9f98cbdb599aea622d78b       True      False      False      3              3                   3                     0                      22h
worker-cnf   rendered-worker-cnf-1d871ac76e1951d32b2fe92369879826   False     True       False      2              1                   1                     0                      22h

Create a performance profile:
```
$ ./run-perf-profile-creator.sh -t /must-gather/must-gather.tar.gz -- --mcp-name=worker-cnf --reserved-cpu-count=2 --rt-kernel=true > my-performance-profile.yaml
```
Note
The Performance Profile Creator arguments are shown in the Performance Profile Creator arguments table. The following arguments are required:
- reserved-cpu-count
- mcp-name
- rt-kernel
The mcp-name argument in this example is set to worker-cnf based on the output of the command oc get mcp. For single-node OpenShift use --mcp-name=master.

Review the created YAML file:

$ cat my-performance-profile.yaml

Example output

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: performance
spec:
  cpu:
    isolated: 1-39,41-79
    reserved: 0,40
  nodeSelector:
    node-role.kubernetes.io/worker-cnf: ""
  numa:
    topologyPolicy: restricted
  realTimeKernel:
    enabled: false

Apply the generated profile:
Note
Install the Performance Addon Operator before applying the profile.
```
$ oc apply -f my-performance-profile.yaml
```

17.1.4. Performance Profile Creator arguments

Table 17.1. Performance Profile Creator arguments

Argument	Description
`disable-ht`	Disable hyperthreading. Possible values: `true` or `false`. Default: `false`. Warning If this argument is set to `true` you should not disable hyperthreading in the BIOS. Disabling hyperthreading is accomplished with a kernel command line argument.
`info`	This captures cluster information and is used in discovery mode only. Discovery mode also requires the `must-gather-dir-path` argument. If any other arguments are set they are ignored. Possible values: `log` `JSON` Note These options define the output format with the JSON format being reserved for debugging. Default: `log`.
`mcp-name`	MCP name for example `worker-cnf` corresponding to the target machines. This parameter is required.
`must-gather-dir-path`	Must gather directory path. This parameter is required. When the user runs the tool with the wrapper script `must-gather` is supplied by the script itself and the user must not specify it.
`power-consumption-mode`	The power consumption mode. Possible values: `default` `low-latency` `ultra-low-latency` Default: `default`.
`profile-name`	Name of the performance profile to create. Default: `performance`.
`reserved-cpu-count`	Number of reserved CPUs. This parameter is required. Note This must be a natural number. A value of 0 is not allowed.
`rt-kernel`	Enable real-time kernel. This parameter is required. Possible values: `true` or `false`.
`split-reserved-cpus-across-numa`	Split the reserved CPUs across NUMA nodes. Possible values: `true` or `false`. Default: `false`.
`topology-manager-policy`	Kubelet Topology Manager policy of the performance profile to be created. Possible values: `single-numa-node` `best-effort` `restricted` Default: `restricted`.
`user-level-networking`	Run with user level networking (DPDK) enabled. Possible values: `true` or `false`. Default: `false`.

17.2. Additional resources

For more information about the must-gather tool, see Gathering data about your cluster.

Chapter 18. Workload partitioning on single-node OpenShift

In resource-constrained environments, such as single-node OpenShift deployments, it is advantageous to reserve most of the CPU resources for your own workloads and configure OpenShift Container Platform to run on a fixed number of CPUs within the host. In these environments, management workloads, including the control plane, need to be configured to use fewer resources than they might by default in normal clusters. You can isolate the OpenShift Container Platform services, cluster management workloads, and infrastructure pods to run on a reserved set of CPUs.

When you use workload partitioning, the CPU resources used by OpenShift Container Platform for cluster management are isolated to a partitioned set of CPU resources on a single-node cluster. This partitioning isolates cluster management functions to the defined number of CPUs. All cluster management functions operate solely on that cpuset configuration.

The minimum number of reserved CPUs required for the management partition for a single-node cluster is four CPU Hyper threads (HTs). The set of pods that make up the baseline OpenShift Container Platform installation and a set of typical add-on Operators are annotated for inclusion in the management workload partition. These pods operate normally within the minimum size cpuset configuration. Inclusion of Operators or workloads outside of the set of accepted management pods requires additional CPU HTs to be added to that partition.

Workload partitioning isolates the user workloads away from the platform workloads using the normal scheduling capabilities of Kubernetes to manage the number of pods that can be placed onto those cores, and avoids mixing cluster management workloads and user workloads.

When using workload partitioning, you must install the Performance Addon Operator and apply the performance profile:

Workload partitioning pins the OpenShift Container Platform infrastructure pods to a defined cpuset configuration.
The Performance Addon Operator performance profile pins the systemd services to a defined cpuset configuration.
This cpuset configuration must match.

Workload partitioning introduces a new extended resource of <workload-type>.workload.openshift.io/cores for each defined CPU pool, or workload-type. Kubelet advertises these new resources and CPU requests by pods allocated to the pool are accounted for within the corresponding resource rather than the typical cpu resource. When workload partitioning is enabled, the <workload-type>.workload.openshift.io/cores resource allows access to the CPU capacity of the host, not just the default CPU pool.

18.1. Maximizing CPU allocation with workload partitioning

During single-node OpenShift cluster installation, you must enable workload partitioning. This limits the cores allowed to run platform services, maximizing the CPU core for application payloads.

Note

You can enable workload partitioning only during cluster installation. You cannot disable workload partitioning post-installation. However, you can reconfigure workload partitioning by updating the cpu value that you define in the performance profile, and in the related cpuset value in the MachineConfig custom resource (CR).

The base64-encoded CR that enables workload partitioning contains the CPU set that the management workloads are constrained to. Encode host-specific values for crio.conf and kubelet.conf in base64. This content must be adjusted to match the CPU set that is specified in the cluster performance profile and must be accurate for the number of cores in the cluster host.

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 02-master-workload-partitioning
spec:
  config:
    ignition:
      version: 3.2.0
    storage:
      files:
      - contents:
          source: data:text/plain;charset=utf-8;base64,W2NyaW8ucnVudGltZS53b3JrbG9hZHMubWFuYWdlbWVudF0KYWN0aXZhdGlvbl9hbm5vdGF0aW9uID0gInRhcmdldC53b3JrbG9hZC5vcGVuc2hpZnQuaW8vbWFuYWdlbWVudCIKYW5ub3RhdGlvbl9wcmVmaXggPSAicmVzb3VyY2VzLndvcmtsb2FkLm9wZW5zaGlmdC5pbyIKcmVzb3VyY2VzID0geyAiY3B1c2hhcmVzIiA9IDAsICJjcHVzZXQiID0gIjAtMSw1Mi01MyIgfQo=
        mode: 420
        overwrite: true
        path: /etc/crio/crio.conf.d/01-workload-partitioning
        user:
          name: root
      - contents:
          source: data:text/plain;charset=utf-8;base64,ewogICJtYW5hZ2VtZW50IjogewogICAgImNwdXNldCI6ICIwLTEsNTItNTMiCiAgfQp9Cg==
        mode: 420
        overwrite: true
        path: /etc/kubernetes/openshift-workload-pinning
        user:
          name: root

When configured in the cluster host, the contents of /etc/crio/crio.conf.d/01-workload-partitioning should look like this:
```
[crio.runtime.workloads.management]
activation_annotation = "target.workload.openshift.io/management"
annotation_prefix = "resources.workload.openshift.io"
[crio.runtime.workloads.management.resources]
cpushares = 0
cpuset = "0-1, 52-53" 1
```
1
The cpuset value varies based on the installation.
If Hyper-Threading is enabled, specify both threads for each core. The cpuset value must match the reserved CPUs that you define in the spec.cpu.reserved field in the performance profile.
When configured in the cluster, the contents of /etc/kubernetes/openshift-workload-pinning should look like this:
```
{
  "management": {
    "cpuset": "0-1,52-53" 1
  }
}
```
1
The cpuset must match the cpuset value in /etc/crio/crio.conf.d/01-workload-partitioning.

Chapter 19. Clusters at the network far edge

19.1. Challenges of the network far edge

Edge computing presents complex challenges when managing many sites in geographically displaced locations. Use zero touch provisioning (ZTP) and GitOps to provision and manage sites at the far edge of the network.

19.1.1. Overcoming the challenges of the network far edge

Today, service providers want to deploy their infrastructure at the edge of the network. This presents significant challenges:

How do you handle deployments of many edge sites in parallel?
What happens when you need to deploy sites in disconnected environments?
How do you manage the lifecycle of large fleets of clusters?

Zero touch provisioning (ZTP) and GitOps meets these challenges by allowing you to provision remote edge sites at scale with declarative site definitions and configurations for bare-metal equipment. Template or overlay configurations install OpenShift Container Platform features that are required for CNF workloads. The full lifecycle of installation and upgrades is handled through the ZTP pipeline.

ZTP uses GitOps for infrastructure deployments. With GitOps, you use declarative YAML files and other defined patterns stored in Git repositories. Red Hat Advanced Cluster Management (RHACM) uses your Git repositories to drive the deployment of your infrastructure.

GitOps provides traceability, role-based access control (RBAC), and a single source of truth for the desired state of each site. Scalability issues are addressed by Git methodologies and event driven operations through webhooks.

You start the ZTP workflow by creating declarative site definition and configuration custom resources (CRs) that the ZTP pipeline delivers to the edge nodes.

The following diagram shows how ZTP works within the far edge framework.

19.1.2. Using ZTP to provision clusters at the network far edge

Red Hat Advanced Cluster Management (RHACM) manages clusters in a hub-and-spoke architecture, where a single hub cluster manages many spoke clusters. Hub clusters running RHACM provision and deploy the managed clusters by using zero touch provisioning (ZTP) and the assisted service that is deployed when you install RHACM.

The assisted service handles provisioning of OpenShift Container Platform on single node clusters, three-node clusters, or standard clusters running on bare metal.

A high-level overview of using ZTP to provision and maintain bare-metal hosts with OpenShift Container Platform is as follows:

A hub cluster running RHACM manages an OpenShift image registry that mirrors the OpenShift Container Platform release images. RHACM uses the OpenShift image registry to provision the managed clusters.
You manage the bare-metal hosts in a YAML format inventory file, versioned in a Git repository.
You make the hosts ready for provisioning as managed clusters, and use RHACM and the assisted service to install the bare-metal hosts on site.

Installing and deploying the clusters is a two-stage process, involving an initial installation phase, and a subsequent configuration phase. The following diagram illustrates this workflow:

Using GitOps and ZTP to install and deploy managed clusters

19.1.3. Installing managed clusters with SiteConfig resources and RHACM

GitOps ZTP uses SiteConfig custom resources (CRs) in a Git repository to manage the processes that install OpenShift Container Platform clusters. The SiteConfig CR contains cluster-specific parameters required for installation. It has options for applying select configuration CRs during installation including user defined extra manifests.

The ZTP GitOps plugin processes SiteConfig CRs to generate a collection of CRs on the hub cluster. This triggers the assisted service in Red Hat Advanced Cluster Management (RHACM) to install OpenShift Container Platform on the bare-metal host. You can find installation status and error messages in these CRs on the hub cluster.

You can provision single clusters manually or in batches with ZTP:

Provisioning a single cluster: Create a single SiteConfig CR and related installation and configuration CRs for the cluster, and apply them in the hub cluster to begin cluster provisioning. This is a good way to test your CRs before deploying on a larger scale.
Provisioning many clusters: Install managed clusters in batches of up to 400 by defining SiteConfig and related CRs in a Git repository. ArgoCD uses the SiteConfig CRs to deploy the sites. The RHACM policy generator creates the manifests and applies them to the hub cluster. This starts the cluster provisioning process.

19.1.4. Configuring managed clusters with policies and PolicyGenTemplate resources

Zero touch provisioning (ZTP) uses Red Hat Advanced Cluster Management (RHACM) to configure clusters by using a policy-based governance approach to applying the configuration.

The policy generator or PolicyGen is a plugin for the GitOps Operator that enables the creation of RHACM policies from a concise template. The tool can combine multiple CRs into a single policy, and you can generate multiple policies that apply to various subsets of clusters in your fleet.

Note

For scalability and to reduce the complexity of managing configurations across the fleet of clusters, use configuration CRs with as much commonality as possible.

Where possible, apply configuration CRs using a fleet-wide common policy.
The next preference is to create logical groupings of clusters to manage as much of the remaining configurations as possible under a group policy.
When a configuration is unique to an individual site, use RHACM templating on the hub cluster to inject the site-specific data into a common or group policy. Alternatively, apply an individual site policy for the site.

The following diagram shows how the policy generator interacts with GitOps and RHACM in the configuration phase of cluster deployment.

For large fleets of clusters, it is typical for there to be a high-level of consistency in the configuration of those clusters.

The following recommended structuring of policies combines configuration CRs to meet several goals:

Describe common configurations once and apply to the fleet.
Minimize the number of maintained and managed policies.
Support flexibility in common configurations for cluster variants.

Table 19.1. Recommended PolicyGenTemplate policy categories

Policy category	Description
Common	A policy that exists in the common category is applied to all clusters in the fleet. Use common `PolicyGenTemplate` CRs to apply common installation settings across all cluster types.
Groups	A policy that exists in the groups category is applied to a group of clusters in the fleet. Use group `PolicyGenTemplate` CRs to manage specific aspects of single-node, three-node, and standard cluster installations. Cluster groups can also follow geographic region, hardware variant, etc.
Sites	A policy that exists in the sites category is applied to a specific cluster site. Any cluster can have its own specific policies maintained.

Additional resources

For more information about extracting the reference SiteConfig and PolicyGenTemplate CRs from the ztp-site-generate container image, see Preparing the ZTP Git repository.

19.2. Preparing the hub cluster for ZTP

To use RHACM in a disconnected environment, create a mirror registry that mirrors the OpenShift Container Platform release images and Operator Lifecycle Manager (OLM) catalog that contains the required Operator images. OLM manages, installs, and upgrades Operators and their dependencies in the cluster. You can also use a disconnected mirror host to serve the RHCOS ISO and RootFS disk images that are used to provision the bare-metal hosts.

19.2.1. Telco RAN 4.10 validated solution software versions

The Red Hat Telco Radio Access Network (RAN) version 4.10 solution has been validated using the following Red Hat software products.

Table 19.2. Telco RAN 4.10 validated solution software

Product	Software version
Hub cluster OpenShift Container Platform version	4.10
GitOps ZTP plugin	4.9 or 4.10
Red Hat Advanced Cluster Management (RHACM)	2.4 or 2.5
Red Hat OpenShift GitOps	1.4
Topology Aware Lifecycle Manager (TALM)	4.10 (Technology Preview)

19.2.2. Installing GitOps ZTP in a disconnected environment

Use Red Hat Advanced Cluster Management (RHACM), Red Hat OpenShift GitOps, and Topology Aware Lifecycle Manager (TALM) on the hub cluster in the disconnected environment to manage the deployment of multiple managed clusters.

Prerequisites

You have installed the OpenShift Container Platform CLI (oc).
You have logged in as a user with cluster-admin privileges.
You have configured a disconnected mirror registry for use in the cluster.
Note
The disconnected mirror registry that you create must contain a version of TALM backup and pre-cache images that matches the version of TALM running in the hub cluster. The spoke cluster must be able to resolve these images in the disconnected mirror registry.

Procedure

Install RHACM in the hub cluster. See Installing RHACM in a disconnected environment.
Install GitOps and TALM in the hub cluster.

Additional resources

19.2.3. Adding RHCOS ISO and RootFS images to the disconnected mirror host

Before you begin installing clusters in the disconnected environment with Red Hat Advanced Cluster Management (RHACM), you must first host Red Hat Enterprise Linux CoreOS (RHCOS) images for it to use. Use a disconnected mirror to host the RHCOS images.

Prerequisites

Deploy and configure an HTTP server to host the RHCOS image resources on the network. You must be able to access the HTTP server from your computer, and from the machines that you create.

Important

The RHCOS images might not change with every release of OpenShift Container Platform. You must download images with the highest version that is less than or equal to the version that you install. Use the image versions that match your OpenShift Container Platform version if they are available. You require ISO and RootFS images to install RHCOS on the hosts. RHCOS QCOW2 images are not supported for this installation type.

Procedure

Obtain the RHCOS ISO and RootFS images from mirror.openshift.com, for example:

Export the required image names and OpenShift Container Platform version as environment variables:
```
$ export ISO_IMAGE_NAME=<iso_image_name> 1
```
```
$ export ROOTFS_IMAGE_NAME=<rootfs_image_name> 1
```
```
$ export OCP_VERSION=<ocp_version> 1
```
1
ISO image name, for example, rhcos-4.10.1-x86_64-live.x86_64.iso
1
RootFS image name, for example, rhcos-4.10.1-x86_64-live-rootfs.x86_64.img
1
OpenShift Container Platform verison, for example, 4.10.1

Download the required images:

$ sudo wget https://mirror.openshift.com/pub/openshift-v4/dependencies/rhcos/4.10/${OCP_VERSION}/${ISO_IMAGE_NAME} -O /var/www/html/${ISO_IMAGE_NAME}

$ sudo wget https://mirror.openshift.com/pub/openshift-v4/dependencies/rhcos/4.10/${OCP_VERSION}/${ROOTFS_IMAGE_NAME} -O /var/www/html/${ROOTFS_IMAGE_NAME}

Verification steps

Verify that the images downloaded successfully and are being served on the disconnected mirror host, for example:

$ wget http://$(hostname)/${ISO_IMAGE_NAME}

Example output

Saving to: rhcos-4.10.1-x86_64-live.x86_64.iso
rhcos-4.10.1-x86_64-live.x86_64.iso-  11%[====>    ]  10.01M  4.71MB/s

Additional resources

19.2.4. Enabling the assisted service and updating AgentServiceConfig on the hub cluster

Red Hat Advanced Cluster Management (RHACM) uses the assisted service to deploy OpenShift Container Platform clusters. The assisted service is deployed automatically when you enable the MultiClusterHub Operator with Central Infrastructure Management (CIM). When you have enabled CIM on the hub cluster, you then need to update the AgentServiceConfig custom resource (CR) with references to the ISO and RootFS images that are hosted on the mirror registry HTTP server.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.
You have enabled the assisted service on the hub cluster. For more information, see Enabling CIM.

Procedure

Update the AgentServiceConfig CR by running the following command:
```
$ oc edit AgentServiceConfig
```
Add the following entry to the items.spec.osImages field in the CR:
```
- cpuArchitecture: x86_64
    openshiftVersion: "4.10"
    rootFSUrl: https://<host>/<path>/rhcos-live-rootfs.x86_64.img
    url: https://<mirror-registry>/<path>/rhcos-live.x86_64.iso
```
where:
<host>
Is the fully qualified domain name (FQDN) for the target mirror registry HTTP server.
<path>
Is the path to the image on the target mirror registry.
Save and quit the editor to apply the changes.

19.2.5. Configuring the hub cluster to use a disconnected mirror registry

You can configure the hub cluster to use a disconnected mirror registry for a disconnected environment.

Prerequisites

You have a disconnected hub cluster installation with Red Hat Advanced Cluster Management (RHACM) 2.4 installed.
You have hosted the rootfs and iso images on an HTTP server.

Warning

If you enable TLS for the HTTP server, you must confirm the root certificate is signed by an authority trusted by the client and verify the trusted certificate chain between your OpenShift Container Platform hub and managed clusters and the HTTP server. Using a server configured with an untrusted certificate prevents the images from being downloaded to the image creation service. Using untrusted HTTPS servers is not supported.

Procedure

Create a ConfigMap containing the mirror registry config:

apiVersion: v1
kind: ConfigMap
metadata:
  name: assisted-installer-mirror-config
  namespace: assisted-installer
  labels:
    app: assisted-service
data:
  ca-bundle.crt: <certificate> 1
  registries.conf: |  2
    unqualified-search-registries = ["registry.access.redhat.com", "docker.io"]

    [[registry]]
      location = <mirror_registry_url>  3
      insecure = false
      mirror-by-digest-only = true

1: The mirror registry’s certificate used when creating the mirror registry.
2: The configuration for the mirror registry.
3: The URL of the mirror registry.

This updates mirrorRegistryRef in the AgentServiceConfig custom resource, as shown below:

Example output

apiVersion: agent-install.openshift.io/v1beta1
kind: AgentServiceConfig
metadata:
  name: agent
spec:
  databaseStorage:
    volumeName: <db_pv_name>
    accessModes:
    - ReadWriteOnce
    resources:
      requests:
        storage: <db_storage_size>
  filesystemStorage:
    volumeName: <fs_pv_name>
    accessModes:
    - ReadWriteOnce
    resources:
      requests:
        storage: <fs_storage_size>
  mirrorRegistryRef:
    name: 'assisted-installer-mirror-config'
  osImages:
    - openshiftVersion: <ocp_version>
      rootfs: <rootfs_url> 1
      url: <iso_url> 2

1 2: Must match the URLs of the HTTPD server.

Important

A valid NTP server is required during cluster installation. Ensure that a suitable NTP server is available and can be reached from the installed clusters through the disconnected network.

19.2.6. Configuring the hub cluster with ArgoCD

You can configure your hub cluster with a set of ArgoCD applications that generate the required installation and policy custom resources (CR) for each site based on a zero touch provisioning (ZTP) GitOps flow.

Prerequisites

You have a OpenShift Container Platform hub cluster with Red Hat Advanced Cluster Management (RHACM) and Red Hat OpenShift GitOps installed.
You have extracted the reference deployment from the ZTP GitOps plugin container as described in the "Preparing the GitOps ZTP site configuration repository" section. Extracting the reference deployment creates the out/argocd/deployment directory referenced in the following procedure.

Procedure

Prepare the ArgoCD pipeline configuration:
1. Create a Git repository with the directory structure similar to the example directory. For more information, see "Preparing the GitOps ZTP site configuration repository".
2. Configure access to the repository using the ArgoCD UI. Under Settings configure the following:
  - Repositories - Add the connection information. The URL must end in .git, for example, https://repo.example.com/repo.git and credentials.
  - Certificates - Add the public certificate for the repository, if needed.
3. Modify the two ArgoCD applications, out/argocd/deployment/clusters-app.yaml and out/argocd/deployment/policies-app.yaml, based on your Git repository:
  - Update the URL to point to the Git repository. The URL ends with .git, for example, https://repo.example.com/repo.git.
  - The targetRevision indicates which Git repository branch to monitor.
  - path specifies the path to the SiteConfig and PolicyGenTemplate CRs, respectively.
To install the ZTP GitOps plugin you must patch the ArgoCD instance in the hub cluster by using the patch file previously extracted into the out/argocd/deployment/ directory. Run the following command:
```
$ oc patch argocd openshift-gitops \
-n openshift-gitops --type=merge \
--patch-file out/argocd/deployment/argocd-openshift-gitops-patch.json
```
Apply the pipeline configuration to your hub cluster by using the following command:
```
$ oc apply -k out/argocd/deployment
```

Additional resources

Preparing the GitOps ZTP site configuration repository

19.2.7. Preparing the GitOps ZTP site configuration repository

Before you can use the ZTP GitOps pipeline, you need to prepare the Git repository to host the site configuration data.

Prerequisites

You have configured the hub cluster GitOps applications for generating the required installation and policy custom resources (CRs).
You have deployed the managed clusters using zero touch provisioning (ZTP).

Procedure

Create a directory structure with separate paths for the SiteConfig and PolicyGenTemplate CRs.

Export the argocd directory from the ztp-site-generate container image using the following commands:

$ podman pull registry.redhat.io/openshift4/ztp-site-generate-rhel8:v4.10

$ mkdir -p ./out

$ podman run --log-driver=none --rm registry.redhat.io/openshift4/ztp-site-generate-rhel8:v{product-version} extract /home/ztp --tar | tar x -C ./out

Check that the out directory contains the following subdirectories:
- out/extra-manifest contains the source CR files that SiteConfig uses to generate extra manifest configMap.
- out/source-crs contains the source CR files that PolicyGenTemplate uses to generate the Red Hat Advanced Cluster Management (RHACM) policies.
- out/argocd/deployment contains patches and YAML files to apply on the hub cluster for use in the next step of this procedure.
- out/argocd/example contains the examples for SiteConfig and PolicyGenTemplate files that represent the recommended configuration.

The directory structure under out/argocd/example serves as a reference for the structure and content of your Git repository. The example includes SiteConfig and PolicyGenTemplate reference CRs for single-node, three-node, and standard clusters. Remove references to cluster types that you are not using. The following example describes a set of CRs for a network of single-node clusters:

example
├── policygentemplates
│   ├── common-ranGen.yaml
│   ├── example-sno-site.yaml
│   ├── group-du-sno-ranGen.yaml
│   ├── group-du-sno-validator-ranGen.yaml
│   ├── kustomization.yaml
│   └── ns.yaml
└── siteconfig
    ├── example-sno.yaml
    ├── KlusterletAddonConfigOverride.yaml
    └── kustomization.yaml

Keep SiteConfig and PolicyGenTemplate CRs in separate directories. Both the SiteConfig and PolicyGenTemplate directories must contain a kustomization.yaml file that explicitly includes the files in that directory.

This directory structure and the kustomization.yaml files must be committed and pushed to your Git repository. The initial push to Git should include the kustomization.yaml files. The SiteConfig (example-sno.yaml) and PolicyGenTemplate (common-ranGen.yaml, group-du-sno*.yaml, and example-sno-site.yaml) files can be omitted and pushed at a later time as required when deploying a site.

The KlusterletAddonConfigOverride.yaml file is only required if one or more SiteConfig CRs which make reference to it are committed and pushed to Git. See example-sno.yaml for an example of how this is used.

19.3. Installing managed clusters with RHACM and SiteConfig resources

You can provision OpenShift Container Platform clusters at scale with Red Hat Advanced Cluster Management (RHACM) using the assisted service and the GitOps plugin policy generator with core-reduction technology enabled. The zero touch priovisioning (ZTP) pipeline performs the cluster installations. ZTP can be used in a disconnected environment.

19.3.1. GitOps ZTP and Topology Aware Lifecycle Manager

GitOps zero touch provisioning (ZTP) generates installation and configuration CRs from manifests stored in Git. These artifacts are applied to a centralized hub cluster where Red Hat Advanced Cluster Management (RHACM), the assisted service, and the Topology Aware Lifecycle Manager (TALM) use the CRs to install and configure the managed cluster. The configuration phase of the ZTP pipeline uses the TALM to orchestrate the application of the configuration CRs to the cluster. There are several key integration points between GitOps ZTP and the TALM.

Inform policies

By default, GitOps ZTP creates all policies with a remediation action of inform. These policies cause RHACM to report on compliance status of clusters relevant to the policies but does not apply the desired configuration. During the ZTP process, after OpenShift installation, the TALM steps through the created inform policies and enforces them on the target managed cluster(s). This applies the configuration to the managed cluster. Outside of the ZTP phase of the cluster lifecycle, this allows you to change policies without the risk of immediately rolling those changes out to affected managed clusters. You can control the timing and the set of remediated clusters by using TALM.

Automatic creation of ClusterGroupUpgrade CRs

To automate the initial configuration of newly deployed clusters, TALM monitors the state of all ManagedCluster CRs on the hub cluster. Any ManagedCluster CR that does not have a ztp-done label applied, including newly created ManagedCluster CRs, causes the TALM to automatically create a ClusterGroupUpgrade CR with the following characteristics:

The ClusterGroupUpgrade CR is created and enabled in the ztp-install namespace.
ClusterGroupUpgrade CR has the same name as the ManagedCluster CR.
The cluster selector includes only the cluster associated with that ManagedCluster CR.
The set of managed policies includes all policies that RHACM has bound to the cluster at the time the ClusterGroupUpgrade is created.
Pre-caching is disabled.
Timeout set to 4 hours (240 minutes).

The automatic creation of an enabled ClusterGroupUpgrade ensures that initial zero-touch deployment of clusters proceeds without the need for user intervention. Additionally, the automatic creation of a ClusterGroupUpgrade CR for any ManagedCluster without the ztp-done label allows a failed ZTP installation to be restarted by simply deleting the ClusterGroupUpgrade CR for the cluster.

Waves

Each policy generated from a PolicyGenTemplate CR includes a ztp-deploy-wave annotation. This annotation is based on the same annotation from each CR which is included in that policy. The wave annotation is used to order the policies in the auto-generated ClusterGroupUpgrade CR. The wave annotation is not used other than for the auto-generated ClusterGroupUpgrade CR.

Note

All CRs in the same policy must have the same setting for the ztp-deploy-wave annotation. The default value of this annotation for each CR can be overridden in the PolicyGenTemplate. The wave annotation in the source CR is used for determining and setting the policy wave annotation. This annotation is removed from each built CR which is included in the generated policy at runtime.

The TALM applies the configuration policies in the order specified by the wave annotations. The TALM waits for each policy to be compliant before moving to the next policy. It is important to ensure that the wave annotation for each CR takes into account any prerequisites for those CRs to be applied to the cluster. For example, an Operator must be installed before or concurrently with the configuration for the Operator. Similarly, the CatalogSource for an Operator must be installed in a wave before or concurrently with the Operator Subscription. The default wave value for each CR takes these prerequisites into account.

Multiple CRs and policies can share the same wave number. Having fewer policies can result in faster deployments and lower CPU usage. It is a best practice to group many CRs into relatively few waves.

To check the default wave value in each source CR, run the following command against the out/source-crs directory that is extracted from the ztp-site-generate container image:

$ grep -r "ztp-deploy-wave" out/source-crs

Phase labels

The ClusterGroupUpgrade CR is automatically created and includes directives to annotate the ManagedCluster CR with labels at the start and end of the ZTP process.

When ZTP configuration post-installation commences, the ManagedCluster has the ztp-running label applied. When all policies are remediated to the cluster and are fully compliant, these directives cause the TALM to remove the ztp-running label and apply the ztp-done label.

For deployments that make use of the informDuValidator policy, the ztp-done label is applied when the cluster is fully ready for deployment of applications. This includes all reconciliation and resulting effects of the ZTP applied configuration CRs. The ztp-done label affects automatic ClusterGroupUpgrade CR creation by TALM. Do not manipulate this label after the initial ZTP installation of the cluster.

Linked CRs

The automatically created ClusterGroupUpgrade CR has the owner reference set as the ManagedCluster from which it was derived. This reference ensures that deleting the ManagedCluster CR causes the instance of the ClusterGroupUpgrade to be deleted along with any supporting resources.

19.3.2. Overview of deploying managed clusters with ZTP

Red Hat Advanced Cluster Management (RHACM) uses zero touch provisioning (ZTP) to deploy single-node OpenShift Container Platform clusters, three-node clusters, and standard clusters. You manage site configuration data as OpenShift Container Platform custom resources (CRs) in a Git repository. ZTP uses a declarative GitOps approach for a develop once, deploy anywhere model to deploy the managed clusters.

The deployment of the clusters includes:

Installing the host operating system (RHCOS) on a blank server
Deploying OpenShift Container Platform
Creating cluster policies and site subscriptions
Making the necessary network configurations to the server operating system
Deploying profile Operators and performing any needed software-related configuration, such as performance profile, PTP, and SR-IOV

Overview of the managed site installation process

After you apply the managed site custom resources (CRs) on the hub cluster, the following actions happen automatically:

A Discovery image ISO file is generated and booted on the target host.
When the ISO file successfully boots on the target host it reports the host hardware information to RHACM.
After all hosts are discovered, OpenShift Container Platform is installed.
When OpenShift Container Platform finishes installing, the hub installs the klusterlet service on the target cluster.
The requested add-on services are installed on the target cluster.

The Discovery image ISO process is complete when the Agent CR for the managed cluster is created on the hub cluster.

Important

The target bare-metal host must meet the networking, firmware, and hardware requirements listed in Recommended single-node OpenShift cluster configuration for vDU application workloads.

19.3.3. Creating the managed bare-metal host secrets

Add the required Secret custom resources (CRs) for the managed bare-metal host to the hub cluster. You need a secret for the ZTP pipeline to access the Baseboard Management Controller (BMC) and a secret for the assisted installer service to pull cluster installation images from the registry.

Note

The secrets are referenced from the SiteConfig CR by name. The namespace must match the SiteConfig namespace.

Procedure

Create a YAML secret file containing credentials for the host Baseboard Management Controller (BMC) and a pull secret required for installing OpenShift and all add-on cluster Operators:
1. Save the following YAML as the file example-sno-secret.yaml:
```
apiVersion: v1
kind: Secret
metadata:
  name: example-sno-bmc-secret
  namespace: example-sno 1
data: 2
  password: <base64_password>
  username: <base64_username>
type: Opaque
---
apiVersion: v1
kind: Secret
metadata:
  name: pull-secret
  namespace: example-sno  3
data:
  .dockerconfigjson: <pull_secret> 4
type: kubernetes.io/dockerconfigjson
```
  1
  Must match the namespace configured in the related SiteConfig CR
  2
  Base64-encoded values for password and username
  3
  Must match the namespace configured in the related SiteConfig CR
  4
  Base64-encoded pull secret
Add the relative path to example-sno-secret.yaml to the kustomization.yaml file that you use to install the cluster.

19.3.4. Deploying a managed cluster with SiteConfig and ZTP

Use the following procedure to create a SiteConfig custom resource (CR) and related files and initiate the zero touch provisioning (ZTP) cluster deployment.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.
You configured the hub cluster for generating the required installation and policy CRs.
You created a Git repository where you manage your custom site configuration data. The repository must be accessible from the hub cluster and you must configure it as a source repository for the ArgoCD application. See "Preparing the GitOps ZTP site configuration repository" for more information.
Note
When you create the source repository, ensure that you patch the ArgoCD application with the argocd/deployment/argocd-openshift-gitops-patch.json patch-file that you extract from the ztp-site-generate container. See "Configuring the hub cluster with ArgoCD".
To be ready for provisioning managed clusters, you require the following for each bare-metal host:
Network connectivity
Your network requires DNS. Managed cluster hosts should be reachable from the hub cluster. Ensure that Layer 3 connectivity exists between the hub cluster and the managed cluster host.
Baseboard Management Controller (BMC) details
ZTP uses BMC username and password details to connect to the BMC during cluster installation. The GitOps ZTP plugin manages the ManagedCluster CRs on the hub cluster based on the SiteConfig CR in your site Git repo. You create individual BMCSecret CRs for each host manually.

Procedure

Create the required managed cluster secrets on the hub cluster. These resources must be in a namespace with a name matching the cluster name. For example, in out/argocd/example/siteconfig/example-sno.yaml, the cluster name and namespace is example-sno.
1. Export the cluster namespace by running the following command:
```
$ export CLUSTERNS=example-sno
```
2. Create the namespace:
```
$ oc create namespace $CLUSTERNS
```
Create pull secret and BMC Secret CRs for the managed cluster. The pull secret must contain all the credentials necessary for installing OpenShift Container Platform and all required Operators. See "Creating the managed bare-metal host secrets" for more information.
Note
The secrets are referenced from the SiteConfig custom resource (CR) by name. The namespace must match the SiteConfig namespace.
Create a SiteConfig CR for your cluster in your local clone of the Git repository:
1. Choose the appropriate example for your CR from the out/argocd/example/siteconfig/ folder. The folder includes example files for single node, three-node, and standard clusters:
  - example-sno.yaml
  - example-3node.yaml
  - example-standard.yaml
2. Change the cluster and host details in the example file to match the type of cluster you want. For example:
  Example single-node OpenShift cluster SiteConfig CR
```
apiVersion: ran.openshift.io/v1
kind: SiteConfig
metadata:
  name: "<site_name>"
  namespace: "<site_name>"
spec:
  baseDomain: "example.com"
  pullSecretRef:
    name: "assisted-deployment-pull-secret" 1
  clusterImageSetNameRef: "openshift-4.10" 2
  sshPublicKey: "ssh-rsa AAAA..." 3
  clusters:
  - clusterName: "<site_name>"
    networkType: "OVNKubernetes"
    clusterLabels: 4
      common: true
      group-du-sno: ""
      sites : "<site_name>"
    clusterNetwork:
      - cidr: 1001:1::/48
        hostPrefix: 64
    machineNetwork:
      - cidr: 1111:2222:3333:4444::/64
    serviceNetwork:
      - 1001:2::/112
    additionalNTPSources:
      - 1111:2222:3333:4444::2
    #crTemplates:
    #  KlusterletAddonConfig: "KlusterletAddonConfigOverride.yaml" 5
    nodes:
      - hostName: "example-node.example.com" 6
        role: "master"
        #biosConfigRef:
        #  filePath: "example-hw.profile" 7
        bmcAddress: idrac-virtualmedia://<out_of_band_ip>/<system_id>/ 8
        bmcCredentialsName:
          name: "bmh-secret" 9
        bootMACAddress: "AA:BB:CC:DD:EE:11"
        bootMode: "UEFI" 10
        rootDeviceHints:
          wwn: "0x11111000000asd123"
        cpuset: "0-1,52-53"
        nodeNetwork: 11
          interfaces:
            - name: eno1
              macAddress: "AA:BB:CC:DD:EE:11"
          config:
            interfaces:
              - name: eno1
                type: ethernet
                state: up
                ipv4:
                  enabled: false
                ipv6: 12
                  enabled: true
                  address:
                  - ip: 1111:2222:3333:4444::aaaa:1
                    prefix-length: 64
            dns-resolver:
              config:
                search:
                - example.com
                server:
                - 1111:2222:3333:4444::2
            routes:
              config:
              - destination: ::/0
                next-hop-interface: eno1
                next-hop-address: 1111:2222:3333:4444::1
                table-id: 254
```
  1
  Create the assisted-deployment-pull-secret CR with the same namespace as the SiteConfig CR.
  2
  clusterImageSetNameRef defines an image set available on the hub cluster. To see the list of supported versions on your hub cluster, run oc get clusterimagesets.
  3
  Configure the SSH public key used to access the cluster.
  4
  Cluster labels must correspond to the bindingRules field in the PolicyGenTemplate CRs that you define. For example, policygentemplates/common-ranGen.yaml applies to all clusters with common: true set, policygentemplates/group-du-sno-ranGen.yaml applies to all clusters with group-du-sno: "" set.
  5
  Optional. The CR specifed under KlusterletAddonConfig is used to override the default KlusterletAddonConfig that is created for the cluster.
  6
  For single-node deployments, define a single host. For three-node deployments, define three hosts. For standard deployments, define three hosts with role: master and two or more hosts defined with role: worker.
  7
  Optional. Use biosConfigRef to configure desired firmware for the host.
  8
  Applies to all cluster types. Specifies the BMC address.
  9
  Create the bmh-secret CR that specifies the BMC credentials. Use the same namespace as the SiteConfig CR.
  10
  Use UEFISecureBoot to enable secure boot on the host.
  11
  Specifies the network settings for the node.
  12
  Configures the IPv6 address for the host. For single-node OpenShift clusters with static IP addresses, the node-specific API and Ingress IPs should be the same.
  Note
  For more information about BMC addressing, see the "Additional resources" section.
3. You can inspect the default set of extra-manifest MachineConfig CRs in out/argocd/extra-manifest. It is automatically applied to the cluster when it is installed.
4. Optional: To provision additional install-time manifests on the provisioned cluster, create a directory in your Git repository, for example, sno-extra-manifest/, and add your custom manifest CRs to this directory. If your SiteConfig.yaml refers to this directory in the extraManifestPath field, any CRs in this referenced directory are appended to the default set of extra manifests.
Add the SiteConfig CR to the kustomization.yaml file in the generators section, similar to the example shown in out/argocd/example/siteconfig/kustomization.yaml.
Commit the SiteConfig CR and associated kustomization.yaml changes in your Git repository and push the changes.
The ArgoCD pipeline detects the changes and begins the managed cluster deployment.

Additional resources

19.3.5. Monitoring managed cluster installation progress

The ArgoCD pipeline uses the SiteConfig CR to generate the cluster configuration CRs and syncs it with the hub cluster. You can monitor the progress of the synchronization in the ArgoCD dashboard.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Procedure

When the synchronization is complete, the installation generally proceeds as follows:

The Assisted Service Operator installs OpenShift Container Platform on the cluster. You can monitor the progress of cluster installation from the RHACM dashboard or from the command line by running the following commands:
1. Export the cluster name:
```
$ export CLUSTER=<clusterName>
```
2. Query the AgentClusterInstall CR for the managed cluster:
```
$ oc get agentclusterinstall -n $CLUSTER $CLUSTER -o jsonpath='{.status.conditions[?(@.type=="Completed")]}' | jq
```
3. Get the installation events for the cluster:
```
$ curl -sk $(oc get agentclusterinstall -n $CLUSTER $CLUSTER -o jsonpath='{.status.debugInfo.eventsURL}')  | jq '.[-2,-1]'
```

19.3.6. Troubleshooting GitOps ZTP by validating the installation CRs

The ArgoCD pipeline uses the SiteConfig and PolicyGenTemplate custom resources (CRs) to generate the cluster configuration CRs and Red Hat Advanced Cluster Management (RHACM) policies. Use the following steps to troubleshoot issues that might occur during this process.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Procedure

Check that the installation CRs were created by using the following command:
```
$ oc get AgentClusterInstall -n <cluster_name>
```
If no object is returned, use the following steps to troubleshoot the ArgoCD pipeline flow from SiteConfig files to the installation CRs.
Verify that the ManagedCluster CR was generated using the SiteConfig CR on the hub cluster:
```
$ oc get managedcluster
```

If the ManagedCluster is missing, check if the clusters application failed to synchronize the files from the Git repository to the hub cluster:

$ oc describe -n openshift-gitops application clusters

Check for the Status.Conditions field to view the error logs for the managed cluster. For example, setting an invalid value for extraManifestPath: in the SiteConfig CR raises the following error:

Status:
  Conditions:
    Last Transition Time:  2021-11-26T17:21:39Z
    Message:               rpc error: code = Unknown desc = `kustomize build /tmp/https___git.com/ran-sites/siteconfigs/ --enable-alpha-plugins` failed exit status 1: 2021/11/26 17:21:40 Error could not create extra-manifest ranSite1.extra-manifest3 stat extra-manifest3: no such file or directory 2021/11/26 17:21:40 Error: could not build the entire SiteConfig defined by /tmp/kust-plugin-config-913473579: stat extra-manifest3: no such file or directory Error: failure in plugin configured via /tmp/kust-plugin-config-913473579; exit status 1: exit status 1
    Type:  ComparisonError

Check the Status.Sync field. If there are log errors, the Status.Sync field could indicate an Unknown error:

Status:
  Sync:
    Compared To:
      Destination:
        Namespace:  clusters-sub
        Server:     https://kubernetes.default.svc
      Source:
        Path:             sites-config
        Repo URL:         https://git.com/ran-sites/siteconfigs/.git
        Target Revision:  master
    Status:               Unknown

19.3.7. Removing a managed cluster site from the ZTP pipeline

You can remove a managed site and the associated installation and configuration policy CRs from the ZTP pipeline.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Precedure

Remove a site and the associated CRs by removing the associated SiteConfig and PolicyGenTemplate files from the kustomization.yaml file.
When you run the ZTP pipeline again, the generated CRs are removed.
Optional: If you want to permanently remove a site, you should also remove the SiteConfig and site-specific PolicyGenTemplate files from the Git repository.
Optional: If you want to remove a site temporarily, for example when redeploying a site, you can leave the SiteConfig and site-specific PolicyGenTemplate CRs in the Git repository.

Note

After removing the SiteConfig file from the Git repository, if the corresponding clusters get stuck in the detach process, check Red Hat Advanced Cluster Management (RHACM) on the hub cluster for information about cleaning up the detached cluster.

Additional resources

For information about removing a cluster, see Removing a cluster from management.

19.3.8. Removing obsolete content from the ZTP pipeline

If a change to the PolicyGenTemplate configuration results in obsolete policies, for example, if you rename policies, use the following procedure to remove the obsolete policies.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Procedure

Remove the affected PolicyGenTemplate files from the Git repository, commit and push to the remote repository.
Wait for the changes to synchronize through the application and the affected policies to be removed from the hub cluster.
Add the updated PolicyGenTemplate files back to the Git repository, and then commit and push to the remote repository.
Note
Removing zero touch provisioning (ZTP) policies from the Git repository, and as a result also removing them from the hub cluster, does not affect the configuration of the managed cluster. The policy and CRs managed by that policy remains in place on the managed cluster.
Optional: As an alternative, after making changes to PolicyGenTemplate CRs that result in obsolete policies, you can remove these policies from the hub cluster manually. You can delete policies from the RHACM console using the Governance tab or by running the following command:
```
$ oc delete policy -n <namespace> <policy_name>
```

19.3.9. Tearing down the ZTP pipeline

You can remove the ArgoCD pipeline and all generated ZTP artifacts.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Procedure

Detach all clusters from Red Hat Advanced Cluster Management (RHACM) on the hub cluster.
Delete the kustomization.yaml file in the deployment directory using the following command:
```
$ oc delete -k out/argocd/deployment
```
Commit and push your changes to the site repository.

19.4. Configuring managed clusters with policies and PolicyGenTemplate resources

Applied policy custom resources (CRs) configure the managed clusters that you provision. You can customize how Red Hat Advanced Cluster Management (RHACM) uses PolicyGenTemplate CRs to generate the applied policy CRs.

19.4.1. About the PolicyGenTemplate CRD

The PolicyGenTemplate custom resource definition (CRD) tells the PolicyGen policy generator what custom resources (CRs) to include in the cluster configuration, how to combine the CRs into the generated policies, and what items in those CRs need to be updated with overlay content.

The following example shows a PolicyGenTemplate CR (common-du-ranGen.yaml) extracted from the ztp-site-generate reference container. The common-du-ranGen.yaml file defines two Red Hat Advanced Cluster Management (RHACM) policies. The polices manage a collection of configuration CRs, one for each unique value of policyName in the CR. common-du-ranGen.yaml creates a single placement binding and a placement rule to bind the policies to clusters based on the labels listed in the bindingRules section.

Example PolicyGenTemplate CR - common-du-ranGen.yaml

---
apiVersion: ran.openshift.io/v1
kind: PolicyGenTemplate
metadata:
  name: "common"
  namespace: "ztp-common"
spec:
  bindingRules:
    common: "true" 1
  sourceFiles: 2
    - fileName: SriovSubscription.yaml
      policyName: "subscriptions-policy"
    - fileName: SriovSubscriptionNS.yaml
      policyName: "subscriptions-policy"
    - fileName: SriovSubscriptionOperGroup.yaml
      policyName: "subscriptions-policy"
    - fileName: SriovOperatorStatus.yaml
      policyName: "subscriptions-policy"
    - fileName: PtpSubscription.yaml
      policyName: "subscriptions-policy"
    - fileName: PtpSubscriptionNS.yaml
      policyName: "subscriptions-policy"
    - fileName: PtpSubscriptionOperGroup.yaml
      policyName: "subscriptions-policy"
    - fileName: PtpOperatorStatus.yaml
      policyName: "subscriptions-policy"
    - fileName: ClusterLogNS.yaml
      policyName: "subscriptions-policy"
    - fileName: ClusterLogOperGroup.yaml
      policyName: "subscriptions-policy"
    - fileName: ClusterLogSubscription.yaml
      policyName: "subscriptions-policy"
    - fileName: ClusterLogOperatorStatus.yaml
      policyName: "subscriptions-policy"
    - fileName: StorageNS.yaml
      policyName: "subscriptions-policy"
    - fileName: StorageOperGroup.yaml
      policyName: "subscriptions-policy"
    - fileName: StorageSubscription.yaml
      policyName: "subscriptions-policy"
    - fileName: StorageOperatorStatus.yaml
      policyName: "subscriptions-policy"
    - fileName: ReduceMonitoringFootprint.yaml
      policyName: "config-policy"
    - fileName: OperatorHub.yaml 3
      policyName: "config-policy"
    - fileName: DefaultCatsrc.yaml 4
      policyName: "config-policy" 5
      metadata:
        name: redhat-operators
      spec:
        displayName: disconnected-redhat-operators
        image: registry.example.com:5000/disconnected-redhat-operators/disconnected-redhat-operator-index:v4.9
    - fileName: DisconnectedICSP.yaml
      policyName: "config-policy"
      spec:
        repositoryDigestMirrors:
        - mirrors:
          - registry.example.com:5000
          source: registry.redhat.io

1: common: "true" applies the policies to all clusters with this label.
2: Files listed under sourceFiles create the Operator policies for installed clusters.
3: OperatorHub.yaml configures the OperatorHub for the disconnected registry.
4: DefaultCatsrc.yaml configures the catalog source for the disconnected registry.
5: policyName: "config-policy" configures Operator subscriptions. The OperatorHub CR disables the default and this CR replaces redhat-operators with a CatalogSource CR that points to the disconnected registry.

A PolicyGenTemplate CR can be constructed with any number of included CRs. Apply the following example CR in the hub cluster to generate a policy containing a single CR:

apiVersion: ran.openshift.io/v1
kind: PolicyGenTemplate
metadata:
  name: "group-du-sno"
  namespace: "ztp-group"
spec:
  bindingRules:
    group-du-sno: ""
  mcp: "master"
  sourceFiles:
    - fileName: PtpConfigSlave.yaml
      policyName: "config-policy"
      metadata:
        name: "du-ptp-slave"
      spec:
        profile:
        - name: "slave"
          interface: "ens5f0"
          ptp4lOpts: "-2 -s --summary_interval -4"
          phc2sysOpts: "-a -r -n 24"

Using the source file PtpConfigSlave.yaml as an example, the file defines a PtpConfig CR. The generated policy for the PtpConfigSlave example is named group-du-sno-config-policy. The PtpConfig CR defined in the generated group-du-sno-config-policy is named du-ptp-slave. The spec defined in PtpConfigSlave.yaml is placed under du-ptp-slave along with the other spec items defined under the source file.

The following example shows the group-du-sno-config-policy CR:

apiVersion: policy.open-cluster-management.io/v1
kind: Policy
metadata:
  name: group-du-ptp-config-policy
  namespace: groups-sub
  annotations:
    policy.open-cluster-management.io/categories: CM Configuration Management
    policy.open-cluster-management.io/controls: CM-2 Baseline Configuration
    policy.open-cluster-management.io/standards: NIST SP 800-53
spec:
    remediationAction: inform
    disabled: false
    policy-templates:
        - objectDefinition:
            apiVersion: policy.open-cluster-management.io/v1
            kind: ConfigurationPolicy
            metadata:
                name: group-du-ptp-config-policy-config
            spec:
                remediationAction: inform
                severity: low
                namespaceselector:
                    exclude:
                        - kube-*
                    include:
                        - '*'
                object-templates:
                    - complianceType: musthave
                      objectDefinition:
                        apiVersion: ptp.openshift.io/v1
                        kind: PtpConfig
                        metadata:
                            name: du-ptp-slave
                            namespace: openshift-ptp
                        spec:
                            recommend:
                                - match:
                                - nodeLabel: node-role.kubernetes.io/worker-du
                                  priority: 4
                                  profile: slave
                            profile:
                                - interface: ens5f0
                                  name: slave
                                  phc2sysOpts: -a -r -n 24
                                  ptp4lConf: |
                                    [global]
                                    #
                                    # Default Data Set
                                    #
                                    twoStepFlag 1
                                    slaveOnly 0
                                    priority1 128
                                    priority2 128
                                    domainNumber 24
                                    .....

19.4.2. Recommendations when customizing PolicyGenTemplate CRs

Consider the following best practices when customizing site configuration PolicyGenTemplate custom resources (CRs):

Use as few policies as are necessary. Using fewer policies requires less resources. Each additional policy creates overhead for the hub cluster and the deployed managed cluster. CRs are combined into policies based on the policyName field in the PolicyGenTemplate CR. CRs in the same PolicyGenTemplate which have the same value for policyName are managed under a single policy.
In disconnected environments, use a single catalog source for all Operators by configuring the registry as a single index containing all Operators. Each additional CatalogSource CR on the managed clusters increases CPU usage.
MachineConfig CRs should be included as extraManifests in the SiteConfig CR so that they are applied during installation. This can reduce the overall time taken until the cluster is ready to deploy applications.
PolicyGenTemplates should override the channel field to explicitly identify the desired version. This ensures that changes in the source CR during upgrades does not update the generated subscription.

Additional resources

For recommendations about scaling clusters with RHACM, see Performance and scalability.

Note

When managing large numbers of spoke clusters on the hub cluster, minimize the number of policies to reduce resource consumption.

Grouping multiple configuration CRs into a single or limited number of policies is one way to reduce the overall number of policies on the hub cluster. When using the common, group, and site hierarchy of policies for managing site configuration, it is especially important to combine site-specific configuration into a single policy.

19.4.3. PolicyGenTemplate CRs for RAN deployments

Use PolicyGenTemplate (PGT) custom resources (CRs) to customize the configuration applied to the cluster by using the GitOps zero touch provisioning (ZTP) pipeline. The PGT CR allows you to generate one or more policies to manage the set of configuration CRs on your fleet of clusters. The PGT identifies the set of managed CRs, bundles them into policies, builds the policy wrapping around those CRs, and associates the policies with clusters by using label binding rules.

The reference configuration, obtained from the GitOps ZTP container, is designed to provide a set of critical features and node tuning settings that ensure the cluster can support the stringent performance and resource utilization constraints typical of RAN (Radio Access Network) Distributed Unit (DU) applications. Changes or omissions from the baseline configuration can affect feature availability, performance, and resource utilization. Use the reference PolicyGenTemplate CRs as the basis to create a hierarchy of configuration files tailored to your specific site requirements.

The baseline PolicyGenTemplate CRs that are defined for RAN DU cluster configuration can be extracted from the GitOps ZTP ztp-site-generate container. See "Preparing the GitOps ZTP site configuration repository" for further details.

The PolicyGenTemplate CRs can be found in the ./out/argocd/example/policygentemplates folder. The reference architecture has common, group, and site-specific configuration CRs. Each PolicyGenTemplate CR refers to other CRs that can be found in the ./out/source-crs folder.

The PolicyGenTemplate CRs relevant to RAN cluster configuration are described below. Variants are provided for the group PolicyGenTemplate CRs to account for differences in single-node, three-node compact, and standard cluster configurations. Similarly, site-specific configuration variants are provided for single-node clusters and multi-node (compact or standard) clusters. Use the group and site-specific configuration variants that are relevant for your deployment.

Table 19.3. PolicyGenTemplate CRs for RAN deployments

PolicyGenTemplate CR	Description
`example-multinode-site.yaml`	Contains a set of CRs that get applied to multi-node clusters. These CRs configure SR-IOV features typical for RAN installations.
`example-sno-site.yaml`	Contains a set of CRs that get applied to single-node OpenShift clusters. These CRs configure SR-IOV features typical for RAN installations.
`common-ranGen.yaml`	Contains a set of common RAN CRs that get applied to all clusters. These CRs subscribe to a set of operators providing cluster features typical for RAN as well as baseline cluster tuning.
`group-du-3node-ranGen.yaml`	Contains the RAN policies for three-node clusters only.
`group-du-sno-ranGen.yaml`	Contains the RAN policies for single-node clusters only.
`group-du-standard-ranGen.yaml`	Contains the RAN policies for standard three control-plane clusters.
`group-du-3node-validator-ranGen.yaml`	`PolicyGenTemplate` CR used to generate the various policies required for three-node clusters.
`group-du-standard-validator-ranGen.yaml`	`PolicyGenTemplate` CR used to generate the various policies required for standard clusters.
`group-du-sno-validator-ranGen.yaml`	`PolicyGenTemplate` CR used to generate the various policies required for single-node OpenShift clusters.

Additional resources

Preparing the GitOps ZTP site configuration repository

19.4.4. Customizing a managed cluster with PolicyGenTemplate CRs

Use the following procedure to customize the policies that get applied to the managed cluster that you provision using the zero touch provisioning (ZTP) pipeline.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.
You configured the hub cluster for generating the required installation and policy CRs.
You created a Git repository where you manage your custom site configuration data. The repository must be accessible from the hub cluster and be defined as a source repository for the Argo CD application.

Procedure

Create a PolicyGenTemplate CR for site-specific configuration CRs.
1. Choose the appropriate example for your CR from the out/argocd/example/policygentemplates folder, for example, example-sno-site.yaml or example-multinode-site.yaml.
2. Change the bindingRules field in the example file to match the site-specific label included in the SiteConfig CR. In the example SiteConfig file, the site-specific label is sites: example-sno.
  Note
  Ensure that the labels defined in your PolicyGenTemplate bindingRules field correspond to the labels that are defined in the related managed clusters SiteConfig CR.
3. Change the content in the example file to match the desired configuration.
Optional: Create a PolicyGenTemplate CR for any common configuration CRs that apply to the entire fleet of clusters.
1. Select the appropriate example for your CR from the out/argocd/example/policygentemplates folder, for example, common-ranGen.yaml.
2. Change the content in the example file to match the desired configuration.
Optional: Create a PolicyGenTemplate CR for any group configuration CRs that apply to the certain groups of clusters in the fleet.
Ensure that the content of the overlaid spec files matches your desired end state. As a reference, the out/source-crs directory contains the full list of source-crs available to be included and overlaid by your PolicyGenTemplate templates.
Note
Depending on the specific requirements of your clusters, you might need more than a single group policy per cluster type, especially considering that the example group policies each have a single PerformancePolicy.yaml file that can only be shared across a set of clusters if those clusters consist of identical hardware configurations.
1. Select the appropriate example for your CR from the out/argocd/example/policygentemplates folder, for example, group-du-sno-ranGen.yaml.
2. Change the content in the example file to match the desired configuration.
Optional. Create a validator inform policy PolicyGenTemplate CR to signal when the ZTP installation and configuration of the deployed cluster is complete. For more information, see "Creating a validator inform policy".
Define all the policy namespaces in a YAML file similar to the example out/argocd/example/policygentemplates/ns.yaml file.
Important
Do not include the Namespace CR in the same file with the PolicyGenTemplate CR.
Add the PolicyGenTemplate CRs and Namespace CR to the kustomization.yaml file in the generators section, similar to the example shown in out/argocd/example/policygentemplates/kustomization.yaml.
Commit the PolicyGenTemplate CRs, Namespace CR, and associated kustomization.yaml file in your Git repository and push the changes.
The ArgoCD pipeline detects the changes and begins the managed cluster deployment. You can push the changes to the SiteConfig CR and the PolicyGenTemplate CR simultaneously.

Additional resources

Signalling ZTP cluster deployment completion with validator inform policies

19.4.5. Monitoring managed cluster policy deployment progress

The ArgoCD pipeline uses PolicyGenTemplate CRs in Git to generate the RHACM policies and then sync them to the hub cluster. You can monitor the progress of the managed cluster policy synchronization after the assisted service installs OpenShift Container Platform on the managed cluster.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Procedure

The Topology Aware Lifecycle Manager (TALM) applies the configuration policies that are bound to the cluster.
After the cluster installation is complete and the cluster becomes Ready, a ClusterGroupUpgrade CR corresponding to this cluster, with a list of ordered policies defined by the ran.openshift.io/ztp-deploy-wave annotations, is automatically created by the TALM. The cluster’s policies are applied in the order listed in ClusterGroupUpgrade CR.
You can monitor the high-level progress of configuration policy reconciliation by using the following commands:
```
$ export CLUSTER=<clusterName>
```
```
$ oc get clustergroupupgrades -n ztp-install $CLUSTER -o jsonpath='{.status.conditions[-1:]}' | jq
```
Example output
```
{
  "lastTransitionTime": "2022-11-09T07:28:09Z",
  "message": "The ClusterGroupUpgrade CR has upgrade policies that are still non compliant",
  "reason": "UpgradeNotCompleted",
  "status": "False",
  "type": "Ready"
}
```

You can monitor the detailed cluster policy compliance status by using the RHACM dashboard or the command line.

To check policy compliance by using oc, run the following command:

$ oc get policies -n $CLUSTER

Example output

NAME                                                     REMEDIATION ACTION   COMPLIANCE STATE   AGE
ztp-common.common-config-policy                          inform               Compliant          3h42m
ztp-common.common-subscriptions-policy                   inform               NonCompliant       3h42m
ztp-group.group-du-sno-config-policy                     inform               NonCompliant       3h42m
ztp-group.group-du-sno-validator-du-policy               inform               NonCompliant       3h42m
ztp-install.example1-common-config-policy-pjz9s          enforce              Compliant          167m
ztp-install.example1-common-subscriptions-policy-zzd9k   enforce              NonCompliant       164m
ztp-site.example1-config-policy                          inform               NonCompliant       3h42m
ztp-site.example1-perf-policy                            inform               NonCompliant       3h42m

To check policy status from the RHACM web console, perform the following actions:
1. Click Governance → Find policies.
2. Click on a cluster policy to check it’s status.

When all of the cluster policies become compliant, ZTP installation and configuration for the cluster is complete. The ztp-done label is added to the cluster.

In the reference configuration, the final policy that becomes compliant is the one defined in the *-du-validator-policy policy. This policy, when compliant on a cluster, ensures that all cluster configuration, Operator installation, and Operator configuration is complete.

19.4.6. Validating the generation of configuration policy CRs

Policy custom resources (CRs) are generated in the same namespace as the PolicyGenTemplate from which they are created. The same troubleshooting flow applies to all policy CRs generated from a PolicyGenTemplate regardless of whether they are ztp-common, ztp-group, or ztp-site based, as shown using the following commands:

$ export NS=<namespace>

$ oc get policy -n $NS

The expected set of policy-wrapped CRs should be displayed.

If the policies failed synchronization, use the following troubleshooting steps.

Procedure

To display detailed information about the policies, run the following command:
```
$ oc describe -n openshift-gitops application policies
```

Check for Status: Conditions: to show the error logs. For example, setting an invalid sourceFile→fileName: generates the error shown below:

Status:
  Conditions:
    Last Transition Time:  2021-11-26T17:21:39Z
    Message:               rpc error: code = Unknown desc = `kustomize build /tmp/https___git.com/ran-sites/policies/ --enable-alpha-plugins` failed exit status 1: 2021/11/26 17:21:40 Error could not find test.yaml under source-crs/: no such file or directory Error: failure in plugin configured via /tmp/kust-plugin-config-52463179; exit status 1: exit status 1
    Type:  ComparisonError

Check for Status: Sync:. If there are log errors at Status: Conditions:, the Status: Sync: shows Unknown or Error:

Status:
  Sync:
    Compared To:
      Destination:
        Namespace:  policies-sub
        Server:     https://kubernetes.default.svc
      Source:
        Path:             policies
        Repo URL:         https://git.com/ran-sites/policies/.git
        Target Revision:  master
    Status:               Error

When Red Hat Advanced Cluster Management (RHACM) recognizes that policies apply to a ManagedCluster object, the policy CR objects are applied to the cluster namespace. Check to see if the policies were copied to the cluster namespace:

$ oc get policy -n $CLUSTER

Example output:

NAME                                         REMEDIATION ACTION   COMPLIANCE STATE   AGE
ztp-common.common-config-policy              inform               Compliant          13d
ztp-common.common-subscriptions-policy       inform               Compliant          13d
ztp-group.group-du-sno-config-policy         inform               Compliant          13d
Ztp-group.group-du-sno-validator-du-policy   inform               Compliant          13d
ztp-site.example-sno-config-policy           inform               Compliant          13d

RHACM copies all applicable policies into the cluster namespace. The copied policy names have the format: <policyGenTemplate.Namespace>.<policyGenTemplate.Name>-<policyName>.

Check the placement rule for any policies not copied to the cluster namespace. The matchSelector in the PlacementRule for those policies should match labels on the ManagedCluster object:
```
$ oc get placementrule -n $NS
```
Note the PlacementRule name appropriate for the missing policy, common, group, or site, using the following command:
```
$ oc get placementrule -n $NS <placementRuleName> -o yaml
```
- The status-decisions should include your cluster name.
- The key-value pair of the matchSelector in the spec must match the labels on your managed cluster.
Check the labels on the ManagedCluster object using the following command:
```
$ oc get ManagedCluster $CLUSTER -o jsonpath='{.metadata.labels}' | jq
```
Check to see which policies are compliant using the following command:
```
$ oc get policy -n $CLUSTER
```
If the Namespace, OperatorGroup, and Subscription policies are compliant but the Operator configuration policies are not, it is likely that the Operators did not install on the managed cluster. This causes the Operator configuration policies to fail to apply because the CRD is not yet applied to the spoke.

19.4.7. Restarting policy reconciliation

You can restart policy reconciliation when unexpected compliance issues occur, for example, when the ClusterGroupUpgrade custom resource (CR) has timed out.

Procedure

A ClusterGroupUpgrade CR is generated in the namespace ztp-install by the Topology Aware Lifecycle Manager after the managed cluster becomes Ready:
```
$ export CLUSTER=<clusterName>
```
```
$ oc get clustergroupupgrades -n ztp-install $CLUSTER
```
If there are unexpected issues and the policies fail to become complaint within the configured timeout (the default is 4 hours), the status of the ClusterGroupUpgrade CR shows UpgradeTimedOut:
```
$ oc get clustergroupupgrades -n ztp-install $CLUSTER -o jsonpath='{.status.conditions[?(@.type=="Ready")]}'
```
A ClusterGroupUpgrade CR in the UpgradeTimedOut state automatically restarts its policy reconciliation every hour. If you have changed your policies, you can start a retry immediately by deleting the existing ClusterGroupUpgrade CR. This triggers the automatic creation of a new ClusterGroupUpgrade CR that begins reconciling the policies immediately:
```
$ oc delete clustergroupupgrades -n ztp-install $CLUSTER
```

Note that when the ClusterGroupUpgrade CR completes with status UpgradeCompleted and the managed cluster has the label ztp-done applied, you can make additional configuration changes using PolicyGenTemplate. Deleting the existing ClusterGroupUpgrade CR will not make the TALM generate a new CR.

At this point, ZTP has completed its interaction with the cluster and any further interactions should be treated as an update and a new ClusterGroupUpgrade CR created for remediation of the policies.

Additional resources

For information about using Topology Aware Lifecycle Manager (TALM) to construct your own ClusterGroupUpgrade CR, see About the ClusterGroupUpgrade CR.

19.4.8. Indication of done for ZTP installations

Zero touch provisioning (ZTP) simplifies the process of checking the ZTP installation status for a cluster. The ZTP status moves through three phases: cluster installation, cluster configuration, and ZTP done.

Cluster installation phase

The cluster installation phase is shown by the ManagedClusterJoined and ManagedClusterAvailable conditions in the ManagedCluster CR . If the ManagedCluster CR does not have these conditions, or the condition is set to False, the cluster is still in the installation phase. Additional details about installation are available from the AgentClusterInstall and ClusterDeployment CRs. For more information, see "Troubleshooting GitOps ZTP".

Cluster configuration phase

The cluster configuration phase is shown by a ztp-running label applied the ManagedCluster CR for the cluster.

ZTP done

Cluster installation and configuration is complete in the ZTP done phase. This is shown by the removal of the ztp-running label and addition of the ztp-done label to the ManagedCluster CR. The ztp-done label shows that the configuration has been applied and the baseline DU configuration has completed cluster tuning.

The transition to the ZTP done state is conditional on the compliant state of a Red Hat Advanced Cluster Management (RHACM) validator inform policy. This policy captures the existing criteria for a completed installation and validates that it moves to a compliant state only when ZTP provisioning of the managed cluster is complete.

The validator inform policy ensures the configuration of the cluster is fully applied and Operators have completed their initialization. The policy validates the following:

The target MachineConfigPool contains the expected entries and has finished updating. All nodes are available and not degraded.
The SR-IOV Operator has completed initialization as indicated by at least one SriovNetworkNodeState with syncStatus: Succeeded.
The PTP Operator daemon set exists.

19.5. Manually installing a single-node OpenShift cluster with ZTP

You can deploy a managed single-node OpenShift cluster by using Red Hat Advanced Cluster Management (RHACM) and the assisted service.

Note

If you are creating multiple managed clusters, use the SiteConfig method described in Deploying far edge sites with ZTP.

Important

The target bare-metal host must meet the networking, firmware, and hardware requirements listed in Recommended cluster configuration for vDU application workloads.

19.5.1. Generating ZTP installation and configuration CRs manually

Use the generator entrypoint for the ztp-site-generate container to generate the site installation and configuration custom resource (CRs) for a cluster based on SiteConfig and PolicyGenTemplate CRs.

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.

Procedure

Create an output folder by running the following command:
```
$ mkdir -p ./out
```

Export the argocd directory from the ztp-site-generate container image:

$ podman run --log-driver=none --rm registry.redhat.io/openshift4/ztp-site-generate-rhel8:v4.10 extract /home/ztp --tar | tar x -C ./out

The ./out directory has the reference PolicyGenTemplate and SiteConfig CRs in the out/argocd/example/ folder.

Example output

out
 └── argocd
      └── example
           ├── policygentemplates
           │     ├── common-ranGen.yaml
           │     ├── example-sno-site.yaml
           │     ├── group-du-sno-ranGen.yaml
           │     ├── group-du-sno-validator-ranGen.yaml
           │     ├── kustomization.yaml
           │     └── ns.yaml
           └── siteconfig
                  ├── example-sno.yaml
                  ├── KlusterletAddonConfigOverride.yaml
                  └── kustomization.yaml

Create an output folder for the site installation CRs:
```
$ mkdir -p ./site-install
```

Modify the example SiteConfig CR for the cluster type that you want to install. Copy example-sno.yaml to site-1-sno.yaml and modify the CR to match the details of the site and bare-metal host that you want to install, for example:

Example single-node OpenShift cluster SiteConfig CR

apiVersion: ran.openshift.io/v1
kind: SiteConfig
metadata:
  name: "<site_name>"
  namespace: "<site_name>"
spec:
  baseDomain: "example.com"
  pullSecretRef:
    name: "assisted-deployment-pull-secret" 1
  clusterImageSetNameRef: "openshift-4.10" 2
  sshPublicKey: "ssh-rsa AAAA..." 3
  clusters:
  - clusterName: "<site_name>"
    networkType: "OVNKubernetes"
    clusterLabels: 4
      common: true
      group-du-sno: ""
      sites : "<site_name>"
    clusterNetwork:
      - cidr: 1001:1::/48
        hostPrefix: 64
    machineNetwork:
      - cidr: 1111:2222:3333:4444::/64
    serviceNetwork:
      - 1001:2::/112
    additionalNTPSources:
      - 1111:2222:3333:4444::2
    #crTemplates:
    #  KlusterletAddonConfig: "KlusterletAddonConfigOverride.yaml" 5
    nodes:
      - hostName: "example-node.example.com" 6
        role: "master"
        #biosConfigRef:
        #  filePath: "example-hw.profile" 7
        bmcAddress: idrac-virtualmedia://<out_of_band_ip>/<system_id>/ 8
        bmcCredentialsName:
          name: "bmh-secret" 9
        bootMACAddress: "AA:BB:CC:DD:EE:11"
        bootMode: "UEFI" 10
        rootDeviceHints:
          wwn: "0x11111000000asd123"
        cpuset: "0-1,52-53"
        nodeNetwork: 11
          interfaces:
            - name: eno1
              macAddress: "AA:BB:CC:DD:EE:11"
          config:
            interfaces:
              - name: eno1
                type: ethernet
                state: up
                ipv4:
                  enabled: false
                ipv6: 12
                  enabled: true
                  address:
                  - ip: 1111:2222:3333:4444::aaaa:1
                    prefix-length: 64
            dns-resolver:
              config:
                search:
                - example.com
                server:
                - 1111:2222:3333:4444::2
            routes:
              config:
              - destination: ::/0
                next-hop-interface: eno1
                next-hop-address: 1111:2222:3333:4444::1
                table-id: 254

1: Create the assisted-deployment-pull-secret CR with the same namespace as the SiteConfig CR.
2: clusterImageSetNameRef defines an image set available on the hub cluster. To see the list of supported versions on your hub cluster, run oc get clusterimagesets.
3: Configure the SSH public key used to access the cluster.
4: Cluster labels must correspond to the bindingRules field in the PolicyGenTemplate CRs that you define. For example, policygentemplates/common-ranGen.yaml applies to all clusters with common: true set, policygentemplates/group-du-sno-ranGen.yaml applies to all clusters with group-du-sno: "" set.
5: Optional. The CR specifed under KlusterletAddonConfig is used to override the default KlusterletAddonConfig that is created for the cluster.
6: For single-node deployments, define a single host. For three-node deployments, define three hosts. For standard deployments, define three hosts with role: master and two or more hosts defined with role: worker.
7: Optional. Use biosConfigRef to configure desired firmware for the host.
8: Applies to all cluster types. Specifies the BMC address.
9: Create the bmh-secret CR that specifies the BMC credentials. Use the same namespace as the SiteConfig CR.
10: Use UEFISecureBoot to enable secure boot on the host.
11: Specifies the network settings for the node.
12: Configures the IPv6 address for the host. For single-node OpenShift clusters with static IP addresses, the node-specific API and Ingress IPs should be the same.

Generate the day-0 installation CRs by processing the modified SiteConfig CR site-1-sno.yaml by running the following command:

$ podman run -it --rm -v `pwd`/out/argocd/example/siteconfig:/resources:Z -v `pwd`/site-install:/output:Z,U registry.redhat.io/openshift4/ztp-site-generate-rhel8:v4.10.1 generator install site-1-sno.yaml /output

Example output

site-install
└── site-1-sno
    ├── site-1_agentclusterinstall_example-sno.yaml
    ├── site-1-sno_baremetalhost_example-node1.example.com.yaml
    ├── site-1-sno_clusterdeployment_example-sno.yaml
    ├── site-1-sno_configmap_example-sno.yaml
    ├── site-1-sno_infraenv_example-sno.yaml
    ├── site-1-sno_klusterletaddonconfig_example-sno.yaml
    ├── site-1-sno_machineconfig_02-master-workload-partitioning.yaml
    ├── site-1-sno_machineconfig_predefined-extra-manifests-master.yaml
    ├── site-1-sno_machineconfig_predefined-extra-manifests-worker.yaml
    ├── site-1-sno_managedcluster_example-sno.yaml
    ├── site-1-sno_namespace_example-sno.yaml
    └── site-1-sno_nmstateconfig_example-node1.example.com.yaml

Optional: Generate just the day-0 MachineConfig installation CRs for a particular cluster type by processing the reference SiteConfig CR with the -E option. For example, run the following commands:

Create an output folder for the MachineConfig CRs:
```
$ mkdir -p ./site-machineconfig
```

Generate the MachineConfig installation CRs:

$ podman run -it --rm -v `pwd`/out/argocd/example/siteconfig:/resources:Z -v `pwd`/site-machineconfig:/output:Z,U registry.redhat.io/openshift4/ztp-site-generate-rhel8:v4.10.1 generator install -E site-1-sno.yaml /output

Example output

site-machineconfig
└── site-1-sno
    ├── site-1-sno_machineconfig_02-master-workload-partitioning.yaml
    ├── site-1-sno_machineconfig_predefined-extra-manifests-master.yaml
    └── site-1-sno_machineconfig_predefined-extra-manifests-worker.yaml

Generate and export the day-2 configuration CRs using the reference PolicyGenTemplate CRs from the previous step. Run the following commands:

Create an output folder for the day-2 CRs:
```
$ mkdir -p ./ref
```

Generate and export the day-2 configuration CRs:

$ podman run -it --rm -v `pwd`/out/argocd/example/policygentemplates:/resources:Z -v `pwd`/ref:/output:Z,U registry.redhat.io/openshift4/ztp-site-generate-rhel8:v4.10.1 generator config -N . /output

The command generates example group and site-specific PolicyGenTemplate CRs for single-node OpenShift, three-node clusters, and standard clusters in the ./ref folder.

Example output

ref
 └── customResource
      ├── common
      ├── example-multinode-site
      ├── example-sno
      ├── group-du-3node
      ├── group-du-3node-validator
      │    └── Multiple-validatorCRs
      ├── group-du-sno
      ├── group-du-sno-validator
      ├── group-du-standard
      └── group-du-standard-validator
           └── Multiple-validatorCRs

Use the generated CRs as the basis for the CRs that you use to install the cluster. You apply the installation CRs to the hub cluster as described in "Installing a single managed cluster". The configuration CRs can be applied to the cluster after cluster installation is complete.

Additional resources

19.5.2. Creating the managed bare-metal host secrets

Note

The secrets are referenced from the SiteConfig CR by name. The namespace must match the SiteConfig namespace.

Procedure

Create a YAML secret file containing credentials for the host Baseboard Management Controller (BMC) and a pull secret required for installing OpenShift and all add-on cluster Operators:
1. Save the following YAML as the file example-sno-secret.yaml:
```
apiVersion: v1
kind: Secret
metadata:
  name: example-sno-bmc-secret
  namespace: example-sno 1
data: 2
  password: <base64_password>
  username: <base64_username>
type: Opaque
---
apiVersion: v1
kind: Secret
metadata:
  name: pull-secret
  namespace: example-sno  3
data:
  .dockerconfigjson: <pull_secret> 4
type: kubernetes.io/dockerconfigjson
```
  1
  Must match the namespace configured in the related SiteConfig CR
  2
  Base64-encoded values for password and username
  3
  Must match the namespace configured in the related SiteConfig CR
  4
  Base64-encoded pull secret
Add the relative path to example-sno-secret.yaml to the kustomization.yaml file that you use to install the cluster.

19.5.3. Installing a single managed cluster

You can manually deploy a single managed cluster using the assisted service and Red Hat Advanced Cluster Management (RHACM).

Prerequisites

You have installed the OpenShift CLI (oc).
You have logged in to the hub cluster as a user with cluster-admin privileges.
You have created the baseboard management controller (BMC) Secret and the image pull-secret Secret custom resources (CRs). See "Creating the managed bare-metal host secrets" for details.
Your target bare-metal host meets the networking and hardware requirements for managed clusters.

Procedure

Create a ClusterImageSet for each specific cluster version to be deployed, for example clusterImageSet-4.10.yaml. A ClusterImageSet has the following format:
```
apiVersion: hive.openshift.io/v1
kind: ClusterImageSet
metadata:
  name: openshift-4.10.0-rc.0 1
spec:
   releaseImage: quay.io/openshift-release-dev/ocp-release:4.10.0-x86_64 2
```
1
The descriptive version that you want to deploy.
2
Specifies the releaseImage to deploy and determines the operating system image version. The discovery ISO is based on the image version as set by releaseImage, or the latest version if the exact version is unavailable.
Apply the clusterImageSet CR:
```
$ oc apply -f clusterImageSet-4.10.yaml
```

Create the Namespace CR in the cluster-namespace.yaml file:

apiVersion: v1
kind: Namespace
metadata:
     name: <cluster_name> 1
     labels:
        name: <cluster_name> 2

1 2: The name of the managed cluster to provision.

Apply the Namespace CR by running the following command:
```
$ oc apply -f cluster-namespace.yaml
```
Apply the generated day-0 CRs that you extracted from the ztp-site-generate container and customized to meet your requirements:
```
$ oc apply -R ./site-install/site-sno-1
```

Additional resources

Connectivity prerequisites for managed cluster networks

19.5.4. Monitoring the managed cluster installation status

Ensure that cluster provisioning was successful by checking the cluster status.

Prerequisites

All of the custom resources have been configured and provisioned, and the Agent custom resource is created on the hub for the managed cluster.

Procedure

Check the status of the managed cluster:
```
$ oc get managedcluster
```
True indicates the managed cluster is ready.
Check the agent status:
```
$ oc get agent -n <cluster_name>
```
Use the describe command to provide an in-depth description of the agent’s condition. Statuses to be aware of include BackendError, InputError, ValidationsFailing, InstallationFailed, and AgentIsConnected. These statuses are relevant to the Agent and AgentClusterInstall custom resources.
```
$ oc describe agent -n <cluster_name>
```

Check the cluster provisioning status:

$ oc get agentclusterinstall -n <cluster_name>

Use the describe command to provide an in-depth description of the cluster provisioning status:
```
$ oc describe agentclusterinstall -n <cluster_name>
```
Check the status of the managed cluster’s add-on services:
```
$ oc get managedclusteraddon -n <cluster_name>
```

Retrieve the authentication information of the kubeconfig file for the managed cluster:

$ oc get secret -n <cluster_name> <cluster_name>-admin-kubeconfig -o jsonpath={.data.kubeconfig} | base64 -d > <directory>/<cluster_name>-kubeconfig

19.5.5. Troubleshooting the managed cluster

Use this procedure to diagnose any installation issues that might occur with the managed cluster.

Procedure

Check the status of the managed cluster:
```
$ oc get managedcluster
```
Example output
```
NAME            HUB ACCEPTED   MANAGED CLUSTER URLS   JOINED   AVAILABLE   AGE
SNO-cluster     true                                   True     True      2d19h
```
If the status in the AVAILABLE column is True, the managed cluster is being managed by the hub.
If the status in the AVAILABLE column is Unknown, the managed cluster is not being managed by the hub. Use the following steps to continue checking to get more information.

Check the AgentClusterInstall install status:

$ oc get clusterdeployment -n <cluster_name>

Example output

NAME        PLATFORM            REGION   CLUSTERTYPE   INSTALLED    INFRAID    VERSION  POWERSTATE AGE
Sno0026    agent-baremetal                               false                          Initialized
2d14h

If the status in the INSTALLED column is false, the installation was unsuccessful.

If the installation failed, enter the following command to review the status of the AgentClusterInstall resource:
```
$ oc describe agentclusterinstall -n <cluster_name> <cluster_name>
```
Resolve the errors and reset the cluster:
1. Remove the cluster’s managed cluster resource:
```
$ oc delete managedcluster <cluster_name>
```
2. Remove the cluster’s namespace:
```
$ oc delete namespace <cluster_name>
```
  This deletes all of the namespace-scoped custom resources created for this cluster. You must wait for the ManagedCluster CR deletion to complete before proceeding.
3. Recreate the custom resources for the managed cluster.

19.5.6. RHACM generated cluster installation CRs reference

Red Hat Advanced Cluster Management (RHACM) supports deploying OpenShift Container Platform on single-node clusters, three-node clusters, and standard clusters with a specific set of installation custom resources (CRs) that you generate using SiteConfig CRs for each site.

Note

Every managed cluster has its own namespace, and all of the installation CRs except for ManagedCluster and ClusterImageSet are under that namespace. ManagedCluster and ClusterImageSet are cluster-scoped, not namespace-scoped. The namespace and the CR names match the cluster name.

The following table lists the installation CRs that are automatically applied by the RHACM assisted service when it installs clusters using the SiteConfig CRs that you configure.

Table 19.4. Cluster installation CRs generated by RHACM

CR	Description	Usage
`BareMetalHost`	Contains the connection information for the Baseboard Management Controller (BMC) of the target bare-metal host.	Provides access to the BMC to load and boot the discovery image on the target server by using the Redfish protocol.
`InfraEnv`	Contains information for installing OpenShift Container Platform on the target bare-metal host.	Used with `ClusterDeployment` to generate the discovery ISO for the managed cluster.
`AgentClusterInstall`	Specifies details of the managed cluster configuration such as networking and the number of control plane nodes. Displays the cluster `kubeconfig` and credentials when the installation is complete.	Specifies the managed cluster configuration information and provides status during the installation of the cluster.
`ClusterDeployment`	References the `AgentClusterInstall` CR to use.	Used with `InfraEnv` to generate the discovery ISO for the managed cluster.
`NMStateConfig`	Provides network configuration information such as `MAC` address to `IP` mapping, DNS server, default route, and other network settings. This is not needed if DHCP is used.	Sets up a static IP address for the managed cluster’s Kube API server.
`Agent`	Contains hardware information about the target bare-metal host.	Created automatically on the hub when the target machine’s discovery image boots.
`ManagedCluster`	When a cluster is managed by the hub, it must be imported and known. This Kubernetes object provides that interface.	The hub uses this resource to manage and show the status of managed clusters.
`KlusterletAddonConfig`	Contains the list of services provided by the hub to be deployed to the `ManagedCluster` resource.	Tells the hub which addon services to deploy to the `ManagedCluster` resource.
`Namespace`	Logical space for `ManagedCluster` resources existing on the hub. Unique per site.	Propagates resources to the `ManagedCluster`.
`Secret`	Two CRs are created: `BMC Secret` and `Image Pull Secret`.	`BMC Secret` authenticates into the target bare-metal host using its username and password. `Image Pull Secret` contains authentication information for the OpenShift Container Platform image installed on the target bare-metal host.
`ClusterImageSet`	Contains OpenShift Container Platform image information such as the repository and image name.	Passed into resources to provide OpenShift Container Platform images.

19.6. Recommended single-node OpenShift cluster configuration for vDU application workloads

Use the following reference information to understand the single-node OpenShift configurations required to deploy virtual distributed unit (vDU) applications in the cluster. Configurations include cluster optimizations for high performance workloads, enabling workload partitioning, and minimizing the number of reboots required post-installation.

Additional resources

To deploy a single cluster by hand, see Manually installing a single-node OpenShift cluster with ZTP.
To deploy a fleet of clusters using GitOps zero touch provisioning (ZTP), see Deploying far edge sites with ZTP.

19.6.1. Running low latency applications on OpenShift Container Platform

OpenShift Container Platform enables low latency processing for applications running on commercial off-the-shelf (COTS) hardware by using several technologies and specialized hardware devices:

Real-time kernel for RHCOS: Ensures workloads are handled with a high degree of process determinism.
CPU isolation: Avoids CPU scheduling delays and ensures CPU capacity is available consistently.
NUMA-aware topology management: Aligns memory and huge pages with CPU and PCI devices to pin guaranteed container memory and huge pages to the non-uniform memory access (NUMA) node. Pod resources for all Quality of Service (QoS) classes stay on the same NUMA node. This decreases latency and improves performance of the node.
Huge pages memory management: Using huge page sizes improves system performance by reducing the amount of system resources required to access page tables.
Precision timing synchronization using PTP: Allows synchronization between nodes in the network with sub-microsecond accuracy.

19.6.2. Recommended cluster host requirements for vDU application workloads

Running vDU application workloads requires a bare-metal host with sufficient resources to run OpenShift Container Platform services and production workloads.

Table 19.5. Minimum resource requirements

Profile	vCPU	Memory	Storage
Minimum	4 to 8 vCPU cores	32GB of RAM	120GB

Note

One vCPU is equivalent to one physical core when simultaneous multithreading (SMT), or Hyper-Threading, is not enabled. When enabled, use the following formula to calculate the corresponding ratio:

(threads per core × cores) × sockets = vCPUs

Important

The server must have a Baseboard Management Controller (BMC) when booting with virtual media.

19.6.3. Configuring host firmware for low latency and high performance

Bare-metal hosts require the firmware to be configured before the host can be provisioned. The firmware configuration is dependent on the specific hardware and the particular requirements of your installation.

Procedure

Set the UEFI/BIOS Boot Mode to UEFI.
In the host boot sequence order, set Hard drive first.

Apply the specific firmware configuration for your hardware. The following table describes a representative firmware configuration for an Intel Xeon Skylake or Intel Cascade Lake server, based on the Intel FlexRAN 4G and 5G baseband PHY reference design.

Important

The exact firmware configuration depends on your specific hardware and network requirements. The following sample configuration is for illustrative purposes only.

Table 19.6. Sample firmware configuration for an Intel Xeon Skylake or Cascade Lake server

Firmware setting	Configuration
CPU Power and Performance Policy	Performance
Uncore Frequency Scaling	Disabled
Performance P-limit	Disabled
Enhanced Intel SpeedStep ® Tech	Enabled
Intel Configurable TDP	Enabled
Configurable TDP Level	Level 2
Intel® Turbo Boost Technology	Enabled
Energy Efficient Turbo	Disabled
Hardware P-States	Disabled
Package C-State	C0/C1 state
C1E	Disabled
Processor C6	Disabled

Note

Enable global SR-IOV and VT-d settings in the firmware for the host. These settings are relevant to bare-metal environments.

19.6.4. Connectivity prerequisites for managed cluster networks

Before you can install and provision a managed cluster with the zero touch provisioning (ZTP) GitOps pipeline, the managed cluster host must meet the following networking prerequisites:

There must be bi-directional connectivity between the ZTP GitOps container in the hub cluster and the Baseboard Management Controller (BMC) of the target bare-metal host.
The managed cluster must be able to resolve and reach the API hostname of the hub hostname and *.apps hostname. Here is an example of the API hostname of the hub and *.apps hostname:
- api.hub-cluster.internal.domain.com
- console-openshift-console.apps.hub-cluster.internal.domain.com
The hub cluster must be able to resolve and reach the API and *.apps hostname of the managed cluster. Here is an example of the API hostname of the managed cluster and *.apps hostname:
- api.sno-managed-cluster-1.internal.domain.com
- console-openshift-console.apps.sno-managed-cluster-1.internal.domain.com

19.6.5. Recommended installation-time cluster configurations

The ZTP pipeline applies the following custom resources (CRs) during cluster installation. These configuration CRs ensure that the cluster meets the feature and performance requirements necessary for running a vDU application.

Note

When using the ZTP GitOps plugin and SiteConfig CRs for cluster deployment, the following MachineConfig CRs are included by default.

Use the SiteConfig extraManifests filter to alter the CRs that are included by default. For more information, see Advanced managed cluster configuration with SiteConfig CRs.

19.6.5.1. Workload partitioning

Single-node OpenShift clusters that run DU workloads require workload partitioning. This limits the cores allowed to run platform services, maximizing the CPU core for application payloads.

Note

Workload partitioning can only be enabled during cluster installation. You cannot disable workload partitioning post-installation. However, you can reconfigure workload partitioning by updating the cpu value that you define in the performance profile, and in the related MachineConfig custom resource (CR).

The base64-encoded CR that enables workload partitioning contains the CPU set that the management workloads are constrained to. Encode host-specific values for crio.conf and kubelet.conf in base64. Adjust the content to match the CPU set that is specified in the cluster performance profile. It must match the number of cores in the cluster host.

Recommended workload partitioning configuration

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 02-master-workload-partitioning
spec:
  config:
    ignition:
      version: 3.2.0
    storage:
      files:
      - contents:
          source: data:text/plain;charset=utf-8;base64,W2NyaW8ucnVudGltZS53b3JrbG9hZHMubWFuYWdlbWVudF0KYWN0aXZhdGlvbl9hbm5vdGF0aW9uID0gInRhcmdldC53b3JrbG9hZC5vcGVuc2hpZnQuaW8vbWFuYWdlbWVudCIKYW5ub3RhdGlvbl9wcmVmaXggPSAicmVzb3VyY2VzLndvcmtsb2FkLm9wZW5zaGlmdC5pbyIKcmVzb3VyY2VzID0geyAiY3B1c2hhcmVzIiA9IDAsICJjcHVzZXQiID0gIjAtMSw1Mi01MyIgfQo=
        mode: 420
        overwrite: true
        path: /etc/crio/crio.conf.d/01-workload-partitioning
        user:
          name: root
      - contents:
          source: data:text/plain;charset=utf-8;base64,ewogICJtYW5hZ2VtZW50IjogewogICAgImNwdXNldCI6ICIwLTEsNTItNTMiCiAgfQp9Cg==
        mode: 420
        overwrite: true
        path: /etc/kubernetes/openshift-workload-pinning
        user:
          name: root

When configured in the cluster host, the contents of /etc/crio/crio.conf.d/01-workload-partitioning should look like this:
```
[crio.runtime.workloads.management]
activation_annotation = "target.workload.openshift.io/management"
annotation_prefix = "resources.workload.openshift.io"
resources = { "cpushares" = 0, "cpuset" = "0-1,52-53" } 1
```
1
The CPUs value varies based on the installation.
If Hyper-Threading is enabled, specify both threads for each core. The CPUs value must match the reserved CPU set specified in the performance profile.
When configured in the cluster, the contents of /etc/kubernetes/openshift-workload-pinning should look like this:
```
{
  "management": {
    "cpuset": "0-1,52-53" 1
  }
}
```
1
The cpuset must match the CPUs value in /etc/crio/crio.conf.d/01-workload-partitioning.

19.6.5.2. Reduced platform management footprint

To reduce the overall management footprint of the platform, a MachineConfig custom resource (CR) is required that places all Kubernetes-specific mount points in a new namespace separate from the host operating system. The following base64-encoded example MachineConfig CR illustrates this configuration.

Recommended container mount namespace configuration

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: container-mount-namespace-and-kubelet-conf-master
spec:
  config:
    ignition:
      version: 3.2.0
    storage:
      files:
      - contents:
          source: data:text/plain;charset=utf-8;base64,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
        mode: 493
        path: /usr/local/bin/extractExecStart
      - contents:
          source: data:text/plain;charset=utf-8;base64,IyEvYmluL2Jhc2gKbnNlbnRlciAtLW1vdW50PS9ydW4vY29udGFpbmVyLW1vdW50LW5hbWVzcGFjZS9tbnQgIiRAIgo=
        mode: 493
        path: /usr/local/bin/nsenterCmns
    systemd:
      units:
      - contents: |
          [Unit]
          Description=Manages a mount namespace that both kubelet and crio can use to share their container-specific mounts

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          RuntimeDirectory=container-mount-namespace
          Environment=RUNTIME_DIRECTORY=%t/container-mount-namespace
          Environment=BIND_POINT=%t/container-mount-namespace/mnt
          ExecStartPre=bash -c "findmnt ${RUNTIME_DIRECTORY} || mount --make-unbindable --bind ${RUNTIME_DIRECTORY} ${RUNTIME_DIRECTORY}"
          ExecStartPre=touch ${BIND_POINT}
          ExecStart=unshare --mount=${BIND_POINT} --propagation slave mount --make-rshared /
          ExecStop=umount -R ${RUNTIME_DIRECTORY}
        enabled: true
        name: container-mount-namespace.service
      - dropins:
        - contents: |
            [Unit]
            Wants=container-mount-namespace.service
            After=container-mount-namespace.service

            [Service]
            ExecStartPre=/usr/local/bin/extractExecStart %n /%t/%N-execstart.env ORIG_EXECSTART
            EnvironmentFile=-/%t/%N-execstart.env
            ExecStart=
            ExecStart=bash -c "nsenter --mount=%t/container-mount-namespace/mnt \
                ${ORIG_EXECSTART}"
          name: 90-container-mount-namespace.conf
        name: crio.service
      - dropins:
        - contents: |
            [Unit]
            Wants=container-mount-namespace.service
            After=container-mount-namespace.service

            [Service]
            ExecStartPre=/usr/local/bin/extractExecStart %n /%t/%N-execstart.env ORIG_EXECSTART
            EnvironmentFile=-/%t/%N-execstart.env
            ExecStart=
            ExecStart=bash -c "nsenter --mount=%t/container-mount-namespace/mnt \
                ${ORIG_EXECSTART} --housekeeping-interval=30s"
          name: 90-container-mount-namespace.conf
        - contents: |
            [Service]
            Environment="OPENSHIFT_MAX_HOUSEKEEPING_INTERVAL_DURATION=60s"
            Environment="OPENSHIFT_EVICTION_MONITORING_PERIOD_DURATION=30s"
          name: 30-kubelet-interval-tuning.conf
        name: kubelet.service

19.6.5.3. SCTP

Stream Control Transmission Protocol (SCTP) is a key protocol used in RAN applications. This MachineConfig object adds the SCTP kernel module to the node to enable this protocol.

Recommended SCTP configuration

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: load-sctp-module
spec:
  config:
    ignition:
      version: 2.2.0
    storage:
      files:
        - contents:
            source: data:,
            verification: {}
          filesystem: root
            mode: 420
            path: /etc/modprobe.d/sctp-blacklist.conf
        - contents:
            source: data:text/plain;charset=utf-8,sctp
          filesystem: root
            mode: 420
            path: /etc/modules-load.d/sctp-load.conf

19.6.5.4. Accelerated container startup

The following MachineConfig CR configures core OpenShift processes and containers to use all available CPU cores during system startup and shutdown. This accelerates the system recovery during initial boot and reboots.

Recommended accelerated container startup configuration

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 04-accelerated-container-startup-master
spec:
  config:
    ignition:
      version: 3.2.0
    storage:
      files:
      - contents:
          source: data:text/plain;charset=utf-8;base64,#!/bin/bash
#
# Temporarily reset the core system processes's CPU affinity to be unrestricted to accelerate startup and shutdown
#
# The defaults below can be overridden via environment variables
#

# The default set of critical processes whose affinity should be temporarily unbound:
CRITICAL_PROCESSES=${CRITICAL_PROCESSES:-"systemd ovs crio kubelet NetworkManager conmon dbus"}

# Default wait time is 600s = 10m:
MAXIMUM_WAIT_TIME=${MAXIMUM_WAIT_TIME:-600}

# Default steady-state threshold = 2%
# Allowed values:
#  4  - absolute pod count (+/-)
#  4% - percent change (+/-)
#  -1 - disable the steady-state check
STEADY_STATE_THRESHOLD=${STEADY_STATE_THRESHOLD:-2%}

# Default steady-state window = 60s
# If the running pod count stays within the given threshold for this time
# period, return CPU utilization to normal before the maximum wait time has
# expires
STEADY_STATE_WINDOW=${STEADY_STATE_WINDOW:-60}

# Default steady-state allows any pod count to be "steady state"
# Increasing this will skip any steady-state checks until the count rises above
# this number to avoid false positives if there are some periods where the
# count doesn't increase but we know we can't be at steady-state yet.
STEADY_STATE_MINIMUM=${STEADY_STATE_MINIMUM:-0}

#######################################################

KUBELET_CPU_STATE=/var/lib/kubelet/cpu_manager_state
FULL_CPU_STATE=/sys/fs/cgroup/cpuset/cpuset.cpus
unrestrictedCpuset() {
  local cpus
  if [[ -e $KUBELET_CPU_STATE ]]; then
      cpus=$(jq -r '.defaultCpuSet' <$KUBELET_CPU_STATE)
  fi
  if [[ -z $cpus ]]; then
    # fall back to using all cpus if the kubelet state is not configured yet
    [[ -e $FULL_CPU_STATE ]] || return 1
    cpus=$(<$FULL_CPU_STATE)
  fi
  echo $cpus
}

restrictedCpuset() {
  for arg in $(</proc/cmdline); do
    if [[ $arg =~ ^systemd.cpu_affinity= ]]; then
      echo ${arg#*=}
      return 0
    fi
  done
  return 1
}

getCPUCount () {
  local cpuset="$1"
  local cpulist=()
  local cpus=0
  local mincpus=2

  if [[ -z $cpuset || $cpuset =~ [^0-9,-] ]]; then
    echo $mincpus
    return 1
  fi

  IFS=',' read -ra cpulist <<< $cpuset

  for elm in "${cpulist[@]}"; do
    if [[ $elm =~ ^[0-9]+$ ]]; then
      (( cpus++ ))
    elif [[ $elm =~ ^[0-9]+-[0-9]+$ ]]; then
      local low=0 high=0
      IFS='-' read low high <<< $elm
      (( cpus += high - low + 1 ))
    else
      echo $mincpus
      return 1
    fi
  done

  # Return a minimum of 2 cpus
  echo $(( cpus > $mincpus ? cpus : $mincpus ))
  return 0
}

resetOVSthreads () {
  local cpucount="$1"
  local curRevalidators=0
  local curHandlers=0
  local desiredRevalidators=0
  local desiredHandlers=0
  local rc=0

  curRevalidators=$(ps -Teo pid,tid,comm,cmd | grep -e revalidator | grep -c ovs-vswitchd)
  curHandlers=$(ps -Teo pid,tid,comm,cmd | grep -e handler | grep -c ovs-vswitchd)

  # Calculate the desired number of threads the same way OVS does.
  # OVS will set these thread count as a one shot process on startup, so we
  # have to adjust up or down during the boot up process. The desired outcome is
  # to not restrict the number of thread at startup until we reach a steady
  # state.  At which point we need to reset these based on our restricted  set
  # of cores.
  # See OVS function that calculates these thread counts:
  # https://github.com/openvswitch/ovs/blob/master/ofproto/ofproto-dpif-upcall.c#L635
  (( desiredRevalidators=$cpucount / 4 + 1 ))
  (( desiredHandlers=$cpucount - $desiredRevalidators ))


  if [[ $curRevalidators -ne $desiredRevalidators || $curHandlers -ne $desiredHandlers ]]; then

    logger "Recovery: Re-setting OVS revalidator threads: ${curRevalidators} -> ${desiredRevalidators}"
    logger "Recovery: Re-setting OVS handler threads: ${curHandlers} -> ${desiredHandlers}"

    ovs-vsctl set \
      Open_vSwitch . \
      other-config:n-handler-threads=${desiredHandlers} \
      other-config:n-revalidator-threads=${desiredRevalidators}
    rc=$?
  fi

  return $rc
}

resetAffinity() {
  local cpuset="$1"
  local failcount=0
  local successcount=0
  logger "Recovery: Setting CPU affinity for critical processes \"$CRITICAL_PROCESSES\" to $cpuset"
  for proc in $CRITICAL_PROCESSES; do
    local pids="$(pgrep $proc)"
    for pid in $pids; do
      local tasksetOutput
      tasksetOutput="$(taskset -apc "$cpuset" $pid 2>&1)"
      if [[ $? -ne 0 ]]; then
        echo "ERROR: $tasksetOutput"
        ((failcount++))
      else
        ((successcount++))
      fi
    done
  done

  resetOVSthreads "$(getCPUCount ${cpuset})"
  if [[ $? -ne 0 ]]; then
    ((failcount++))
  else
    ((successcount++))
  fi

  logger "Recovery: Re-affined $successcount pids successfully"
  if [[ $failcount -gt 0 ]]; then
    logger "Recovery: Failed to re-affine $failcount processes"
    return 1
  fi
}

setUnrestricted() {
  logger "Recovery: Setting critical system processes to have unrestricted CPU access"
  resetAffinity "$(unrestrictedCpuset)"
}

setRestricted() {
  logger "Recovery: Resetting critical system processes back to normally restricted access"
  resetAffinity "$(restrictedCpuset)"
}

currentAffinity() {
  local pid="$1"
  taskset -pc $pid | awk -F': ' '{print $2}'
}

within() {
  local last=$1 current=$2 threshold=$3
  local delta=0 pchange
  delta=$(( current - last ))
  if [[ $current -eq $last ]]; then
    pchange=0
  elif [[ $last -eq 0 ]]; then
    pchange=1000000
  else
    pchange=$(( ( $delta * 100) / last ))
  fi
  echo -n "last:$last current:$current delta:$delta pchange:${pchange}%: "
  local absolute limit
  case $threshold in
    *%)
      absolute=${pchange##-} # absolute value
      limit=${threshold%%%}
      ;;
    *)
      absolute=${delta##-} # absolute value
      limit=$threshold
      ;;
  esac
  if [[ $absolute -le $limit ]]; then
    echo "within (+/-)$threshold"
    return 0
  else
    echo "outside (+/-)$threshold"
    return 1
  fi
}

steadystate() {
  local last=$1 current=$2
  if [[ $last -lt $STEADY_STATE_MINIMUM ]]; then
    echo "last:$last current:$current Waiting to reach $STEADY_STATE_MINIMUM before checking for steady-state"
    return 1
  fi
  within $last $current $STEADY_STATE_THRESHOLD
}

waitForReady() {
  logger "Recovery: Waiting ${MAXIMUM_WAIT_TIME}s for the initialization to complete"
  local lastSystemdCpuset="$(currentAffinity 1)"
  local lastDesiredCpuset="$(unrestrictedCpuset)"
  local t=0 s=10
  local lastCcount=0 ccount=0 steadyStateTime=0
  while [[ $t -lt $MAXIMUM_WAIT_TIME ]]; do
    sleep $s
    ((t += s))
    # Re-check the current affinity of systemd, in case some other process has changed it
    local systemdCpuset="$(currentAffinity 1)"
    # Re-check the unrestricted Cpuset, as the allowed set of unreserved cores may change as pods are assigned to cores
    local desiredCpuset="$(unrestrictedCpuset)"
    if [[ $systemdCpuset != $lastSystemdCpuset || $lastDesiredCpuset != $desiredCpuset ]]; then
      resetAffinity "$desiredCpuset"
      lastSystemdCpuset="$(currentAffinity 1)"
      lastDesiredCpuset="$desiredCpuset"
    fi

    # Detect steady-state pod count
    ccount=$(crictl ps | wc -l)
    if steadystate $lastCcount $ccount; then
      ((steadyStateTime += s))
      echo "Steady-state for ${steadyStateTime}s/${STEADY_STATE_WINDOW}s"
      if [[ $steadyStateTime -ge $STEADY_STATE_WINDOW ]]; then
        logger "Recovery: Steady-state (+/- $STEADY_STATE_THRESHOLD) for ${STEADY_STATE_WINDOW}s: Done"
        return 0
      fi
    else
      if [[ $steadyStateTime -gt 0 ]]; then
        echo "Resetting steady-state timer"
        steadyStateTime=0
      fi
    fi
    lastCcount=$ccount
  done
  logger "Recovery: Recovery Complete Timeout"
}

main() {
  if ! unrestrictedCpuset >&/dev/null; then
    logger "Recovery: No unrestricted Cpuset could be detected"
    return 1
  fi

  if ! restrictedCpuset >&/dev/null; then
    logger "Recovery: No restricted Cpuset has been configured.  We are already running unrestricted."
    return 0
  fi

  # Ensure we reset the CPU affinity when we exit this script for any reason
  # This way either after the timer expires or after the process is interrupted
  # via ^C or SIGTERM, we return things back to the way they should be.
  trap setRestricted EXIT

  logger "Recovery: Recovery Mode Starting"
  setUnrestricted
  waitForReady
}

if [[ "${BASH_SOURCE[0]}" = "${0}" ]]; then
  main "${@}"
  exit $?
fi

        mode: 493
        path: /usr/local/bin/accelerated-container-startup.sh
    systemd:
      units:
      - contents: |
          [Unit]
          Description=Unlocks more CPUs for critical system processes during container startup

          [Service]
          Type=simple
          ExecStart=/usr/local/bin/accelerated-container-startup.sh

          # Maximum wait time is 600s = 10m:
          Environment=MAXIMUM_WAIT_TIME=600

          # Steady-state threshold = 2%
          # Allowed values:
          #  4  - absolute pod count (+/-)
          #  4% - percent change (+/-)
          #  -1 - disable the steady-state check
          # Note: '%' must be escaped as '%%' in systemd unit files
          Environment=STEADY_STATE_THRESHOLD=2%%

          # Steady-state window = 120s
          # If the running pod count stays within the given threshold for this time
          # period, return CPU utilization to normal before the maximum wait time has
          # expires
          Environment=STEADY_STATE_WINDOW=120

          # Steady-state minimum = 40
          # Increasing this will skip any steady-state checks until the count rises above
          # this number to avoid false positives if there are some periods where the
          # count doesn't increase but we know we can't be at steady-state yet.
          Environment=STEADY_STATE_MINIMUM=40

          [Install]
          WantedBy=multi-user.target
        enabled: true
        name: accelerated-container-startup.service
      - contents: |
          [Unit]
          Description=Unlocks more CPUs for critical system processes during container shutdown
          DefaultDependencies=no

          [Service]
          Type=simple
          ExecStart=/usr/local/bin/accelerated-container-startup.sh

          # Maximum wait time is 600s = 10m:
          Environment=MAXIMUM_WAIT_TIME=600

          # Steady-state threshold
          # Allowed values:
          #  4  - absolute pod count (+/-)
          #  4% - percent change (+/-)
          #  -1 - disable the steady-state check
          # Note: '%' must be escaped as '%%' in systemd unit files
          Environment=STEADY_STATE_THRESHOLD=-1

          # Steady-state window = 60s
          # If the running pod count stays within the given threshold for this time
          # period, return CPU utilization to normal before the maximum wait time has
          # expires
          Environment=STEADY_STATE_WINDOW=60

          [Install]
          WantedBy=shutdown.target reboot.target halt.target
        enabled: true
        name: accelerated-container-shutdown.service

19.6.5.5. Automatic kernel crash dumps with kdump

kdump is a Linux kernel feature that creates a kernel crash dump when the kernel crashes. kdump is enabled with the following MachineConfig CR:

Recommended kdump configuration

apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 06-kdump-enable-master
spec:
  config:
    ignition:
      version: 3.2.0
    systemd:
      units:
      - enabled: true
        name: kdump.service
  kernelArguments:
    - crashkernel=512M

19.6.6. Recommended post-installation cluster configurations

When the cluster installation is complete, the ZTP pipeline applies the following custom resources (CRs) that are required to run DU workloads.

Note

In GitOps ZTP v4.10 and earlier, you configure UEFI secure boot with a MachineConfig CR. This is no longer required in GitOps ZTP v4.11 and later. In v4.11, you configure UEFI secure boot for single-node OpenShift clusters using Performance profile CRs. For more information, see Performance profile.

19.6.6.1. Operator namespaces and Operator groups

Single-node OpenShift clusters that run DU workloads require the following OperatorGroup and Namespace custom resources (CRs):

Local Storage Operator
Logging Operator
PTP Operator
SR-IOV Network Operator

The following YAML summarizes these CRs:

Recommended Operator Namespace and OperatorGroup configuration

apiVersion: v1
kind: Namespace
metadata:
  annotations:
    workload.openshift.io/allowed: management
  name: openshift-local-storage
---
apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: openshift-local-storage
  namespace: openshift-local-storage
spec:
  targetNamespaces:
    - openshift-local-storage
---
apiVersion: v1
kind: Namespace
metadata:
  annotations:
    workload.openshift.io/allowed: management
  name: openshift-logging
---
apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: cluster-logging
  namespace: openshift-logging
spec:
  targetNamespaces:
    - openshift-logging
---
apiVersion: v1
kind: Namespace
metadata:
  annotations:
    workload.openshift.io/allowed: management
  labels:
    openshift.io/cluster-monitoring: "true"
  name: openshift-ptp
---
apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: ptp-operators
  namespace: openshift-ptp
spec:
  targetNamespaces:
    - openshift-ptp
---
apiVersion: v1
kind: Namespace
metadata:
  annotations:
    workload.openshift.io/allowed: management
    name: openshift-sriov-network-operator
---
apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: sriov-network-operators
  namespace: openshift-sriov-network-operator
spec:
  targetNamespaces:
    - openshift-sriov-network-operator

19.6.6.2. Operator subscriptions

Single-node OpenShift clusters that run DU workloads require the following Subscription CRs. The subscription provides the location to download the following Operators:

Local Storage Operator
Logging Operator
PTP Operator
SR-IOV Network Operator

Recommended Operator subscriptions

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: cluster-logging
  namespace: openshift-logging
spec:
  channel: "stable" 1
  name: cluster-logging
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  installPlanApproval: Manual 2
---
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: local-storage-operator
  namespace: openshift-local-storage
spec:
  channel: "stable"
  installPlanApproval: Automatic
  name: local-storage-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  installPlanApproval: Manual
---
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
    name: ptp-operator-subscription
    namespace: openshift-ptp
spec:
  channel: "stable"
  name: ptp-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  installPlanApproval: Manual
---
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: sriov-network-operator-subscription
  namespace: openshift-sriov-network-operator
spec:
  channel: "stable"
  name: sriov-network-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  installPlanApproval: Manual

1: Specify the channel to get the Operator from. stable is the recommended channel.
2: Specify Manual or Automatic. In Automatic mode, the Operator automatically updates to the latest versions in the channel as they become available in the registry. In Manual mode, new Operator versions are installed only after they are explicitly approved.

19.6.6.3. Cluster logging and log forwarding

Single-node OpenShift clusters that run DU workloads require logging and log forwarding for debugging. The following example YAML illustrates the required ClusterLogging and ClusterLogForwarder CRs.

Recommended cluster logging and log forwarding configuration

apiVersion: logging.openshift.io/v1
kind: ClusterLogging 1
metadata:
  name: instance
  namespace: openshift-logging
spec:
  collection:
    logs:
      fluentd: {}
      type: fluentd
  curation:
    type: "curator"
    curator:
      schedule: "30 3 * * *"
  managementState: Managed
---
apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder 2
metadata:
  name: instance
  namespace: openshift-logging
spec:
  inputs:
    - infrastructure: {}
      name: infra-logs
  outputs:
    - name: kafka-open
      type: kafka
      url: tcp://10.46.55.190:9092/test    3
  pipelines:
    - inputRefs:
      - audit
      name: audit-logs
      outputRefs:
      - kafka-open
    - inputRefs:
      - infrastructure
      name: infrastructure-logs
      outputRefs:
      - kafka-open

1: Updates the existing ClusterLogging instance or creates the instance if it does not exist.
2: Updates the existing ClusterLogForwarder instance or creates the instance if it does not exist.
3: Specifies the URL of the Kafka server where the logs are forwarded to.

19.6.6.4. Performance profile

Single-node OpenShift clusters that run DU workloads require a Node Tuning Operator performance profile to use real-time host capabilities and services.

Note

In earlier versions of OpenShift Container Platform, the Performance Addon Operator was used to implement automatic tuning to achieve low latency performance for OpenShift applications. In OpenShift Container Platform 4.11, these functions are part of the Node Tuning Operator.

The following example PerformanceProfile CR illustrates the required cluster configuration.

Recommended performance profile configuration

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: openshift-node-performance-profile 1
spec:
  additionalKernelArgs:
    - rcupdate.rcu_normal_after_boot=0
    - "efi=runtime" 2
  cpu:
    isolated: 2-51,54-103 3
    reserved: 0-1,52-53   4
  hugepages:
    defaultHugepagesSize: 1G
    pages:
      - count: 32 5
        size: 1G  6
        node: 1 7
  machineConfigPoolSelector:
    pools.operator.machineconfiguration.openshift.io/master: ""
  nodeSelector:
    node-role.kubernetes.io/master: ""
  numa:
    topologyPolicy: "restricted"
  realTimeKernel:
    enabled: true    8

1: Ensure that the value for name matches that specified in the spec.profile.data field of TunedPerformancePatch.yaml and the status.configuration.source.name field of validatorCRs/informDuValidator.yaml.
2 3: Configures UEFI secure boot for the cluster host.
4: Set the isolated CPUs. Ensure all of the Hyper-Threading pairs match.
5: Set the reserved CPUs. When workload partitioning is enabled, system processes, kernel threads, and system container threads are restricted to these CPUs. All CPUs that are not isolated should be reserved.
6: Set the number of huge pages.
7: Set the huge page size.
8: Set enabled to true to install the real-time Linux kernel.

19.6.6.5. PTP

Single-node OpenShift clusters use Precision Time Protocol (PTP) for network time synchronization. The following example PtpConfig CR illustrates the required PTP slave configuration.

Recommended PTP configuration

apiVersion: ptp.openshift.io/v1
kind: PtpConfig
metadata:
  name: du-ptp-slave
  namespace: openshift-ptp
spec:
  profile:
    - interface: ens5f0     1
      name: slave
      phc2sysOpts: -a -r -n 24
      ptp4lConf: |
        [global]
        #
        # Default Data Set
        #
        twoStepFlag 1
        slaveOnly 0
        priority1 128
        priority2 128
        domainNumber 24
        #utc_offset 37
        clockClass 248
        clockAccuracy 0xFE
        offsetScaledLogVariance 0xFFFF
        free_running 0
        freq_est_interval 1
        dscp_event 0
        dscp_general 0
        dataset_comparison ieee1588
        G.8275.defaultDS.localPriority 128
        #
        # Port Data Set
        #
        logAnnounceInterval -3
        logSyncInterval -4
        logMinDelayReqInterval -4
        logMinPdelayReqInterval -4
        announceReceiptTimeout 3
        syncReceiptTimeout 0
        delayAsymmetry 0
        fault_reset_interval 4
        neighborPropDelayThresh 20000000
        masterOnly 0
        G.8275.portDS.localPriority 128
        #
        # Run time options
        #
        assume_two_step 0
        logging_level 6
        path_trace_enabled 0
        follow_up_info 0
        hybrid_e2e 0
        inhibit_multicast_service 0
        net_sync_monitor 0
        tc_spanning_tree 0
        tx_timestamp_timeout 1
        unicast_listen 0
        unicast_master_table 0
        unicast_req_duration 3600
        use_syslog 1
        verbose 0
        summary_interval 0
        kernel_leap 1
        check_fup_sync 0
        #
        # Servo Options
        #
        pi_proportional_const 0.0
        pi_integral_const 0.0
        pi_proportional_scale 0.0
        pi_proportional_exponent -0.3
        pi_proportional_norm_max 0.7
        pi_integral_scale 0.0
        pi_integral_exponent 0.4
        pi_integral_norm_max 0.3
        step_threshold 2.0
        first_step_threshold 0.00002
        max_frequency 900000000
        clock_servo pi
        sanity_freq_limit 200000000
        ntpshm_segment 0
        #
        # Transport options
        #
        transportSpecific 0x0
        ptp_dst_mac 01:1B:19:00:00:00
        p2p_dst_mac 01:80:C2:00:00:0E
        udp_ttl 1
        udp6_scope 0x0E
        uds_address /var/run/ptp4l
        #
        # Default interface options
        #
        clock_type OC
        network_transport L2
        delay_mechanism E2E
        time_stamping hardware
        tsproc_mode filter
        delay_filter moving_median
        delay_filter_length 10
        egressLatency 0
        ingressLatency 0
        boundary_clock_jbod 0
        #
        # Clock description
        #
        productDescription ;;
        revisionData ;;
        manufacturerIdentity 00:00:00
        userDescription ;
        timeSource 0xA0
      ptp4lOpts: -2 -s --summary_interval -4
recommend:
  - match:
      - nodeLabel: node-role.kubernetes.io/master
    priority: 4
    profile: slave

1: Sets the interface used to receive the PTP clock signal.

19.6.6.6. Extended Tuned profile

Single-node OpenShift clusters that run DU workloads require additional performance tuning configurations necessary for high-performance workloads. The following example Tuned CR extends the Tuned profile:

Recommended extended Tuned profile configuration

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: performance-patch
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
    - data: |
        [main]
        summary=Configuration changes profile inherited from performance created tuned
        include=openshift-node-performance-openshift-node-performance-profile
        [bootloader]
        cmdline_crash=nohz_full=2-51,54-103
        [sysctl]
        kernel.timer_migration=1
        [scheduler]
        group.ice-ptp=0:f:10:*:ice-ptp.*
        [service]
        service.stalld=start,enable
        service.chronyd=stop,disable
      name: performance-patch
  recommend:
    - machineConfigLabels:
        machineconfiguration.openshift.io/role: master
      priority: 19
      profile: performance-patch

19.6.6.7. SR-IOV

Single root I/O virtualization (SR-IOV) is commonly used to enable the fronthaul and the midhaul networks. The following YAML example configures SR-IOV for a single-node OpenShift cluster.

Recommended SR-IOV configuration

apiVersion: sriovnetwork.openshift.io/v1
kind: SriovOperatorConfig
metadata:
  name: default
  namespace: openshift-sriov-network-operator
spec:
  configDaemonNodeSelector:
    node-role.kubernetes.io/master: ""
  disableDrain: true
  enableInjector: true
  enableOperatorWebhook: true
---
apiVersion: sriovnetwork.openshift.io/v1
kind: SriovNetwork
metadata:
  name: sriov-nw-du-mh
  namespace: openshift-sriov-network-operator
spec:
  networkNamespace: openshift-sriov-network-operator
  resourceName: du_mh
  vlan: 150 1
---
apiVersion: sriovnetwork.openshift.io/v1
kind: SriovNetworkNodePolicy
metadata:
  name: sriov-nnp-du-mh
  namespace: openshift-sriov-network-operator
spec:
  deviceType: vfio-pci 2
  isRdma: false
  nicSelector:
    pfNames:
      - ens7f0 3
  nodeSelector:
    node-role.kubernetes.io/master: ""
  numVfs: 8 4
  priority: 10
  resourceName: du_mh
---
apiVersion: sriovnetwork.openshift.io/v1
kind: SriovNetwork
metadata:
  name: sriov-nw-du-fh
  namespace: openshift-sriov-network-operator
spec:
  networkNamespace: openshift-sriov-network-operator
  resourceName: du_fh
  vlan: 140 5
---
apiVersion: sriovnetwork.openshift.io/v1
kind: SriovNetworkNodePolicy
metadata:
  name: sriov-nnp-du-fh
  namespace: openshift-sriov-network-operator
spec:
  deviceType: netdevice 6
  isRdma: true
  nicSelector:
    pfNames:
      - ens5f0 7
  nodeSelector:
    node-role.kubernetes.io/master: ""
  numVfs: 8 8
  priority: 10
  resourceName: du_fh

1: Specifies the VLAN for the midhaul network.
2: Select either vfio-pci or netdevice, as needed.
3: Specifies the interface connected to the midhaul network.
4: Specifies the number of VFs for the midhaul network.
5: The VLAN for the fronthaul network.
6: Select either vfio-pci or netdevice, as needed.
7: Specifies the interface connected to the fronthaul network.
8: Specifies the number of VFs for the fronthaul network.

19.6.6.8. Console Operator

The console-operator installs and maintains the web console on a cluster. When the node is centrally managed the Operator is not needed and makes space for application workloads. The following Console custom resource (CR) example disables the console.

Recommended console configuration

apiVersion: operator.openshift.io/v1
kind: Console
metadata:
  annotations:
    include.release.openshift.io/ibm-cloud-managed: "false"
    include.release.openshift.io/self-managed-high-availability: "false"
    include.release.openshift.io/single-node-developer: "false"
    release.openshift.io/create-only: "true"
  name: cluster
spec:
  logLevel: Normal
  managementState: Removed
  operatorLogLevel: Normal

19.6.6.9. Grafana and Alertmanager

Single-node OpenShift clusters that run DU workloads require reduced CPU resources consumed by the OpenShift Container Platform monitoring components. The following ConfigMap custom resource (CR) disables Grafana and Alertmanager.

Recommended cluster monitoring configuration

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    grafana:
      enabled: false
    alertmanagerMain:
      enabled: false
    prometheusK8s:
       retention: 24h

19.6.6.10. Network diagnostics

Single-node OpenShift clusters that run DU workloads require less inter-pod network connectivity checks to reduce the additional load created by these pods. The following custom resource (CR) disables these checks.

Recommended network diagnostics configuration

apiVersion: operator.openshift.io/v1
kind: Network
metadata:
  name: cluster
spec:
  disableNetworkDiagnostics: true

Additional resources

Deploying far edge sites using ZTP

19.7. Validating single-node OpenShift cluster tuning for vDU application workloads

Before you can deploy virtual distributed unit (vDU) applications, you need to tune and configure the cluster host firmware and various other cluster configuration settings. Use the following information to validate the cluster configuration to support vDU workloads.

Additional resources

For more information about single-node OpenShift clusters tuned for vDU application deployments, see Reference configuration for deploying vDUs on single-node OpenShift.

19.7.1. Recommended firmware configuration for vDU cluster hosts

Use the following table as the basis to configure the cluster host firmware for vDU applications running on OpenShift Container Platform 4.10.

Note

The following table is a general recommendation for vDU cluster host firmware configuration. Exact firmware settings will depend on your requirements and specific hardware platform. Automatic setting of firmware is not handled by the zero touch provisioning pipeline.

Table 19.7. Recommended cluster host firmware settings

Firmware setting	Configuration	Description
HyperTransport (HT)	Enabled	HyperTransport (HT) bus is a bus technology developed by AMD. HT provides a high-speed link between the components in the host memory and other system peripherals.
UEFI	Enabled	Enable booting from UEFI for the vDU host.
CPU Power and Performance Policy	Performance	Set CPU Power and Performance Policy to optimize the system for performance over energy efficiency.
Uncore Frequency Scaling	Disabled	Disable Uncore Frequency Scaling to prevent the voltage and frequency of non-core parts of the CPU from being set independently.
Uncore Frequency	Maximum	Sets the non-core parts of the CPU such as cache and memory controller to their maximum possible frequency of operation.
Performance P-limit	Disabled	Disable Performance P-limit to prevent the Uncore frequency coordination of processors.
Enhanced Intel® SpeedStep Tech	Enabled	Enable Enhanced Intel SpeedStep to allow the system to dynamically adjust processor voltage and core frequency that decreases power consumption and heat production in the host.
Intel® Turbo Boost Technology	Enabled	Enable Turbo Boost Technology for Intel-based CPUs to automatically allow processor cores to run faster than the rated operating frequency if they are operating below power, current, and temperature specification limits.
Intel Configurable TDP	Enabled	Enables Thermal Design Power (TDP) for the CPU.
Configurable TDP Level	Level 2	TDP level sets the CPU power consumption required for a particular performance rating. TDP level 2 sets the CPU to the most stable performance level at the cost of power consumption.
Energy Efficient Turbo	Disabled	Disable Energy Efficient Turbo to prevent the processor from using an energy-efficiency based policy.
Hardware P-States	Disabled	Disable `P-states` (performance states) to optimize the operating system and CPU for performance over power consumption.
Package C-State	C0/C1 state	Use C0 or C1 states to set the processor to a fully active state (C0) or to stop CPU internal clocks running in software (C1).
C1E	Disabled	CPU Enhanced Halt (C1E) is a power saving feature in Intel chips. Disabling C1E prevents the operating system from sending a halt command to the CPU when inactive.
Processor C6	Disabled	C6 power-saving is a CPU feature that automatically disables idle CPU cores and cache. Disabling C6 improves system performance.
Sub-NUMA Clustering	Disabled	Sub-NUMA clustering divides the processor cores, cache, and memory into multiple NUMA domains. Disabling this option can increase performance for latency-sensitive workloads.

Note

Enable global SR-IOV and VT-d settings in the firmware for the host. These settings are relevant to bare-metal environments.

19.7.2. Recommended cluster configurations to run vDU applications

Clusters running virtualized distributed unit (vDU) applications require a highly tuned and optimized configuration. The following information describes the various elements that you require to support vDU workloads in OpenShift Container Platform 4.10 clusters.

19.7.2.1. Recommended cluster MachineConfig CRs

The following MachineConfig CRs configure the cluster host:

Table 19.8. Recommended MachineConfig CRs

CR filename	Description
`02-workload-partitioning.yaml`	Configures workload partitioning for the cluster. Apply this `MachineConfig` CR when you install the cluster.
`MachineConfigSctp.yaml`	Loads the SCTP kernel module. This `MachineConfig` CR is optional and can be omitted if you do not require this kernel module.
`MachineConfigContainerMountNS.yaml`	Configures the container mount namespace and kubelet conf.
`MachineConfigAcceleratedStartup.yaml`	Configures accelerated startup for the cluster.
`06-kdump-master.yaml`, `06-kdump-worker.yaml`	Configures `kdump` for the cluster.

19.7.2.2. Recommended cluster Operators

The following Operators are required for clusters running vDU applications and are a part of the baseline reference configuration:

Node Tuning Operator (NTO). NTO packages functionality that was previously delivered with the Performance Addon Operator, which is now a part of NTO.
PTP Operator
SR-IOV Network Operator
Red Hat OpenShift Logging Operator
Local Storage Operator

19.7.2.3. Recommended cluster kernel configuration

Always use the latest supported realtime kernel version in your cluster. You should also ensure that the following configurations are applied in the cluster:

Ensure the following additionalKernelArgs are set in the cluster performance profile:

spec:
  additionalKernelArgs:
  - "idle=poll"
  - "rcupdate.rcu_normal_after_boot=0"
  - "efi=runtime"

Ensure that the performance-patch profile in the Tuned CR configures the correct CPU isolation set that matches the isolated CPU set in the related PerformanceProfile CR, for example:

spec:
  profile:
    - name: performance-patch
      # The 'include' line must match the associated PerformanceProfile name
      # And the cmdline_crash CPU set must match the 'isolated' set in the associated PerformanceProfile
      data: |
        [main]
        summary=Configuration changes profile inherited from performance created tuned
        include=openshift-node-performance-openshift-node-performance-profile
        [bootloader]
        cmdline_crash=nohz_full=2-51,54-103 1
        [sysctl]
        kernel.timer_migration=1
        [scheduler]
        group.ice-ptp=0:f:10:*:ice-ptp.*
        [service]
        service.stalld=start,enable
        service.chronyd=stop,disable

1: Listed CPUs depend on the host hardware configuration, specifically the number of available CPUs in the system and the CPU topology.

19.7.2.4. Checking the realtime kernel version

Always use the latest version of the realtime kernel in your OpenShift Container Platform clusters. If you are unsure about the kernel version that is in use in the cluster, you can compare the current realtime kernel version to the release version with the following procedure.

Prerequisites

You have installed the OpenShift CLI (oc).
You are logged in as a user with cluster-admin privileges.
You have installed podman.

Procedure

Run the following command to get the cluster version:

$ OCP_VERSION=$(oc get clusterversion version -o jsonpath='{.status.desired.version}{"\n"}')

Get the release image SHA number:

$ DTK_IMAGE=$(oc adm release info --image-for=driver-toolkit quay.io/openshift-release-dev/ocp-release:$OCP_VERSION-x86_64)

Run the release image container and extract the kernel version that is packaged with cluster’s current release:
```
$ podman run --rm $DTK_IMAGE rpm -qa | grep 'kernel-rt-core-' | sed 's#kernel-rt-core-##'
```
Example output
```
4.18.0-305.49.1.rt7.121.el8_4.x86_64
```
This is the default realtime kernel version that ships with the release.
Note
The realtime kernel is denoted by the string .rt in the kernel version.

Verification

Check that the kernel version listed for the cluster’s current release matches actual realtime kernel that is running in the cluster. Run the following commands to check the running realtime kernel version:

Open a remote shell connection to the cluster node:
```
$ oc debug node/<node_name>
```

Check the realtime kernel version:

sh-4.4# uname -r

Example output

4.18.0-305.49.1.rt7.121.el8_4.x86_64

19.7.3. Checking that the recommended cluster configurations are applied

You can check that clusters are running the correct configuration. The following procedure describes how to check the various configurations that you require to deploy a DU application in OpenShift Container Platform 4.10 clusters.

Prerequisites

You have deployed a cluster and tuned it for vDU workloads.
You have installed the OpenShift CLI (oc).
You have logged in as a user with cluster-admin privileges.

Procedure

Check that the default Operator Hub sources are disabled. Run the following command:
```
$ oc get operatorhub cluster -o yaml
```
Example output
```
spec:
    disableAllDefaultSources: true
```
Check that all required CatalogSource resources are annotated for workload partitioning (PreferredDuringScheduling) by running the following command:
```
$ oc get catalogsource -A -o jsonpath='{range .items[*]}{.metadata.name}{" -- "}{.metadata.annotations.target\.workload\.openshift\.io/management}{"\n"}{end}'
```
Example output
```
certified-operators -- {"effect": "PreferredDuringScheduling"}
community-operators -- {"effect": "PreferredDuringScheduling"}
ran-operators 1
redhat-marketplace -- {"effect": "PreferredDuringScheduling"}
redhat-operators -- {"effect": "PreferredDuringScheduling"}
```
1
CatalogSource resources that are not annotated are also returned. In this example, the ran-operators CatalogSource resource is not annotated and does not have the PreferredDuringScheduling annotation.
Note
In a properly configured vDU cluster, only a single annotated catalog source is listed.
Check that all applicable OpenShift Container Platform Operator namespaces are annotated for workload partitioning. This includes all Operators installed with core OpenShift Container Platform and the set of additional Operators included in the reference DU tuning configuration. Run the following command:
```
$ oc get namespaces -A -o jsonpath='{range .items[*]}{.metadata.name}{" -- "}{.metadata.annotations.workload\.openshift\.io/allowed}{"\n"}{end}'
```
Example output
```
default --
openshift-apiserver -- management
openshift-apiserver-operator -- management
openshift-authentication -- management
openshift-authentication-operator -- management
```
Important
Additional Operators must not be annotated for workload partitioning. In the output from the previous command, additional Operators should be listed without any value on the right-hand side of the -- separator.

Check that the ClusterLogging configuration is correct. Run the following commands:

Validate that the appropriate input and output logs are configured:

$ oc get -n openshift-logging ClusterLogForwarder instance -o yaml

Example output

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  creationTimestamp: "2022-07-19T21:51:41Z"
  generation: 1
  name: instance
  namespace: openshift-logging
  resourceVersion: "1030342"
  uid: 8c1a842d-80c5-447a-9150-40350bdf40f0
spec:
  inputs:
  - infrastructure: {}
    name: infra-logs
  outputs:
  - name: kafka-open
    type: kafka
    url: tcp://10.46.55.190:9092/test
  pipelines:
  - inputRefs:
    - audit
    name: audit-logs
    outputRefs:
    - kafka-open
  - inputRefs:
    - infrastructure
    name: infrastructure-logs
    outputRefs:
    - kafka-open
...

Check that the curation schedule is appropriate for your application:

$ oc get -n openshift-logging clusterloggings.logging.openshift.io instance -o yaml

Example output

apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  creationTimestamp: "2022-07-07T18:22:56Z"
  generation: 1
  name: instance
  namespace: openshift-logging
  resourceVersion: "235796"
  uid: ef67b9b8-0e65-4a10-88ff-ec06922ea796
spec:
  collection:
    logs:
      fluentd: {}
      type: fluentd
  curation:
    curator:
      schedule: 30 3 * * *
    type: curator
  managementState: Managed
...

Check that the web console is disabled (managementState: Removed) by running the following command:
```
$ oc get consoles.operator.openshift.io cluster -o jsonpath="{ .spec.managementState }"
```
Example output
```
Removed
```

Check that chronyd is disabled on the cluster node by running the following commands:

$ oc debug node/<node_name>

Check the status of chronyd on the node:

sh-4.4# chroot /host

sh-4.4# systemctl status chronyd

Example output

● chronyd.service - NTP client/server
    Loaded: loaded (/usr/lib/systemd/system/chronyd.service; disabled; vendor preset: enabled)
    Active: inactive (dead)
      Docs: man:chronyd(8)
            man:chrony.conf(5)

Check that the PTP interface is successfully synchronized to the primary clock using a remote shell connection to the linuxptp-daemon container and the PTP Management Client (pmc) tool:

Set the $PTP_POD_NAME variable with the name of the linuxptp-daemon pod by running the following command:
```
$ PTP_POD_NAME=$(oc get pods -n openshift-ptp -l app=linuxptp-daemon -o name)
```

Run the following command to check the sync status of the PTP device:

$ oc -n openshift-ptp rsh -c linuxptp-daemon-container ${PTP_POD_NAME} pmc -u -f /var/run/ptp4l.0.config -b 0 'GET PORT_DATA_SET'

Example output

sending: GET PORT_DATA_SET
  3cecef.fffe.7a7020-1 seq 0 RESPONSE MANAGEMENT PORT_DATA_SET
    portIdentity            3cecef.fffe.7a7020-1
    portState               SLAVE
    logMinDelayReqInterval  -4
    peerMeanPathDelay       0
    logAnnounceInterval     1
    announceReceiptTimeout  3
    logSyncInterval         0
    delayMechanism          1
    logMinPdelayReqInterval 0
    versionNumber           2
  3cecef.fffe.7a7020-2 seq 0 RESPONSE MANAGEMENT PORT_DATA_SET
    portIdentity            3cecef.fffe.7a7020-2
    portState               LISTENING
    logMinDelayReqInterval  0
    peerMeanPathDelay       0
    logAnnounceInterval     1
    announceReceiptTimeout  3
    logSyncInterval         0
    delayMechanism          1
    logMinPdelayReqInterval 0
    versionNumber           2

Run the following pmc command to check the PTP clock status:

$ oc -n openshift-ptp rsh -c linuxptp-daemon-container ${PTP_POD_NAME} pmc -u -f /var/run/ptp4l.0.config -b 0 'GET TIME_STATUS_NP'

Example output

sending: GET TIME_STATUS_NP
  3cecef.fffe.7a7020-0 seq 0 RESPONSE MANAGEMENT TIME_STATUS_NP
    master_offset              10 1
    ingress_time               1657275432697400530
    cumulativeScaledRateOffset +0.000000000
    scaledLastGmPhaseChange    0
    gmTimeBaseIndicator        0
    lastGmPhaseChange          0x0000'0000000000000000.0000
    gmPresent                  true 2
    gmIdentity                 3c2c30.ffff.670e00

1: master_offset should be between -100 and 100 ns.
2: Indicates that the PTP clock is synchronized to a master, and the local clock is not the grandmaster clock.

Check that the expected master offset value corresponding to the value in /var/run/ptp4l.0.config is found in the linuxptp-daemon-container log:

$ oc logs $PTP_POD_NAME -n openshift-ptp -c linuxptp-daemon-container

Example output

phc2sys[56020.341]: [ptp4l.1.config] CLOCK_REALTIME phc offset  -1731092 s2 freq -1546242 delay    497
ptp4l[56020.390]: [ptp4l.1.config] master offset         -2 s2 freq   -5863 path delay       541
ptp4l[56020.390]: [ptp4l.0.config] master offset         -8 s2 freq  -10699 path delay       533

Check that the SR-IOV configuration is correct by running the following commands:

Check that the disableDrain value in the SriovOperatorConfig resource is set to true:

$ oc get sriovoperatorconfig -n openshift-sriov-network-operator default -o jsonpath="{.spec.disableDrain}{'\n'}"

Example output

true

Check that the SriovNetworkNodeState sync status is Succeeded by running the following command:

$ oc get SriovNetworkNodeStates -n openshift-sriov-network-operator -o jsonpath="{.items[*].status.syncStatus}{'\n'}"

Example output

Succeeded

Verify that the expected number and configuration of virtual functions (Vfs) under each interface configured for SR-IOV is present and correct in the .status.interfaces field. For example:

$ oc get SriovNetworkNodeStates -n openshift-sriov-network-operator -o yaml

Example output

apiVersion: v1
items:
- apiVersion: sriovnetwork.openshift.io/v1
  kind: SriovNetworkNodeState
...
  status:
    interfaces:
    ...
    - Vfs:
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.0
        vendor: "8086"
        vfID: 0
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.1
        vendor: "8086"
        vfID: 1
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.2
        vendor: "8086"
        vfID: 2
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.3
        vendor: "8086"
        vfID: 3
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.4
        vendor: "8086"
        vfID: 4
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.5
        vendor: "8086"
        vfID: 5
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.6
        vendor: "8086"
        vfID: 6
      - deviceID: 154c
        driver: vfio-pci
        pciAddress: 0000:3b:0a.7
        vendor: "8086"
        vfID: 7

Check that the cluster performance profile is correct. The cpu and hugepages sections will vary depending on your hardware configuration. Run the following command:

$ oc get PerformanceProfile openshift-node-performance-profile -o yaml

Example output

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  creationTimestamp: "2022-07-19T21:51:31Z"
  finalizers:
  - foreground-deletion
  generation: 1
  name: openshift-node-performance-profile
  resourceVersion: "33558"
  uid: 217958c0-9122-4c62-9d4d-fdc27c31118c
spec:
  additionalKernelArgs:
  - idle=poll
  - rcupdate.rcu_normal_after_boot=0
  - efi=runtime
  cpu:
    isolated: 2-51,54-103
    reserved: 0-1,52-53
  hugepages:
    defaultHugepagesSize: 1G
    pages:
    - count: 32
      size: 1G
  machineConfigPoolSelector:
    pools.operator.machineconfiguration.openshift.io/master: ""
  net:
    userLevelNetworking: true
  nodeSelector:
    node-role.kubernetes.io/master: ""
  numa:
    topologyPolicy: restricted
  realTimeKernel:
    enabled: true
status:
  conditions:
  - lastHeartbeatTime: "2022-07-19T21:51:31Z"
    lastTransitionTime: "2022-07-19T21:51:31Z"
    status: "True"
    type: Available
  - lastHeartbeatTime: "2022-07-19T21:51:31Z"
    lastTransitionTime: "2022-07-19T21:51:31Z"
    status: "True"
    type: Upgradeable
  - lastHeartbeatTime: "2022-07-19T21:51:31Z"
    lastTransitionTime: "2022-07-19T21:51:31Z"
    status: "False"
    type: Progressing
  - lastHeartbeatTime: "2022-07-19T21:51:31Z"
    lastTransitionTime: "2022-07-19T21:51:31Z"
    status: "False"
    type: Degraded
  runtimeClass: performance-openshift-node-performance-profile
  tuned: openshift-cluster-node-tuning-operator/openshift-node-performance-openshift-node-performance-profile

Note

CPU settings are dependent on the number of cores available on the server and should align with workload partitioning settings. hugepages configuration is server and application dependent.

Check that the PerformanceProfile was successfully applied to the cluster by running the following command:

$ oc get performanceprofile openshift-node-performance-profile -o jsonpath="{range .status.conditions[*]}{ @.type }{' -- '}{@.status}{'\n'}{end}"

Example output

Available -- True
Upgradeable -- True
Progressing -- False
Degraded -- False

Check the Tuned performance patch settings by running the following command:

$ oc get tuneds.tuned.openshift.io -n openshift-cluster-node-tuning-operator performance-patch -o yaml

Example output

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  creationTimestamp: "2022-07-18T10:33:52Z"
  generation: 1
  name: performance-patch
  namespace: openshift-cluster-node-tuning-operator
  resourceVersion: "34024"
  uid: f9799811-f744-4179-bf00-32d4436c08fd
spec:
  profile:
  - data: |
      [main]
      summary=Configuration changes profile inherited from performance created tuned
      include=openshift-node-performance-openshift-node-performance-profile
      [bootloader]
      cmdline_crash=nohz_full=2-23,26-47 1
      [sysctl]
      kernel.timer_migration=1
      [scheduler]
      group.ice-ptp=0:f:10:*:ice-ptp.*
      [service]
      service.stalld=start,enable
      service.chronyd=stop,disable
    name: performance-patch
  recommend:
  - machineConfigLabels:
      machineconfiguration.openshift.io/role: master
    priority: 19
    profile: performance-patch

1: The cpu list in cmdline=nohz_full= will vary based on your hardware configuration.

Check that cluster networking diagnostics are disabled by running the following command:
```
$ oc get networks.operator.openshift.io cluster -o jsonpath='{.spec.disableNetworkDiagnostics}'
```
Example output
```
true
```

Check that the Kubelet housekeeping interval is tuned to slower rate. This is set in the containerMountNS machine config. Run the following command:

$ oc describe machineconfig container-mount-namespace-and-kubelet-conf-master | grep OPENSHIFT_MAX_HOUSEKEEPING_INTERVAL_DURATION

Example output

Environment="OPENSHIFT_MAX_HOUSEKEEPING_INTERVAL_DURATION=60s"

Check that Grafana and alertManagerMain are disabled and that the Prometheus retention period is set to 24h by running the following command:
```
$ oc get configmap cluster-monitoring-config -n openshift-monitoring -o jsonpath="{ .data.config\.yaml }"
```
Example output
```
grafana:
  enabled: false
alertmanagerMain:
  enabled: false
prometheusK8s:
   retention: 24h
```
1. Use the following commands to verify that Grafana and alertManagerMain routes are not found in the cluster:
```
$ oc get route -n openshift-monitoring alertmanager-main
```
```
$ oc get route -n openshift-monitoring grafana
```
  Both queries should return Error from server (NotFound) messages.
Check that there is a minimum of 4 CPUs allocated as reserved for each of the PerformanceProfile, Tuned performance-patch, workload partitioning, and kernel command line arguments by running the following command:
```
$ oc get performanceprofile -o jsonpath="{ .items[0].spec.cpu.reserved }"
```
Example output
```
0-1,52-53
```
Note
Depending on your workload requirements, you might require additional reserved CPUs to be allocated.

19.8. Advanced managed cluster configuration with SiteConfig resources

You can use SiteConfig custom resources (CRs) to deploy custom functionality and configurations in your managed clusters at installation time.

19.8.1. Customizing extra installation manifests in the ZTP GitOps pipeline

You can define a set of extra manifests for inclusion in the installation phase of the zero touch provisioning (ZTP) GitOps pipeline. These manifests are linked to the SiteConfig custom resources (CRs) and are applied to the cluster during installation. Including MachineConfig CRs at install time makes the installation process more efficient.

Prerequisites

Create a Git repository where you manage your custom site configuration data. The repository must be accessible from the hub cluster and be defined as a source repository for the Argo CD application.

Procedure

Create a set of extra manifest CRs that the ZTP pipeline uses to customize the cluster installs.
In your custom /siteconfig directory, create an /extra-manifest folder for your extra manifests. The following example illustrates a sample /siteconfig with /extra-manifest folder:
```
siteconfig
├── site1-sno-du.yaml
├── site2-standard-du.yaml
└── extra-manifest
    └── 01-example-machine-config.yaml
```
Add your custom extra manifest CRs to the siteconfig/extra-manifest directory.

In your SiteConfig CR, enter the directory name in the extraManifestPath field, for example:

clusters:
- clusterName: "example-sno"
  networkType: "OVNKubernetes"
  extraManifestPath: extra-manifest

Save the SiteConfig CRs and /extra-manifest CRs and push them to the site configuration repo.

The ZTP pipeline appends the CRs in the /extra-manifest directory to the default set of extra manifests during cluster provisioning.

19.8.2. Filtering custom resources using SiteConfig filters

By using filters, you can easily customize SiteConfig custom resources (CRs) to include or exclude other CRs for use in the installation phase of the zero touch provisioning (ZTP) GitOps pipeline.

You can specify an inclusionDefault value of include or exclude for the SiteConfig CR, along with a list of the specific extraManifest RAN CRs that you want to include or exclude. Setting inclusionDefault to include makes the ZTP pipeline apply all the files in /source-crs/extra-manifest during installation. Setting inclusionDefault to exclude does the opposite.

You can exclude individual CRs from the /source-crs/extra-manifest folder that are otherwise included by default. The following example configures a custom single-node OpenShift SiteConfig CR to exclude the /source-crs/extra-manifest/03-sctp-machine-config-worker.yaml CR at installation time.

Some additional optional filtering scenarios are also described.

Prerequisites

You configured the hub cluster for generating the required installation and policy CRs.
You created a Git repository where you manage your custom site configuration data. The repository must be accessible from the hub cluster and be defined as a source repository for the Argo CD application.

Procedure

To prevent the ZTP pipeline from applying the 03-sctp-machine-config-worker.yaml CR file, apply the following YAML in the SiteConfig CR:

apiVersion: ran.openshift.io/v1
kind: SiteConfig
metadata:
  name: "site1-sno-du"
  namespace: "site1-sno-du"
spec:
  baseDomain: "example.com"
  pullSecretRef:
    name: "assisted-deployment-pull-secret"
  clusterImageSetNameRef: "openshift-4.10"
  sshPublicKey: "<ssh_public_key>"
  clusters:
- clusterName: "site1-sno-du"
  extraManifests:
    filter:
      exclude:
        - 03-sctp-machine-config-worker.yaml

The ZTP pipeline skips the 03-sctp-machine-config-worker.yaml CR during installation. All other CRs in /source-crs/extra-manifest are applied.

Save the SiteConfig CR and push the changes to the site configuration repository.
The ZTP pipeline monitors and adjusts what CRs it applies based on the SiteConfig filter instructions.
Optional: To prevent the ZTP pipeline from applying all the /source-crs/extra-manifest CRs during cluster installation, apply the following YAML in the SiteConfig CR:
```
- clusterName: "site1-sno-du"
  extraManifests:
    filter:
      inclusionDefault: exclude
```
Optional: To exclude all the /source-crs/extra-manifest RAN CRs and instead include a custom CR file during installation, edit the custom SiteConfig CR to set the custom manifests folder and the include file, for example:
```
clusters:
- clusterName: "site1-sno-du"
  extraManifestPath: "<custom_manifest_folder>" 1
  extraManifests:
    filter:
      inclusionDefault: exclude  2
      include:
        - custom-sctp-machine-config-worker.yaml
```
1
Replace <custom_manifest_folder> with the name of the folder that contains the custom installation CRs, for example, user-custom-manifest/.
2
Set inclusionDefault to exclude to prevent the ZTP pipeline from applying the files in /source-crs/extra-manifest during installation.
The following example illustrates the custom folder structure:
```
siteconfig
  ├── site1-sno-du.yaml
  └── user-custom-manifest
        └── custom-sctp-machine-config-worker.yaml
```

19.9. Advanced managed cluster configuration with PolicyGenTemplate resources

You can use PolicyGenTemplate CRs to deploy custom functionality in your managed clusters.

19.9.1. Deploying additional changes to clusters

If you require cluster configuration changes outside of the base GitOps ZTP pipeline configuration, there are three options:

Apply the additional configuration after the ZTP pipeline is complete: When the GitOps ZTP pipeline deployment is complete, the deployed cluster is ready for application workloads. At this point, you can install additional Operators and apply configurations specific to your requirements. Ensure that additional configurations do not negatively affect the performance of the platform or allocated CPU budget.
Add content to the ZTP library: The base source custom resources (CRs) that you deploy with the GitOps ZTP pipeline can be augmented with custom content as required.
Create extra manifests for the cluster installation: Extra manifests are applied during installation and make the installation process more efficient.

Important

Providing additional source CRs or modifying existing source CRs can significantly impact the performance or CPU profile of OpenShift Container Platform.

Additional resources

See Customizing extra installation manifests in the ZTP GitOps pipeline for information about adding extra manifests.

19.9.2. Using PolicyGenTemplate CRs to override source CRs content

PolicyGenTemplate custom resources (CRs) allow you to overlay additional configuration details on top of the base source CRs provided with the GitOps plugin in the ztp-site-generate container. You can think of PolicyGenTemplate CRs as a logical merge or patch to the base CR. Use PolicyGenTemplate CRs to update a single field of the base CR, or overlay the entire contents of the base CR. You can update values and insert fields that are not in the base CR.

The following example procedure describes how to update fields in the generated PerformanceProfile CR for the reference configuration based on the PolicyGenTemplate CR in the group-du-sno-ranGen.yaml file. Use the procedure as a basis for modifying other parts of the PolicyGenTemplate based on your requirements.

Prerequisites

Create a Git repository where you manage your custom site configuration data. The repository must be accessible from the hub cluster and be defined as a source repository for Argo CD.

Procedure

Review the baseline source CR for existing content. You can review the source CRs listed in the reference PolicyGenTemplate CRs by extracting them from the zero touch provisioning (ZTP) container.
1. Create an /out folder:
```
$ mkdir -p ./out
```
2. Extract the source CRs:
```
$ podman run --log-driver=none --rm registry.redhat.io/openshift4/ztp-site-generate-rhel8:v{product-version}.1 extract /home/ztp --tar | tar x -C ./out
```

Review the baseline PerformanceProfile CR in ./out/source-crs/PerformanceProfile.yaml:

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: $name
  annotations:
    ran.openshift.io/ztp-deploy-wave: "10"
spec:
  additionalKernelArgs:
  - "idle=poll"
  - "rcupdate.rcu_normal_after_boot=0"
  cpu:
    isolated: $isolated
    reserved: $reserved
  hugepages:
    defaultHugepagesSize: $defaultHugepagesSize
    pages:
      - size: $size
        count: $count
        node: $node
  machineConfigPoolSelector:
    pools.operator.machineconfiguration.openshift.io/$mcp: ""
  net:
    userLevelNetworking: true
  nodeSelector:
    node-role.kubernetes.io/$mcp: ''
  numa:
    topologyPolicy: "restricted"
  realTimeKernel:
    enabled: true

Note

Any fields in the source CR which contain $… are removed from the generated CR if they are not provided in the PolicyGenTemplate CR.

Update the PolicyGenTemplate entry for PerformanceProfile in the group-du-sno-ranGen.yaml reference file. The following example PolicyGenTemplate CR stanza supplies appropriate CPU specifications, sets the hugepages configuration, and adds a new field that sets globallyDisableIrqLoadBalancing to false.

- fileName: PerformanceProfile.yaml
  policyName: "config-policy"
  metadata:
    name: openshift-node-performance-profile
  spec:
    cpu:
      # These must be tailored for the specific hardware platform
      isolated: "2-19,22-39"
      reserved: "0-1,20-21"
    hugepages:
      defaultHugepagesSize: 1G
      pages:
        - size: 1G
          count: 10
    globallyDisableIrqLoadBalancing: false

Commit the PolicyGenTemplate change in Git, and then push to the Git repository being monitored by the GitOps ZTP argo CD application.

Example output

The ZTP application generates an RHACM policy that contains the generated PerformanceProfile CR. The contents of that CR are derived by merging the metadata and spec contents from the PerformanceProfile entry in the PolicyGenTemplate onto the source CR. The resulting CR has the following content:

---
apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
    name: openshift-node-performance-profile
spec:
    additionalKernelArgs:
        - idle=poll
        - rcupdate.rcu_normal_after_boot=0
    cpu:
        isolated: 2-19,22-39
        reserved: 0-1,20-21
    globallyDisableIrqLoadBalancing: false
    hugepages:
        defaultHugepagesSize: 1G
        pages:
            - count: 10
              size: 1G
    machineConfigPoolSelector:
        pools.operator.machineconfiguration.openshift.io/master: ""
    net:
        userLevelNetworking: true
    nodeSelector:
        node-role.kubernetes.io/master: ""
    numa:
        topologyPolicy: restricted
    realTimeKernel:
        enabled: true

Note

In the /source-crs folder that you extract from the ztp-site-generate container, the $ syntax is not used for template substitution as implied by the syntax. Rather, if the policyGen tool sees the $ prefix for a string and you do not specify a value for that field in the related PolicyGenTemplate CR, the field is omitted from the output CR entirely.

An exception to this is the $mcp variable in /source-crs YAML files that is substituted with the specified value for mcp from the PolicyGenTemplate CR. For example, in example/policygentemplates/group-du-standard-ranGen.yaml, the value for mcp is worker:

spec:
  bindingRules:
    group-du-standard: ""
  mcp: "worker"

The policyGen tool replace instances of $mcp with worker in the output CRs.

19.9.3. Adding new content to the GitOps ZTP pipeline

The source CRs in the GitOps ZTP site generator container provide a set of critical features and node tuning settings for RAN Distributed Unit (DU) applications. These are applied to the clusters that you deploy with ZTP. To add or modify existing source CRs in the ztp-site-generate container, rebuild the ztp-site-generate container and make it available to the hub cluster, typically from the disconnected registry associated with the hub cluster. Any valid OpenShift Container Platform CR can be added.

Perform the following procedure to add new content to the ZTP pipeline.

Procedure

Create a directory containing a Containerfile and the source CR YAML files that you want to include in the updated ztp-site-generate container, for example:
```
ztp-update/
├── example-cr1.yaml
├── example-cr2.yaml
└── ztp-update.in
```

Add the following content to the ztp-update.in Containerfile:

FROM registry.redhat.io/openshift4/ztp-site-generate-rhel8:v4.10

ADD example-cr2.yaml /kustomize/plugin/ran.openshift.io/v1/policygentemplate/source-crs/
ADD example-cr1.yaml /kustomize/plugin/ran.openshift.io/v1/policygentemplate/source-crs/

Open a terminal at the ztp-update/ folder and rebuild the container:
```
$ podman build -t ztp-site-generate-rhel8-custom:v4.10-custom-1
```

Push the built container image to your disconnected registry, for example:

$ podman push localhost/ztp-site-generate-rhel8-custom:v4.10-custom-1 registry.example.com:5000/ztp-site-generate-rhel8-custom:v4.10-custom-1

Patch the Argo CD instance on the hub cluster to point to the newly built container image:

$ oc patch -n openshift-gitops argocd openshift-gitops --type=json -p '[{"op": "replace", "path":"/spec/repo/initContainers/0/image", "value": "registry.example.com:5000/ztp-site-generate-rhel8-custom:v4.10-custom-1"} ]'

When the Argo CD instance is patched, the openshift-gitops-repo-server pod automatically restarts.

Verification

Verify that the new openshift-gitops-repo-server pod has completed initialization and that the previous repo pod is terminated:
```
$ oc get pods -n openshift-gitops | grep openshift-gitops-repo-server
```
Example output
```
openshift-gitops-server-7df86f9774-db682          1/1     Running   	     1          28s
```
You must wait until the new openshift-gitops-repo-server pod has completed initialization and the previous pod is terminated before the newly added container image content is available.

Additional resources

Alternatively, you can patch the ArgoCD instance as described in Configuring the hub cluster with ArgoCD by modifying argocd-openshift-gitops-patch.json with an updated initContainer image before applying the patch file.

19.9.4. Signalling ZTP cluster deployment completion with validator inform policies

Create a validator inform policy that signals when the zero touch provisioning (ZTP) installation and configuration of the deployed cluster is complete. This policy can be used for deployments of single-node OpenShift clusters, three-node clusters, and standard clusters.

Procedure

Create a standalone PolicyGenTemplate custom resource (CR) that contains the source file validatorCRs/informDuValidator.yaml. You only need one standalone PolicyGenTemplate CR for each cluster type. For example, this CR applies a validator inform policy for single-node OpenShift clusters:
Example single-node cluster validator inform policy CR (group-du-sno-validator-ranGen.yaml)
```
apiVersion: ran.openshift.io/v1
kind: PolicyGenTemplate
metadata:
  name: "group-du-sno-validator" 1
  namespace: "ztp-group" 2
spec:
  bindingRules:
    group-du-sno: "" 3
  bindingExcludedRules:
    ztp-done: "" 4
  mcp: "master" 5
  sourceFiles:
    - fileName: validatorCRs/informDuValidator.yaml
      remediationAction: inform 6
      policyName: "du-policy" 7
```
1
The name of PolicyGenTemplates object. This name is also used as part of the names for the placementBinding, placementRule, and policy that are created in the requested namespace.
2
This value should match the namespace used in the group PolicyGenTemplates.
3
The group-du-* label defined in bindingRules must exist in the SiteConfig files.
4
The label defined in bindingExcludedRules must be`ztp-done:`. The ztp-done label is used in coordination with the Topology Aware Lifecycle Manager.
5
mcp defines the MachineConfigPool object that is used in the source file validatorCRs/informDuValidator.yaml. It should be master for single node and three-node cluster deployments and worker for standard cluster deployments.
6
Optional. The default value is inform.
7
This value is used as part of the name for the generated RHACM policy. The generated validator policy for the single node example is group-du-sno-validator-du-policy.
Commit the PolicyGenTemplate CR file in your Git repository and push the changes.

Additional resources

Upgrading GitOps ZTP

19.9.5. Configuring PTP fast events using PolicyGenTemplate CRs

You can configure PTP fast events for vRAN clusters that are deployed using the GitOps Zero Touch Provisioning (ZTP) pipeline. Use PolicyGenTemplate custom resources (CRs) as the basis to create a hierarchy of configuration files tailored to your specific site requirements.

Prerequisites

Create a Git repository where you manage your custom site configuration data.

Procedure

Add the following YAML into .spec.sourceFiles in the common-ranGen.yaml file to configure the AMQP Operator:

#AMQ interconnect operator for fast events
- fileName: AmqSubscriptionNS.yaml
  policyName: "subscriptions-policy"
- fileName: AmqSubscriptionOperGroup.yaml
  policyName: "subscriptions-policy"
- fileName: AmqSubscription.yaml
  policyName: "subscriptions-policy"

Apply the following PolicyGenTemplate changes to group-du-3node-ranGen.yaml, group-du-sno-ranGen.yaml, or group-du-standard-ranGen.yaml files according to your requirements:
1. In .sourceFiles, add the PtpOperatorConfig CR file that configures the AMQ transport host to the config-policy:
```
- fileName: PtpOperatorConfigForEvent.yaml
  policyName: "config-policy"
```
2. Configure the linuxptp and phc2sys for the PTP clock type and interface. For example, add the following stanza into .sourceFiles:
```
- fileName: PtpConfigSlave.yaml 1
  policyName: "config-policy"
  metadata:
    name: "du-ptp-slave"
  spec:
    profile:
    - name: "slave"
      interface: "ens5f1" 2
      ptp4lOpts: "-2 -s --summary_interval -4" 3
      phc2sysOpts: "-a -r -m -n 24 -N 8 -R 16" 4
    ptpClockThreshold: 5
      holdOverTimeout: 30 #secs
      maxOffsetThreshold: 100  #nano secs
      minOffsetThreshold: -100 #nano secs
```
  1
  Can be one PtpConfigMaster.yaml, PtpConfigSlave.yaml, or PtpConfigSlaveCvl.yaml depending on your requirements. PtpConfigSlaveCvl.yaml configures linuxptp services for an Intel E810 Columbiaville NIC. For configurations based on group-du-sno-ranGen.yaml or group-du-3node-ranGen.yaml, use PtpConfigSlave.yaml.
  2
  Device specific interface name.
  3
  You must append the --summary_interval -4 value to ptp4lOpts in .spec.sourceFiles.spec.profile to enable PTP fast events.
  4
  Required phc2sysOpts values. -m prints messages to stdout. The linuxptp-daemon DaemonSet parses the logs and generates Prometheus metrics.
  5
  Optional. If the ptpClockThreshold stanza is not present, default values are used for the ptpClockThreshold fields. The stanza shows default ptpClockThreshold values. The ptpClockThreshold values configure how long after the PTP master clock is disconnected before PTP events are triggered. holdOverTimeout is the time value in seconds before the PTP clock event state changes to FREERUN when the PTP master clock is disconnected. The maxOffsetThreshold and minOffsetThreshold settings configure offset values in nanoseconds that compare against the values for CLOCK_REALTIME (phc2sys) or master offset (ptp4l). When the ptp4l or phc2sys offset value is outside this range, the PTP clock state is set to FREERUN. When the offset value is within this range, the PTP clock state is set to LOCKED.
Apply the following PolicyGenTemplate changes to your specific site YAML files, for example, example-sno-site.yaml:
1. In .sourceFiles, add the Interconnect CR file that configures the AMQ router to the config-policy:
```
- fileName: AmqInstance.yaml
  policyName: "config-policy"
```
Merge any other required changes and files with your custom site repository.
Push the changes to your site configuration repository to deploy PTP fast events to new sites using GitOps ZTP.

Additional resources

For more information about how to install the AMQ Interconnect Operator, see Installing the AMQ messaging bus.

19.9.6. Configuring bare-metal event monitoring using PolicyGenTemplate CRs

You can configure bare-metal hardware events for vRAN clusters that are deployed using the GitOps Zero Touch Provisioning (ZTP) pipeline.

Prerequisites

Install the OpenShift CLI (oc).
Log in as a user with cluster-admin privileges.
Create a Git repository where you manage your custom site configuration data.

Procedure

To configure the AMQ Interconnect Operator and the Bare Metal Event Relay Operator, add the following YAML to spec.sourceFiles in the common-ranGen.yaml file:

# AMQ interconnect operator for fast events
- fileName: AmqSubscriptionNS.yaml
  policyName: "subscriptions-policy"
- fileName: AmqSubscriptionOperGroup.yaml
  policyName: "subscriptions-policy"
- fileName: AmqSubscription.yaml
  policyName: "subscriptions-policy"
# Bare Metal Event Rely operator
- fileName: BareMetalEventRelaySubscriptionNS.yaml
  policyName: "subscriptions-policy"
- fileName: BareMetalEventRelaySubscriptionOperGroup.yaml
  policyName: "subscriptions-policy"
- fileName: BareMetalEventRelaySubscription.yaml
  policyName: "subscriptions-policy"

Add the Interconnect CR to .spec.sourceFiles in the site configuration file, for example, the example-sno-site.yaml file:
```
- fileName: AmqInstance.yaml
  policyName: "config-policy"
```
Add the HardwareEvent CR to spec.sourceFiles in your specific group configuration file, for example, in the group-du-sno-ranGen.yaml file:
```
- fileName: HardwareEvent.yaml
  policyName: "config-policy"
  spec:
    nodeSelector: {}
    transportHost: "amqp://<amq_interconnect_name>.<amq_interconnect_namespace>.svc.cluster.local" 1
    logLevel: "info"
```
1
The transportHost URL is composed of the existing AMQ Interconnect CR name and namespace. For example, in transportHost: "amqp://amq-router.amq-router.svc.cluster.local", the AMQ Interconnect name and namespace are both set to amq-router.
Note
Each baseboard management controller (BMC) requires a single HardwareEvent resource only.
Commit the PolicyGenTemplate change in Git, and then push the changes to your site configuration repository to deploy bare-metal events monitoring to new sites using GitOps ZTP.

Create the Redfish Secret by running the following command:

$ oc -n openshift-bare-metal-events create secret generic redfish-basic-auth \
--from-literal=username=<bmc_username> --from-literal=password=<bmc_password> \
--from-literal=hostaddr="<bmc_host_ip_addr>"

Additional resources

For more information about how to install the Bare Metal Event Relay, see Installing the Bare Metal Event Relay using the CLI.

Additional resources

For more information about how to create the username, password, and host IP address for the BMC secret, see Creating the bare-metal event and Secret CRs.

19.10. Updating managed clusters with the Topology Aware Lifecycle Manager

You can use the Topology Aware Lifecycle Manager (TALM) to manage the software lifecycle of OpenShift Container Platform managed clusters. TALM uses Red Hat Advanced Cluster Management (RHACM) policies to perform changes on the target clusters.

Important

The Topology Aware Lifecycle Manager is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

Additional resources

For more information about the Topology Aware Lifecycle Manager, see About the Topology Aware Lifecycle Manager.

19.10.1. Updating clusters in a disconnected environment

You can upgrade managed clusters and Operators for managed clusters that you have deployed using GitOps ZTP and Topology Aware Lifecycle Manager (TALM).

19.10.1.1. Setting up the environment

TALM can perform both platform and Operator updates.

You must mirror both the platform image and Operator images that you want to update to in your mirror registry before you can use TALM to update your disconnected clusters. Complete the following steps to mirror the images:

For platform updates, you must perform the following steps:
1. Mirror the desired OpenShift Container Platform image repository. Ensure that the desired platform image is mirrored by following the "Mirroring the OpenShift Container Platform image repository" procedure linked in the Additional Resources. Save the contents of the imageContentSources section in the imageContentSources.yaml file:
  Example output
```
imageContentSources:
 - mirrors:
   - mirror-ocp-registry.ibmcloud.io.cpak:5000/openshift-release-dev/openshift4
   source: quay.io/openshift-release-dev/ocp-release
 - mirrors:
   - mirror-ocp-registry.ibmcloud.io.cpak:5000/openshift-release-dev/openshift4
   source: quay.io/openshift-release-dev/ocp-v4.0-art-dev
```
2. Save the image signature of the desired platform image that was mirrored. You must add the image signature to the PolicyGenTemplate CR for platform updates. To get the image signature, perform the following steps:
  1. Specify the desired OpenShift Container Platform tag by running the following command:
    $ OCP_RELEASE_NUMBER=<release_version>
  2. Specify the architecture of the server by running the following command:
    $ ARCHITECTURE=<server_architecture>
  3. Get the release image digest from Quay by running the following command
    $ DIGEST="$(oc adm release info quay.io/openshift-release-dev/ocp-release:${OCP_RELEASE_NUMBER}-${ARCHITECTURE} | sed -n 's/Pull From: .*@//p')"
  4. Set the digest algorithm by running the following command:
    $ DIGEST_ALGO="${DIGEST%%:*}"
  5. Set the digest signature by running the following command:
    $ DIGEST_ENCODED="${DIGEST#*:}"
  6. Get the image signature from the mirror.openshift.com website by running the following command:
    $ SIGNATURE_BASE64=$(curl -s "https://mirror.openshift.com/pub/openshift-v4/signatures/openshift/release/${DIGEST_ALGO}=${DIGEST_ENCODED}/signature-1" | base64 -w0 && echo)
  7. Save the image signature to the checksum-<OCP_RELEASE_NUMBER>.yaml file by running the following commands:
    $ cat >checksum-${OCP_RELEASE_NUMBER}.yaml <<EOF ${DIGEST_ALGO}-${DIGEST_ENCODED}: ${SIGNATURE_BASE64} EOF
3. Prepare the update graph. You have two options to prepare the update graph:
  1. Use the OpenShift Update Service.
    For more information about how to set up the graph on the hub cluster, see Deploy the operator for OpenShift Update Service and Build the graph data init container.
  2. Make a local copy of the upstream graph. Host the update graph on an http or https server in the disconnected environment that has access to the managed cluster. To download the update graph, use the following command:
    $ curl -s https://api.openshift.com/api/upgrades_info/v1/graph?channel=stable-4.10 -o ~/upgrade-graph_stable-4.10
For Operator updates, you must perform the following task:
- Mirror the Operator catalogs. Ensure that the desired operator images are mirrored by following the procedure in the "Mirroring Operator catalogs for use with disconnected clusters" section.

Additional resources

For more information about how to update ZTP, see Upgrading GitOps ZTP.
For more information about how to mirror an OpenShift Container Platform image repository, see Mirroring the OpenShift Container Platform image repository.
For more information about how to mirror Operator catalogs for disconnected clusters, see Mirroring Operator catalogs for use with disconnected clusters.
For more information about how to prepare the disconnected environment and mirroring the desired image repository, see Preparing the disconnected environment.
For more information about update channels and releases, see Understanding update channels and releases.

19.10.1.2. Performing a platform update

You can perform a platform update with the TALM.

Prerequisites

Install the Topology Aware Lifecycle Manager (TALM).
Update ZTP to the latest version.
Provision one or more managed clusters with ZTP.
Mirror the desired image repository.
Log in as a user with cluster-admin privileges.
Create RHACM policies in the hub cluster.

Procedure

Create a PolicyGenTemplate CR for the platform update:
1. Save the following contents of the PolicyGenTemplate CR in the du-upgrade.yaml file.
  Example of PolicyGenTemplate for platform update
```
apiVersion: ran.openshift.io/v1
kind: PolicyGenTemplate
metadata:
  name: "du-upgrade"
  namespace: "ztp-group-du-sno"
spec:
  bindingRules:
    group-du-sno: ""
  mcp: "master"
  remediationAction: inform
  sourceFiles:
    - fileName: ImageSignature.yaml 1
      policyName: "platform-upgrade-prep"
      binaryData:
        ${DIGEST_ALGO}-${DIGEST_ENCODED}: ${SIGNATURE_BASE64} 2
    - fileName: DisconnectedICSP.yaml
      policyName: "platform-upgrade-prep"
      metadata:
        name: disconnected-internal-icsp-for-ocp
      spec:
        repositoryDigestMirrors: 3
          - mirrors:
            - quay-intern.example.com/ocp4/openshift-release-dev
            source: quay.io/openshift-release-dev/ocp-release
          - mirrors:
            - quay-intern.example.com/ocp4/openshift-release-dev
            source: quay.io/openshift-release-dev/ocp-v4.0-art-dev
    - fileName: ClusterVersion.yaml 4
      policyName: "platform-upgrade-prep"
      metadata:
        name: version
        annotations:
          ran.openshift.io/ztp-deploy-wave: "1"
      spec:
        channel: "stable-4.10"
        upstream: http://upgrade.example.com/images/upgrade-graph_stable-4.10
    - fileName: ClusterVersion.yaml 5
      policyName: "platform-upgrade"
      metadata:
        name: version
      spec:
        channel: "stable-4.10"
        upstream: http://upgrade.example.com/images/upgrade-graph_stable-4.10
        desiredUpdate:
          version: 4.10.4
      status:
        history:
          - version: 4.10.4
            state: "Completed"
```
  1
  The ConfigMap CR contains the signature of the desired release image to update to.
  2
  Shows the image signature of the desired OpenShift Container Platform release. Get the signature from the checksum-${OCP_RELASE_NUMBER}.yaml file you saved when following the procedures in the "Setting up the environment" section.
  3
  Shows the mirror repository that contains the desired OpenShift Container Platform image. Get the mirrors from the imageContentSources.yaml file that you saved when following the procedures in the "Setting up the environment" section.
  4
  Shows the ClusterVersion CR to update upstream.
  5
  Shows the ClusterVersion CR to trigger the update. The channel, upstream, and desiredVersion fields are all required for image pre-caching.
  The PolicyGenTemplate CR generates two policies:
  - The du-upgrade-platform-upgrade-prep policy does the preparation work for the platform update. It creates the ConfigMap CR for the desired release image signature, creates the image content source of the mirrored release image repository, and updates the cluster version with the desired update channel and the update graph reachable by the managed cluster in the disconnected environment.
  - The du-upgrade-platform-upgrade policy is used to perform platform upgrade.
2. Add the du-upgrade.yaml file contents to the kustomization.yaml file located in the ZTP Git repository for the PolicyGenTemplate CRs and push the changes to the Git repository.
  ArgoCD pulls the changes from the Git repository and generates the policies on the hub cluster.
3. Check the created policies by running the following command:
```
$ oc get policies -A | grep platform-upgrade
```
Apply the required update resources before starting the platform update with the TALM.
1. Save the content of the platform-upgrade-prep ClusterUpgradeGroup CR with the du-upgrade-platform-upgrade-prep policy and the target managed clusters to the cgu-platform-upgrade-prep.yml file, as shown in the following example:
```
apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-platform-upgrade-prep
  namespace: default
spec:
  managedPolicies:
  - du-upgrade-platform-upgrade-prep
  clusters:
  - spoke1
  remediationStrategy:
    maxConcurrency: 1
  enable: true
```
2. Apply the policy to the hub cluster by running the following command:
```
$ oc apply -f cgu-platform-upgrade-prep.yml
```
3. Monitor the update process. Upon completion, ensure that the policy is compliant by running the following command:
```
$ oc get policies --all-namespaces
```
Create the ClusterGroupUpdate CR for the platform update with the spec.enable field set to false.
1. Save the content of the platform update ClusterGroupUpdate CR with the du-upgrade-platform-upgrade policy and the target clusters to the cgu-platform-upgrade.yml file, as shown in the following example:
```
apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-platform-upgrade
  namespace: default
spec:
  managedPolicies:
  - du-upgrade-platform-upgrade
  preCaching: false
  clusters:
  - spoke1
  remediationStrategy:
    maxConcurrency: 1
  enable: false
```
2. Apply the ClusterGroupUpdate CR to the hub cluster by running the following command:
```
$ oc apply -f cgu-platform-upgrade.yml
```
Optional: Pre-cache the images for the platform update.
1. Enable pre-caching in the ClusterGroupUpdate CR by running the following command:
```
$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-platform-upgrade \
--patch '{"spec":{"preCaching": true}}' --type=merge
```
2. Monitor the update process and wait for the pre-caching to complete. Check the status of pre-caching by running the following command on the hub cluster:
```
$ oc get cgu cgu-platform-upgrade -o jsonpath='{.status.precaching.status}'
```
Start the platform update:
1. Enable the cgu-platform-upgrade policy and disable pre-caching by running the following command:
```
$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-platform-upgrade \
--patch '{"spec":{"enable":true, "preCaching": false}}' --type=merge
```
2. Monitor the process. Upon completion, ensure that the policy is compliant by running the following command:
```
$ oc get policies --all-namespaces
```

Additional resources

For more information about mirroring the images in a disconnected environment, see Preparing the disconnected environment

19.10.1.3. Performing an Operator update

You can perform an Operator update with the TALM.

Prerequisites

Install the Topology Aware Lifecycle Manager (TALM).
Update ZTP to the latest version.
Provision one or more managed clusters with ZTP.
Mirror the desired index image, bundle images, and all Operator images referenced in the bundle images.
Log in as a user with cluster-admin privileges.
Create RHACM policies in the hub cluster.

Procedure

Update the PolicyGenTemplate CR for the Operator update.
1. Update the du-upgrade PolicyGenTemplate CR with the following additional contents in the du-upgrade.yaml file:
```
apiVersion: ran.openshift.io/v1
kind: PolicyGenTemplate
metadata:
  name: "du-upgrade"
  namespace: "ztp-group-du-sno"
spec:
  bindingRules:
    group-du-sno: ""
  mcp: "master"
  remediationAction: inform
  sourceFiles:
    - fileName: DefaultCatsrc.yaml
      remediationAction: inform
      policyName: "operator-catsrc-policy"
      metadata:
        name: redhat-operators
      spec:
        displayName: Red Hat Operators Catalog
        image: registry.example.com:5000/olm/redhat-operators:v4.10 1
        updateStrategy: 2
          registryPoll:
            interval: 1h
```
  1
  The index image URL contains the desired Operator images. If the index images are always pushed to the same image name and tag, this change is not needed.
  2
  Set how frequently the Operator Lifecycle Manager (OLM) polls the index image for new Operator versions with the registryPoll.interval field. This change is not needed if a new index image tag is always pushed for y-stream and z-stream Operator updates. The registryPoll.interval field can be set to a shorter interval to expedite the update, however shorter intervals increase computational load. To counteract this, you can restore registryPoll.interval to the default value once the update is complete.
2. This update generates one policy, du-upgrade-operator-catsrc-policy, to update the redhat-operators catalog source with the new index images that contain the desired Operators images.
  Note
  If you want to use the image pre-caching for Operators and there are Operators from a different catalog source other than redhat-operators, you must perform the following tasks:
  Prepare a separate catalog source policy with the new index image or registry poll interval update for the different catalog source.
  Prepare a separate subscription policy for the desired Operators that are from the different catalog source.
  For example, the desired SRIOV-FEC Operator is available in the certified-operators catalog source. To update the catalog source and the Operator subscription, add the following contents to generate two policies, du-upgrade-fec-catsrc-policy and du-upgrade-subscriptions-fec-policy:
```
apiVersion: ran.openshift.io/v1
kind: PolicyGenTemplate
metadata:
  name: "du-upgrade"
  namespace: "ztp-group-du-sno"
spec:
  bindingRules:
    group-du-sno: ""
  mcp: "master"
  remediationAction: inform
  sourceFiles:
       …
    - fileName: DefaultCatsrc.yaml
      remediationAction: inform
      policyName: "fec-catsrc-policy"
      metadata:
        name: certified-operators
      spec:
        displayName: Intel SRIOV-FEC Operator
        image: registry.example.com:5000/olm/far-edge-sriov-fec:v4.10
        updateStrategy:
          registryPoll:
            interval: 10m
    - fileName: AcceleratorsSubscription.yaml
      policyName: "subscriptions-fec-policy"
      spec:
        channel: "stable"
        source: certified-operators
```
3. Remove the specified subscriptions channels in the common PolicyGenTemplate CR, if they exist. The default subscriptions channels from the ZTP image are used for the update.
  Note
  The default channel for the Operators applied through ZTP 4.10 is stable, except for the performance-addon-operator. The default channel for PAO is 4.10. You can also specify the default channels in the common PolicyGenTemplate CR.
4. Push the PolicyGenTemplate CRs updates to the ZTP Git repository.
  ArgoCD pulls the changes from the Git repository and generates the policies on the hub cluster.
5. Check the created policies by running the following command:
```
$ oc get policies -A | grep -E "catsrc-policy|subscription"
```
Apply the required catalog source updates before starting the Operator update.
1. Save the content of the ClusterGroupUpgrade CR named operator-upgrade-prep with the catalog source policies and the target managed clusters to the cgu-operator-upgrade-prep.yml file:
```
apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-operator-upgrade-prep
  namespace: default
spec:
  clusters:
  - spoke1
  enable: true
  managedPolicies:
  - du-upgrade-operator-catsrc-policy
  remediationStrategy:
    maxConcurrency: 1
```
2. Apply the policy to the hub cluster by running the following command:
```
$ oc apply -f cgu-operator-upgrade-prep.yml
```
3. Monitor the update process. Upon completion, ensure that the policy is compliant by running the following command:
```
$ oc get policies -A | grep -E "catsrc-policy"
```
Create the ClusterGroupUpgrade CR for the Operator update with the spec.enable field set to false.
1. Save the content of the Operator update ClusterGroupUpgrade CR with the du-upgrade-operator-catsrc-policy policy and the subscription policies created from the common PolicyGenTemplate and the target clusters to the cgu-operator-upgrade.yml file, as shown in the following example:
```
apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-operator-upgrade
  namespace: default
spec:
  managedPolicies:
  - du-upgrade-operator-catsrc-policy 1
  - common-subscriptions-policy 2
  preCaching: false
  clusters:
  - spoke1
  remediationStrategy:
    maxConcurrency: 1
  enable: false
```
  1
  The policy is needed by the image pre-caching feature to retrieve the operator images from the catalog source.
  2
  The policy contains Operator subscriptions. If you have upgraded ZTP from 4.9 to 4.10 by following "Upgrade ZTP from 4.9 to 4.10", all Operator subscriptions are grouped into the common-subscriptions-policy policy.
  Note
  One ClusterGroupUpgrade CR can only pre-cache the images of the desired Operators defined in the subscription policy from one catalog source included in the ClusterGroupUpgrade CR. If the desired Operators are from different catalog sources, such as in the example of the SRIOV-FEC Operator, another ClusterGroupUpgrade CR must be created with du-upgrade-fec-catsrc-policy and du-upgrade-subscriptions-fec-policy policies for the SRIOV-FEC Operator images pre-caching and update.
2. Apply the ClusterGroupUpgrade CR to the hub cluster by running the following command:
```
$ oc apply -f cgu-operator-upgrade.yml
```

Optional: Pre-cache the images for the Operator update.

Before starting image pre-caching, verify the subscription policy is NonCompliant at this point by running the following command:

$ oc get policy common-subscriptions-policy -n <policy_namespace>

Example output

NAME                          REMEDIATION ACTION   COMPLIANCE STATE     AGE
common-subscriptions-policy   inform               NonCompliant         27d

Enable pre-caching in the ClusterGroupUpgrade CR by running the following command:

$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-operator-upgrade \
--patch '{"spec":{"preCaching": true}}' --type=merge

Monitor the process and wait for the pre-caching to complete. Check the status of pre-caching by running the following command on the managed cluster:
```
$ oc get cgu cgu-operator-upgrade -o jsonpath='{.status.precaching.status}'
```

Check if the pre-caching is completed before starting the update by running the following command:

$ oc get cgu -n default cgu-operator-upgrade -ojsonpath='{.status.conditions}' | jq

Example output

[
    {
      "lastTransitionTime": "2022-03-08T20:49:08.000Z",
      "message": "The ClusterGroupUpgrade CR is not enabled",
      "reason": "UpgradeNotStarted",
      "status": "False",
      "type": "Ready"
    },
    {
      "lastTransitionTime": "2022-03-08T20:55:30.000Z",
      "message": "Precaching is completed",
      "reason": "PrecachingCompleted",
      "status": "True",
      "type": "PrecachingDone"
    }
]

Start the Operator update.
1. Enable the cgu-operator-upgrade ClusterGroupUpgrade CR and disable pre-caching to start the Operator update by running the following command:
```
$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-operator-upgrade \
--patch '{"spec":{"enable":true, "preCaching": false}}' --type=merge
```
2. Monitor the process. Upon completion, ensure that the policy is compliant by running the following command:
```
$ oc get policies --all-namespaces
```

Additional resources

For more information about updating GitOps ZTP, see Upgrading GitOps ZTP.

19.10.1.4. Performing a platform and an Operator update together

You can perform a platform and an Operator update at the same time.

Prerequisites

Install the Topology Aware Lifecycle Manager (TALM).
Update ZTP to the latest version.
Provision one or more managed clusters with ZTP.
Log in as a user with cluster-admin privileges.
Create RHACM policies in the hub cluster.

Procedure

Create the PolicyGenTemplate CR for the updates by following the steps described in the "Performing a platform update" and "Performing an Operator update" sections.
Apply the prep work for the platform and the Operator update.
1. Save the content of the ClusterGroupUpgrade CR with the policies for platform update preparation work, catalog source updates, and target clusters to the cgu-platform-operator-upgrade-prep.yml file, for example:
```
apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-platform-operator-upgrade-prep
  namespace: default
spec:
  managedPolicies:
  - du-upgrade-platform-upgrade-prep
  - du-upgrade-operator-catsrc-policy
  clusterSelector:
  - group-du-sno
  remediationStrategy:
    maxConcurrency: 10
  enable: true
```
2. Apply the cgu-platform-operator-upgrade-prep.yml file to the hub cluster by running the following command:
```
$ oc apply -f cgu-platform-operator-upgrade-prep.yml
```
3. Monitor the process. Upon completion, ensure that the policy is compliant by running the following command:
```
$ oc get policies --all-namespaces
```
Create the ClusterGroupUpdate CR for the platform and the Operator update with the spec.enable field set to false.
1. Save the contents of the platform and Operator update ClusterGroupUpdate CR with the policies and the target clusters to the cgu-platform-operator-upgrade.yml file, as shown in the following example:
```
apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  name: cgu-du-upgrade
  namespace: default
spec:
  managedPolicies:
  - du-upgrade-platform-upgrade 1
  - du-upgrade-operator-catsrc-policy 2
  - common-subscriptions-policy 3
  preCaching: true
  clusterSelector:
  - group-du-sno
  remediationStrategy:
    maxConcurrency: 1
  enable: false
```
  1
  This is the platform update policy.
  2
  This is the policy containing the catalog source information for the Operators to be updated. It is needed for the pre-caching feature to determine which Operator images to download to the managed cluster.
  3
  This is the policy to update the Operators.
2. Apply the cgu-platform-operator-upgrade.yml file to the hub cluster by running the following command:
```
$ oc apply -f cgu-platform-operator-upgrade.yml
```
Optional: Pre-cache the images for the platform and the Operator update.
1. Enable pre-caching in the ClusterGroupUpgrade CR by running the following command:
```
$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-du-upgrade \
--patch '{"spec":{"preCaching": true}}' --type=merge
```
2. Monitor the update process and wait for the pre-caching to complete. Check the status of pre-caching by running the following command on the managed cluster:
```
$ oc get jobs,pods -n openshift-talm-pre-cache
```
3. Check if the pre-caching is completed before starting the update by running the following command:
```
$ oc get cgu cgu-du-upgrade -ojsonpath='{.status.conditions}'
```
Start the platform and Operator update.
1. Enable the cgu-du-upgrade ClusterGroupUpgrade CR to start the platform and the Operator update by running the following command:
```
$ oc --namespace=default patch clustergroupupgrade.ran.openshift.io/cgu-du-upgrade \
--patch '{"spec":{"enable":true, "preCaching": false}}' --type=merge
```
2. Monitor the process. Upon completion, ensure that the policy is compliant by running the following command:
```
$ oc get policies --all-namespaces
```
  Note
  The CRs for the platform and Operator updates can be created from the beginning by configuring the setting to spec.enable: true. In this case, the update starts immediately after pre-caching completes and there is no need to manually enable the CR.
  Both pre-caching and the update create extra resources, such as policies, placement bindings, placement rules, managed cluster actions, and managed cluster view, to help complete the procedures. Setting the afterCompletion.deleteObjects field to true deletes all these resources after the updates complete.

19.10.1.5. Removing Performance Addon Operator subscriptions from deployed clusters

In earlier versions of OpenShift Container Platform, the Performance Addon Operator provided automatic, low latency performance tuning for applications. In OpenShift Container Platform 4.11 or later, these functions are part of the Node Tuning Operator.

Do not install the Performance Addon Operator on clusters running OpenShift Container Platform 4.11 or later. If you upgrade to OpenShift Container Platform 4.11 or later, the Node Tuning Operator automatically removes the Performance Addon Operator.

Note

You need to remove any policies that create Performance Addon Operator subscriptions to prevent a re-installation of the Operator.

The reference DU profile includes the Performance Addon Operator in the PolicyGenTemplate CR common-ranGen.yaml. To remove the subscription from deployed managed clusters, you must update common-ranGen.yaml.

Note

If you install Performance Addon Operator 4.10.3-5 or later on OpenShift Container Platform 4.11 or later, the Performance Addon Operator detects the cluster version and automatically hibernates to avoid interfering with the Node Tuning Operator functions. However, to ensure best performance, remove the Performance Addon Operator from your OpenShift Container Platform 4.11 clusters.

Prerequisites

Create a Git repository where you manage your custom site configuration data. The repository must be accessible from the hub cluster and be defined as a source repository for ArgoCD.
Update to OpenShift Container Platform 4.11 or later.
Log in as a user with cluster-admin privileges.

Procedure

Change the complianceType to mustnothave for the Performance Addon Operator namespace, Operator group, and subscription in the common-ranGen.yaml file.

 -  fileName: PaoSubscriptionNS.yaml
    policyName: "subscriptions-policy"
    complianceType: mustnothave
 -  fileName: PaoSubscriptionOperGroup.yaml
    policyName: "subscriptions-policy"
    complianceType: mustnothave
 -  fileName: PaoSubscription.yaml
    policyName: "subscriptions-policy"
    complianceType: mustnothave

Merge the changes with your custom site repository and wait for the ArgoCD application to synchronize the change to the hub cluster. The status of the common-subscriptions-policy policy changes to Non-Compliant.
Apply the change to your target clusters by using the Topology Aware Lifecycle Manager. For more information about rolling out configuration changes, see the "Additional resources" section.
Monitor the process. When the status of the common-subscriptions-policy policy for a target cluster is Compliant, the Performance Addon Operator has been removed from the cluster. Get the status of the common-subscriptions-policy by running the following command:
```
$ oc get policy -n ztp-common common-subscriptions-policy
```
Delete the Performance Addon Operator namespace, Operator group and subscription CRs from .spec.sourceFiles in the common-ranGen.yaml file.
Merge the changes with your custom site repository and wait for the ArgoCD application to synchronize the change to the hub cluster. The policy remains compliant.

19.10.2. About the auto-created ClusterGroupUpgrade CR for ZTP

TALM has a controller called ManagedClusterForCGU that monitors the Ready state of the ManagedCluster CRs on the hub cluster and creates the ClusterGroupUpgrade CRs for ZTP (zero touch provisioning).

For any managed cluster in the Ready state without a "ztp-done" label applied, the ManagedClusterForCGU controller automatically creates a ClusterGroupUpgrade CR in the ztp-install namespace with its associated RHACM policies that are created during the ZTP process. TALM then remediates the set of configuration policies that are listed in the auto-created ClusterGroupUpgrade CR to push the configuration CRs to the managed cluster.

Note

If the managed cluster has no bound policies when the cluster becomes Ready, no ClusterGroupUpgrade CR is created.

Example of an auto-created ClusterGroupUpgrade CR for ZTP

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  generation: 1
  name: spoke1
  namespace: ztp-install
  ownerReferences:
  - apiVersion: cluster.open-cluster-management.io/v1
    blockOwnerDeletion: true
    controller: true
    kind: ManagedCluster
    name: spoke1
    uid: 98fdb9b2-51ee-4ee7-8f57-a84f7f35b9d5
  resourceVersion: "46666836"
  uid: b8be9cd2-764f-4a62-87d6-6b767852c7da
spec:
  actions:
    afterCompletion:
      addClusterLabels:
        ztp-done: "" 1
      deleteClusterLabels:
        ztp-running: ""
      deleteObjects: true
    beforeEnable:
      addClusterLabels:
        ztp-running: "" 2
  clusters:
  - spoke1
  enable: true
  managedPolicies:
  - common-spoke1-config-policy
  - common-spoke1-subscriptions-policy
  - group-spoke1-config-policy
  - spoke1-config-policy
  - group-spoke1-validator-du-policy
  preCaching: false
  remediationStrategy:
    maxConcurrency: 1
    timeout: 240

1: Applied to the managed cluster when TALM completes the cluster configuration.
2: Applied to the managed cluster when TALM starts deploying the configuration policies.

19.11. Updating GitOps ZTP

You can update the Gitops zero touch provisioning (ZTP) infrastructure independently from the hub cluster, Red Hat Advanced Cluster Management (RHACM), and the managed OpenShift Container Platform clusters.

Note

You can update the Red Hat OpenShift GitOps Operator when new versions become available. When updating the GitOps ZTP plugin, review the updated files in the reference configuration and ensure that the changes meet your requirements.

19.11.1. Overview of the GitOps ZTP update process

You can update GitOps zero touch provisioning (ZTP) for a fully operational hub cluster running an earlier version of the GitOps ZTP infrastructure. The update process avoids impact on managed clusters.

Note

Any changes to policy settings, including adding recommended content, results in updated polices that must be rolled out to the managed clusters and reconciled.

At a high level, the strategy for updating the GitOps ZTP infrastructure is as follows:

Label all existing clusters with the ztp-done label.
Stop the ArgoCD applications.
Install the new GitOps ZTP tools.
Update required content and optional changes in the Git repository.
Update and restart the application configuration.

19.11.2. Preparing for the upgrade

Use the following procedure to prepare your site for the GitOps zero touch provisioning (ZTP) upgrade.

Procedure

Get the latest version of the GitOps ZTP container that has the custom resources (CRs) used to configure Red Hat OpenShift GitOps for use with GitOps ZTP.
Extract the argocd/deployment directory by using the following commands:
```
$ mkdir -p ./update
```
```
$ podman run --log-driver=none --rm registry.redhat.io/openshift4/ztp-site-generate-rhel8:v{product-version} extract /home/ztp --tar | tar x -C ./update
```
The /update directory contains the following subdirectories:
- update/extra-manifest: contains the source CR files that the SiteConfig CR uses to generate the extra manifest configMap.
- update/source-crs: contains the source CR files that the PolicyGenTemplate CR uses to generate the Red Hat Advanced Cluster Management (RHACM) policies.
- update/argocd/deployment: contains patches and YAML files to apply on the hub cluster for use in the next step of this procedure.
- update/argocd/example: contains example SiteConfig and PolicyGenTemplate files that represent the recommended configuration.
Update the clusters-app.yaml and policies-app.yaml files to reflect the name of your applications and the URL, branch, and path for your Git repository.
If the upgrade includes changes that results in obsolete policies, the obsolete policies should be removed prior to performing the upgrade.
Diff the changes between the configuration and deployment source CRs in the /update folder and Git repo where you manage your fleet site CRs. Apply and push the required changes to your site repository.
Important
When you update GitOps ZTP to the latest version, you must apply the changes from the update/argocd/deployment directory to your site repository. Do not use older versions of the argocd/deployment/ files.

19.11.3. Labeling the existing clusters

To ensure that existing clusters remain untouched by the tool updates, label all existing managed clusters with the ztp-done label.

Note

This procedure only applies when updating clusters that were not provisioned with Topology Aware Lifecycle Manager (TALM). Clusters that you provision with TALM are automatically labeled with ztp-done.

Procedure

Find a label selector that lists the managed clusters that were deployed with zero touch provisioning (ZTP), such as local-cluster!=true:
```
$ oc get managedcluster -l 'local-cluster!=true'
```
Ensure that the resulting list contains all the managed clusters that were deployed with ZTP, and then use that selector to add the ztp-done label:
```
$ oc label managedcluster -l 'local-cluster!=true' ztp-done=
```

19.11.4. Stopping the existing GitOps ZTP applications

Removing the existing applications ensures that any changes to existing content in the Git repository are not rolled out until the new version of the tools is available.

Use the application files from the deployment directory. If you used custom names for the applications, update the names in these files first.

Procedure

Perform a non-cascaded delete on the clusters application to leave all generated resources in place:
```
$ oc delete -f update/argocd/deployment/clusters-app.yaml
```

Perform a cascaded delete on the policies application to remove all previous policies:

$ oc patch -f policies-app.yaml -p '{"metadata": {"finalizers": ["resources-finalizer.argocd.argoproj.io"]}}' --type merge

$ oc delete -f update/argocd/deployment/policies-app.yaml

19.11.5. Required changes to the Git repository

When upgrading the ztp-site-generate container from an earlier release of GitOps ZTP to v4.10 or later, there are additional requirements for the contents of the Git repository. Existing content in the repository must be updated to reflect these changes.

Make required changes to PolicyGenTemplate files:
All PolicyGenTemplate files must be created in a Namespace prefixed with ztp. This ensures that the GitOps zero touch provisioning (ZTP) application is able to manage the policy CRs generated by GitOps ZTP without conflicting with the way Red Hat Advanced Cluster Management (RHACM) manages the policies internally.

Add the kustomization.yaml file to the repository:

All SiteConfig and PolicyGenTemplate CRs must be included in a kustomization.yaml file under their respective directory trees. For example:

├── policygentemplates
│   ├── site1-ns.yaml
│   ├── site1.yaml
│   ├── site2-ns.yaml
│   ├── site2.yaml
│   ├── common-ns.yaml
│   ├── common-ranGen.yaml
│   ├── group-du-sno-ranGen-ns.yaml
│   ├── group-du-sno-ranGen.yaml
│   └── kustomization.yaml
└── siteconfig
    ├── site1.yaml
    ├── site2.yaml
    └── kustomization.yaml

Note

The files listed in the generator sections must contain either SiteConfig or PolicyGenTemplate CRs only. If your existing YAML files contain other CRs, for example, Namespace, these other CRs must be pulled out into separate files and listed in the resources section.

The PolicyGenTemplate kustomization file must contain all PolicyGenTemplate YAML files in the generator section and Namespace CRs in the resources section. For example:

apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization

generators:
- common-ranGen.yaml
- group-du-sno-ranGen.yaml
- site1.yaml
- site2.yaml

resources:
- common-ns.yaml
- group-du-sno-ranGen-ns.yaml
- site1-ns.yaml
- site2-ns.yaml

The SiteConfig kustomization file must contain all SiteConfig YAML files in the generator section and any other CRs in the resources:

apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization

generators:
- site1.yaml
- site2.yaml

Remove the pre-sync.yaml and post-sync.yaml files.
In OpenShift Container Platform 4.10 and later, the pre-sync.yaml and post-sync.yaml files are no longer required. The update/deployment/kustomization.yaml CR manages the policies deployment on the hub cluster.
Note
There is a set of pre-sync.yaml and post-sync.yaml files under both the SiteConfig and PolicyGenTemplate trees.
Review and incorporate recommended changes
Each release may include additional recommended changes to the configuration applied to deployed clusters. Typically these changes result in lower CPU use by the OpenShift platform, additional features, or improved tuning of the platform.
Review the reference SiteConfig and PolicyGenTemplate CRs applicable to the types of cluster in your network. These examples can be found in the argocd/example directory extracted from the GitOps ZTP container.

19.11.6. Installing the new GitOps ZTP applications

Using the extracted argocd/deployment directory, and after ensuring that the applications point to your site Git repository, apply the full contents of the deployment directory. Applying the full contents of the directory ensures that all necessary resources for the applications are correctly configured.

Procedure

To patch the ArgoCD instance in the hub cluster by using the patch file that you previously extracted into the update/argocd/deployment/ directory, enter the following command:
```
$ oc patch argocd openshift-gitops \
-n openshift-gitops --type=merge \
--patch-file update/argocd/deployment/argocd-openshift-gitops-patch.json
```
To apply the contents of the argocd/deployment directory, enter the following command:
```
$ oc apply -k update/argocd/deployment
```

19.11.7. Rolling out the GitOps ZTP configuration changes

If any configuration changes were included in the upgrade due to implementing recommended changes, the upgrade process results in a set of policy CRs on the hub cluster in the Non-Compliant state. With the ZTP GitOps v4.10 and later ztp-site-generate container, these policies are set to inform mode and are not pushed to the managed clusters without an additional step by the user. This ensures that potentially disruptive changes to the clusters can be managed in terms of when the changes are made, for example, during a maintenance window, and how many clusters are updated concurrently.

To roll out the changes, create one or more ClusterGroupUpgrade CRs as detailed in the TALM documentation. The CR must contain the list of Non-Compliant policies that you want to push out to the managed clusters as well as a list or selector of which clusters should be included in the update.

Additional resources

For information about the Topology Aware Lifecycle Manager (TALM), see About the Topology Aware Lifecycle Manager configuration.
For information about creating ClusterGroupUpgrade CRs, see About the auto-created ClusterGroupUpgrade CR for ZTP.

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Scalability and performance

Scaling your OpenShift Container Platform cluster and tuning performance in production environments

Chapter 1. Recommended host practices

1.1. Recommended node host practices

1.2. Creating a KubeletConfig CRD to edit kubelet parameters

1.3. Modifying the number of unavailable worker nodes

1.4. Control plane node sizing

1.4.1. Selecting a larger Amazon Web Services instance type for control plane machines

1.4.1.1. Changing the Amazon Web Services instance type by using the AWS console

1.5. Recommended etcd practices

1.6. Moving etcd to a different disk

1.7. Defragmenting etcd data

1.7.1. Automatic defragmentation

1.7.2. Manual defragmentation

1.8. OpenShift Container Platform infrastructure components

1.9. Moving the monitoring solution

1.10. Moving the default registry

1.11. Moving the router

1.12. Infrastructure node sizing

1.13. Additional resources

Chapter 2. Recommended host practices for IBM Z & LinuxONE environments

2.1. Managing CPU overcommitment

2.2. Disable Transparent Huge Pages

2.3. Boost networking performance with Receive Flow Steering

2.3.1. Use the Machine Config Operator (MCO) to activate RFS

2.4. Choose your networking setup

2.5. Ensure high disk performance with HyperPAV on z/VM

2.5.1. Use the Machine Config Operator (MCO) to activate HyperPAV aliases in nodes using z/VM full-pack minidisks

2.6. RHEL KVM on IBM Z host recommendations

2.6.1. Use multiple queues for your VirtIO network interfaces

2.6.2. Use I/O threads for your virtual block devices

2.6.3. Avoid virtual SCSI devices

2.6.4. Configure guest caching for disk

2.6.5. Exclude the memory balloon device

2.6.6. Tune the CPU migration algorithm of the host scheduler

2.6.7. Disable the cpuset cgroup controller

2.6.8. Tune the polling period for idle virtual CPUs

Chapter 3. Recommended cluster scaling practices

3.1. Recommended practices for scaling the cluster

3.2. Modifying a machine set

3.3. About machine health checks

3.3.1. Limitations when deploying machine health checks

3.4. Sample MachineHealthCheck resource

3.4.1. Short-circuiting machine health check remediation

3.4.1.1. Setting maxUnhealthy by using an absolute value

3.4.1.2. Setting maxUnhealthy by using percentages

3.5. Creating a MachineHealthCheck resource

Chapter 4. Using the Node Tuning Operator

4.1. About the Node Tuning Operator

4.2. Accessing an example Node Tuning Operator specification

4.3. Default profiles set on a cluster

4.4. Verifying that the TuneD profiles are applied

4.5. Custom tuning specification

4.6. Custom tuning examples

4.7. Supported TuneD daemon plugins

Chapter 5. Using CPU Manager and Topology Manager

5.1. Setting up CPU Manager

5.2. Topology Manager policies

5.3. Setting up Topology Manager

5.4. Pod interactions with Topology Manager policies

Chapter 6. Scheduling NUMA-aware workloads

6.1. About NUMA-aware scheduling

6.2. Installing the NUMA Resources Operator

6.2.1. Installing the NUMA Resources Operator using the CLI

6.2.2. Installing the NUMA Resources Operator using the web console

6.3. Creating the NUMAResourcesOperator custom resource

6.4. Deploying the NUMA-aware secondary pod scheduler

6.5. Scheduling workloads with the NUMA-aware scheduler

6.6. Troubleshooting NUMA-aware scheduling

6.6.1. Checking the NUMA-aware scheduler logs

6.6.2. Troubleshooting the resource topology exporter

6.6.3. Correcting a missing resource topology exporter config map

Chapter 7. Scaling the Cluster Monitoring Operator

7.1. Prometheus database storage requirements

7.2. Configuring cluster monitoring

Chapter 8. Planning your environment according to object maximums

8.1. OpenShift Container Platform tested cluster maximums for major releases

8.2. OpenShift Container Platform environment and configuration on which the cluster maximums are tested

8.2.1. AWS cloud platform

14.4.5. Creating a pod with a QoS class of `Guaranteed`