This topic provides recommended performance and scalability practices for control planes in OpenShift Container Platform.

This topic provides recommended performance and scalability practices for infrastructure in OpenShift Container Platform.

This topic provides recommended performance and scalability practices for etcd in OpenShift Container Platform.

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

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

OpenShift Container Platform supports tested cluster maximums rather than absolute cluster maximums. Not every combination of OpenShift Container Platform version, control plane workload, and network plugin are tested, so the following table does not represent an absolute expectation of scale for all deployments. It might not be possible to scale to a maximum on all dimensions simultaneously. The table contains tested maximums for specific workload and deployment configurations, and serves as a scale guide as to what can be expected with similar deployments.

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 default 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".

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

OpenShift Container Platform comes with several system pods, such as SDN, DNS, Operators, and others, which run across every worker node by default. Therefore, the result of the above formula can vary.

Consider an example application environment:

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.

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 bytes (2 MiB) by default. The Kubelet injects environment variables in to each pod scheduled to run in the namespace including:

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.

In a highly virtualized IBM zSystems 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:

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.

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.

The networking stack is one of the most important components for a Kubernetes-based product like OpenShift Container Platform. For IBM zSystems 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:

DASD and ECKD devices are commonly used disk types in IBM zSystems 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.

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 zSystems and IBM® LinuxONE environments.

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

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

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

# sysctl kernel.sched_migration_cost_ns=60000

kernel.sched_migration_cost_ns=60000

The Node Tuning Operator helps you manage node-level tuning by orchestrating the TuneD daemon and achieves low latency performance by using the Performance Profile controller. 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 uses the Performance Profile controller to implement automatic tuning to achieve low latency performance for OpenShift Container Platform applications. The cluster administrator configures a performance profile to define node-level settings such as the following:

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

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

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.

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:
  profile:
  - data: |
      [main]
      summary=Optimize systems running OpenShift (provider specific parent profile)
      include=-provider-${f:exec:cat:/var/lib/tuned/provider},openshift
    name: openshift
  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 ^ {} \;

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

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

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

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:

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
    tunedConfig:
      reapply_sysctl: <bool> 9

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

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

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.

- 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.

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.

Cloud provider-specific TuneD profiles

With this functionality, all Cloud provider-specific nodes can conveniently be assigned a TuneD profile specifically tailored to a given Cloud provider on a OpenShift Container Platform cluster. This can be accomplished without adding additional node labels or grouping nodes into machine config pools.

This functionality takes advantage of spec.providerID node object values in the form of <cloud-provider>://<cloud-provider-specific-id> and writes the file /var/lib/tuned/provider with the value <cloud-provider> in NTO operand containers. The content of this file is then used by TuneD to load provider-<cloud-provider> profile if such profile exists.

The openshift profile that both openshift-control-plane and openshift-node profiles inherit settings from is now updated to use this functionality through the use of conditional profile loading. Neither NTO nor TuneD currently ship any Cloud provider-specific profiles. However, it is possible to create a custom profile provider-<cloud-provider> that will be applied to all Cloud provider-specific cluster nodes.

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: provider-gce
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - data: |
      [main]
      summary=GCE Cloud provider-specific profile
      # Your tuning for GCE Cloud provider goes here.
    name: provider-gce

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.

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

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.

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.

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

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

See Available TuneD Plugins and Getting Started with TuneD for more information.

To set node-level tuning on the nodes in your hosted cluster, you can use the Node Tuning Operator. In hosted control planes, you can configure node tuning by creating config maps that contain Tuned objects and referencing those config maps in your node pools.

For more advanced tuning in hosted control planes, which requires setting kernel boot parameters, you can also use the Node Tuning Operator. The following example shows how you can create a node pool with huge pages reserved.

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 cpumanager-enabled custom resource (CR):

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

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.

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

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.

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.

$ oc get numaresourcesoperators.nodetopology.openshift.io

NAME                    AGE
numaresourcesoperator   10m

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

$ oc get all -n openshift-numaresources

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

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.

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

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

$ oc get configmap

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

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.

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

$ oc -n openshift-ingress patch deploy/router-default --type=strategic --patch='{"spec":{"template":{"spec":{"containers":[{"name":"router","livenessProbe":{"timeoutSeconds":5},"readinessProbe":{"timeoutSeconds":5}}]}}}}'

$ oc -n openshift-ingress describe deploy/router-default | grep -e Liveness: -e Readiness:
    Liveness:   http-get http://:1936/healthz delay=0s timeout=5s period=10s #success=1 #failure=3
    Readiness:  http-get http://:1936/healthz/ready delay=0s timeout=5s period=10s #success=1 #failure=3

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:

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

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

You can optimize CPU usage in OpenShift Container Platform clusters by using mount namespace encapsulation to provide a private namespace for kubelet and CRI-O processes. This reduces the cluster CPU resources used by systemd with no difference in functionality.

7.4.1. Encapsulating mount namespaces

Mount namespaces are used to isolate mount points so that processes in different namespaces cannot view each others' files. Encapsulation is the process of moving Kubernetes mount namespaces to an alternative location where they will not be constantly scanned by the host operating system.

The host operating system uses systemd to constantly scan all mount namespaces: both the standard Linux mounts and the numerous mounts that Kubernetes uses to operate. The current implementation of kubelet and CRI-O both use the top-level namespace for all container runtime and kubelet mount points. However, encapsulating these container-specific mount points in a private namespace reduces systemd overhead with no difference in functionality. Using a separate mount namespace for both CRI-O and kubelet can encapsulate container-specific mounts from any systemd or other host operating system interaction.

This ability to potentially achieve major CPU optimization is now available to all OpenShift Container Platform administrators. Encapsulation can also improve security by storing Kubernetes-specific mount points in a location safe from inspection by unprivileged users.

The following diagrams illustrate a Kubernetes installation before and after encapsulation. Both scenarios show example containers which have mount propagation settings of bidirectional, host-to-container, and none.

Here we see systemd, host operating system processes, kubelet, and the container runtime sharing a single mount namespace.

• systemd, host operating system processes, kubelet, and the container runtime each have access to and visibility of all mount points.
• Container 1, configured with bidirectional mount propagation, can access systemd and host mounts, kubelet and CRI-O mounts. A mount originating in Container 1, such as /run/a is visible to systemd, host operating system processes, kubelet, container runtime, and other containers with host-to-container or bidirectional mount propagation configured (as in Container 2).
• Container 2, configured with host-to-container mount propagation, can access systemd and host mounts, kubelet and CRI-O mounts. A mount originating in Container 2, such as /run/b, is not visible to any other context.
• Container 3, configured with no mount propagation, has no visibility of external mount points. A mount originating in Container 3, such as /run/c, is not visible to any other context.

The following diagram illustrates the system state after encapsulation.

• The main systemd process is no longer devoted to unnecessary scanning of Kubernetes-specific mount points. It only monitors systemd-specific and host mount points.
• The host operating system processes can access only the systemd and host mount points.
• Using a separate mount namespace for both CRI-O and kubelet completely separates all container-specific mounts away from any systemd or other host operating system interaction whatsoever.
• The behavior of Container 1 is unchanged, except a mount it creates such as /run/a is no longer visible to systemd or host operating system processes. It is still visible to kubelet, CRI-O, and other containers with host-to-container or bidirectional mount propagation configured (like Container 2).
• The behavior of Container 2 and Container 3 is unchanged.

[Unit]
Description=Example service
[Service]
ExecStart=/usr/bin/kubensenter /path/to/original/command arg1 arg2

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 compute machine sets describe infrastructure components specific to your configuration. You apply specific Kubernetes labels to these compute 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.

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:

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.

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

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

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.

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.

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

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. The following steps describe how to disable THP using the Node Tuning Operator (NTO).

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. 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 50 ms, 5G is targeted to reach latency numbers of 1 ms 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 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 uses the Node Tuning Operator to implement automatic tuning to achieve low latency performance for OpenShift Container Platform 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.

OpenShift Container Platform also supports workload hints for the Node Tuning Operator that can tune the PerformanceProfile to meet the demands of different industry environments. Workload hints are available for highPowerConsumption (very low latency at the cost of increased power consumption) and realTime (priority given to optimum latency). A combination of true/false settings for these hints can be used to deal with application-specific workload profiles and requirements.

Workload hints simplify the fine-tuning of performance to industry sector settings. Instead of a “one size fits all” approach, workload hints can cater to usage patterns such as placing priority on:

In an ideal world, all of those would be prioritized: in real life, some come at the expense of others. The Node Tuning Operator is now aware of the workload expectations and better able to meet the demands of the workload. The cluster admin can now specify into which use case that workload falls. The Node Tuning Operator uses the PerformanceProfile to fine tune the performance settings for the workload.

The environment in which an application is operating influences its behavior. For a typical data center with no strict latency requirements, only minimal default tuning is needed that enables CPU partitioning for some high performance workload pods. For data centers and workloads where latency is a higher priority, measures are still taken to optimize power consumption. The most complicated cases are clusters close to latency-sensitive equipment such as manufacturing machinery and software-defined radios. This last class of deployment is often referred to as Far edge. For Far edge deployments, ultra-low latency is the ultimate priority, and is achieved at the expense of power management.

In OpenShift Container Platform version 4.10 and previous versions, the Performance Addon Operator was used to implement automatic tuning to achieve low latency performance. Now this functionality is part of the Node Tuning Operator.

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 the Node Tuning 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, isolate CPUs for application containers to run the workloads, and disable unused CPUs to reduce power consumption.

$ oc get node -o wide

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   v1.25.0
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://1.25.0-99.rhaos4.10.gitc3131de.el8
[...]

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"

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

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

annotations:
  cpu-c-states.crio.io: "disable"
  cpu-freq-governor.crio.io: "<governor>"

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

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

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

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:

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.

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
 name: performance
spec:
 cpu:
  isolated: "5-15" 1
  reserved: "0-4" 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

hugepages:
   defaultHugepagesSize: "1G"
   pages:
   - size:  "1G"
     count:  4
     node:  0 1

spec:
  hugepages:
    defaultHugepagesSize: 1G
    pages:
    - count: 1024
      node: 0
      size: 2M
    - count: 4
      node: 1
      size: 1G

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

/proc/irq/0/effective_affinity: 1
/proc/irq/1/effective_affinity: 8
/proc/irq/2/effective_affinity: 0
/proc/irq/3/effective_affinity: 1
/proc/irq/4/effective_affinity: 2
/proc/irq/5/effective_affinity: 1
/proc/irq/6/effective_affinity: 1
/proc/irq/7/effective_affinity: 1
/proc/irq/8/effective_affinity: 1
/proc/irq/9/effective_affinity: 2
/proc/irq/10/effective_affinity: 1
/proc/irq/11/effective_affinity: 1
/proc/irq/12/effective_affinity: 4
/proc/irq/13/effective_affinity: 1
/proc/irq/14/effective_affinity: 1
/proc/irq/15/effective_affinity: 1
/proc/irq/24/effective_affinity: 2
/proc/irq/25/effective_affinity: 4
/proc/irq/26/effective_affinity: 2
/proc/irq/27/effective_affinity: 1
/proc/irq/28/effective_affinity: 8
/proc/irq/29/effective_affinity: 4
/proc/irq/30/effective_affinity: 4
/proc/irq/31/effective_affinity: 8
/proc/irq/32/effective_affinity: 8
/proc/irq/33/effective_affinity: 1
/proc/irq/34/effective_affinity: 2

workloadHints:
highPowerConsumption: false
realTime: false

workloadHints:
highPowerConsumption: false
realTime: true

workloadHints:
highPowerConsumption: true
realTime: true

workloadHints:
realTime: true
highPowerConsumption: false
perPodPowerManagement: true

The Node Tuning 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.

apiVersion: performance.openshift.io/v2
metadata:
  name: performance
spec:
  kind: PerformanceProfile
  spec:
    cpu:
      reserved: 0-1  #total = 2
      isolated: 2-8
    net:
      userLevelNetworking: true
# ...

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
# ...

# 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
...

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 Node Tuning 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:

Each of these types contain the following fields:

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.

--image=registry.redhat.io/openshift4/performance-addon-operator-must-gather-rhel8:v4.12.

Your cluster must meet the following requirements before you can run the 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.

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.

The cnf-tests image uses three tools to measure the latency of the system:

Each tool has a specific use. Use the tools in sequence to achieve reliable test results.

The tests introduce the following environment variables:

Run the cluster latency tests to validate node tuning for your Cloud-native Network Functions (CNF) workload.

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

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

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:

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

Use the following procedures to generate a JUnit latency test output and test failure report.

Use the following procedures to generate a JUnit latency test output and test failure report.

You can run latency tests on single-node OpenShift clusters.

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 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.

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.

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.

Mirroring a different set of test images

You can optionally change the default upstream images that are mirrored for the latency tests.

To run latency tests, the cluster must be accessible from within the cnf-tests container.

Worker latency profiles are multiple sets of carefully-tuned values for the node-status-update-frequency, node-monitor-grace-period, default-not-ready-toleration-seconds and default-unreachable-toleration-seconds parameters. These parameters let you control the reaction of the cluster to latency issues without needing to determine the best values manually.

All worker latency profiles configure the following parameters:

The following Operators monitor the changes to the worker latency profiles and respond accordingly:

While the default configuration works in most cases, OpenShift Container Platform offers two other worker latency profiles for situations where the network is experiencing higher latency than usual. The three worker latency profiles are described in the following sections:

To implement a worker latency profile to deal with network latency, edit the node.config object to add the name of the profile. You can change the profile at any time as latency increases or decreases.

You must move one worker latency profile at a time. For example, you cannot move directly from the Default profile to the LowUpdateSlowReaction worker latency profile. You must move from the default worker latency profile to the MediumUpdateAverageReaction profile first, then to LowUpdateSlowReaction. Similarly, when returning to the default profile, you must move from the low profile to the medium profile first, then to the default.

To change the low profile to medium or change the medium to low, edit the node.config object and set the spec.workerLatencyProfile parameter to the appropriate value.

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:

For single-node OpenShift, the Topology Aware Lifecycle Manager (TALM) offers the following features:

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.

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:

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".

You can use the OpenShift Container Platform web console to install the Topology Aware Lifecycle Manager.

If the Operator is not installed successfully:

You can use the OpenShift CLI (oc) to install the Topology Aware Lifecycle Manager (TALM).

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:

You can control the start time of an update using the enable field in the ClusterGroupUpgrade CR. For example, if you have a scheduled maintenance window of four hours, you can prepare a ClusterGroupUpgrade CR with the enable field set to false.

You can set the timeout by configuring the spec.remediationStrategy.timeout setting as follows:

spec
  remediationStrategy:
          maxConcurrency: 1
          timeout: 240

You can use the batchTimeoutAction to determine what happens if an update fails for a cluster. You can specify continue to skip the failing cluster and continue to upgrade other clusters, or abort to stop policy remediation for all clusters. Once the timeout elapses, TALM removes all enforce policies to ensure that no further updates are made to clusters.

To apply the changes, you set the enabled field to true.

For more information see the "Applying update policies to managed clusters" section.

As TALM works through remediation of the policies to the specified clusters, the ClusterGroupUpgrade CR can report true or false statuses for a number of conditions.

apiVersion: ran.openshift.io/v1alpha1
kind: ClusterGroupUpgrade
metadata:
  creationTimestamp: '2022-11-18T16:27:15Z'
  finalizers:
    - ran.openshift.io/cleanup-finalizer
  generation: 1
  name: talm-cgu
  namespace: talm-namespace
  resourceVersion: '40451823'
  uid: cca245a5-4bca-45fa-89c0-aa6af81a596c
Spec:
  actions:
    afterCompletion:
      deleteObjects: true
    beforeEnable: {}
  backup: false
  clusters: 1
    - spoke1
  enable: false 2
  managedPolicies: 3
    - talm-policy
  preCaching: false
  remediationStrategy: 4
    canaries: 5
        - spoke1
    maxConcurrency: 2 6
    timeout: 240
  clusterLabelSelectors: 7
    - matchExpressions:
      - key: label1
      operator: In
      values:
        - value1a
        - value1b
  batchTimeoutAction: 8
status: 9
    computedMaxConcurrency: 2
    conditions:
      - lastTransitionTime: '2022-11-18T16:27:15Z'
        message: All selected clusters are valid
        reason: ClusterSelectionCompleted
        status: 'True'
        type: ClustersSelected 10
      - lastTransitionTime: '2022-11-18T16:27:15Z'
        message: Completed validation
        reason: ValidationCompleted
        status: 'True'
        type: Validated 11
      - lastTransitionTime: '2022-11-18T16:37:16Z'
        message: Not enabled
        reason: NotEnabled
        status: 'False'
        type: Progressing
    managedPoliciesForUpgrade:
      - name: talm-policy
        namespace: talm-namespace
    managedPoliciesNs:
      talm-policy: talm-namespace
    remediationPlan:
      - - spoke1
      - - spoke2
        - spoke3
    status: