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:

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

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 allows you to create a KubeletConfig custom resource (CR) to edit the kubelet parameters.

The master node resource requirements depend on the number of nodes in the cluster. The following master node size recommendations are based on the results of control plane density focused testing.

For large and dense clusters, etcd can suffer from poor performance if the keyspace grows excessively large and exceeds the space quota. Periodic maintenance of etcd including defragmentation needs to be done to free up space in the data store. It is highly recommended that you monitor Prometheus for etcd metrics and defragment it when needed before etcd raises a cluster-wide alarm that puts the cluster into a maintenance mode, which only accepts key reads and deletes. Some of the key metrics to monitor are 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, and etcd_debugging_mvcc_db_total_size_in_bytes which shows the database size including free space waiting for defragmentation.

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 DaemonSet. 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.

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

Note the default CR is meant for delivering standard node-level tuning for the OpenShift Container Platform platform and any 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/pod labels and profile priorities.

The following are the default profiles set on a cluster.

apiVersion: tuned.openshift.io/v1alpha1
kind: Tuned
metadata:
  name: default
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - name: "openshift"
    data: |
      [main]
      summary=Optimize systems running OpenShift (parent profile)
      include=${f:virt_check:virtual-guest:throughput-performance}
      [selinux]
      avc_cache_threshold=8192
      [net]
      nf_conntrack_hashsize=131072
      [sysctl]
      net.ipv4.ip_forward=1
      kernel.pid_max=>131072
      net.netfilter.nf_conntrack_max=1048576
      net.ipv4.neigh.default.gc_thresh1=8192
      net.ipv4.neigh.default.gc_thresh2=32768
      net.ipv4.neigh.default.gc_thresh3=65536
      net.ipv6.neigh.default.gc_thresh1=8192
      net.ipv6.neigh.default.gc_thresh2=32768
      net.ipv6.neigh.default.gc_thresh3=65536
      [sysfs]
      /sys/module/nvme_core/parameters/io_timeout=4294967295
      /sys/module/nvme_core/parameters/max_retries=10
  - name: "openshift-control-plane"
    data: |
      [main]
      summary=Optimize systems running OpenShift control plane
      include=openshift
      [sysctl]
      # ktune sysctl settings, maximizing i/o throughput
      #
      # Minimal preemption granularity for CPU-bound tasks:
      # (default: 1 msec#  (1 + ilog(ncpus)), units: nanoseconds)
      kernel.sched_min_granularity_ns=10000000
      # The total time the scheduler will consider a migrated process
      # "cache hot" and thus less likely to be re-migrated
      # (system default is 500000, i.e. 0.5 ms)
      kernel.sched_migration_cost_ns=5000000
      # SCHED_OTHER wake-up granularity.
      #
      # Preemption granularity when tasks wake up.  Lower the value to
      # improve wake-up latency and throughput for latency critical tasks.
      kernel.sched_wakeup_granularity_ns=4000000
  - name: "openshift-node"
    data: |
      [main]
      summary=Optimize systems running OpenShift nodes
      include=openshift
      [sysctl]
      net.ipv4.tcp_fastopen=3
      fs.inotify.max_user_watches=65536
  - name: "openshift-control-plane-es"
    data: |
      [main]
      summary=Optimize systems running ES on OpenShift control-plane
      include=openshift-control-plane
      [sysctl]
      vm.max_map_count=262144
  - name: "openshift-node-es"
    data: |
      [main]
      summary=Optimize systems running ES on OpenShift nodes
      include=openshift-node
      [sysctl]
      vm.max_map_count=262144
  recommend:
  - profile: "openshift-control-plane-es"
    priority: 10
    match:
    - label: "tuned.openshift.io/elasticsearch"
      type: "pod"
      match:
      - label: "node-role.kubernetes.io/master"
      - label: "node-role.kubernetes.io/infra"

  - profile: "openshift-node-es"
    priority: 20
    match:
    - label: "tuned.openshift.io/elasticsearch"
      type: "pod"

  - profile: "openshift-control-plane"
    priority: 30
    match:
    - label: "node-role.kubernetes.io/master"
    - label: "node-role.kubernetes.io/infra"

  - profile: "openshift-node"
priority: 40

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.

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:

recommend:
- match:                              # optional; if omitted, profile match is assumed unless a profile with a higher matches first
  <match>                             # an optional array
  priority: <priority>                # profile ordering priority, lower numbers mean higher priority (0 is the highest priority)
  profile: <tuned_profile_name>       # e.g. tuned_profile_1

# ...

- match:
  <match>
  priority: <priority>
  profile: <tuned_profile_name>       # e.g. tuned_profile_n

If <match> is omitted, a profile match (for example, true) is assumed.

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

- label: <label_name>     # node or pod label name
  value: <label_value>    # optional node or pod label value; if omitted, the presence of <label_name> is enough to match
  type: <label_type>      # optional node or pod type ("node" or "pod"); if omitted, "node" is assumed
  <match>                 # an optional <match> array

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> array matches, the entire <match> array evaluates to true. Therefore, the array acts as logical OR operator.

- 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.

The following CR applies custom node-level tuning for OpenShift Container Platform nodes that run an ingress pod with label tuned.openshift.io/ingress-pod-label=ingress-pod-label-value. As an administrator, use the following command to create a custom tuned CR.

oc create -f- <<_EOF_
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-pod-label
      value: "ingress-pod-label-value"
      type: pod
    priority: 10
    profile: openshift-ingress
_EOF_

Excluding the [main] section, the following Tuned plug-ins 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 plug-ins that is not supported. The following Tuned plug-ins are currently not supported:

See Available Tuned Plug-ins and Getting Started with Tuned for more information.

Cluster Loader is included in the origin-tests container image.

The tool creates multiple namespaces (projects), which contain multiple templates or Pods.

provider: local 1
ClusterLoader:
  cleanup: true
  projects:
    - num: 1
      basename: clusterloader-cakephp-mysql
      tuning: default
      ifexists: reuse
      templates:
        - num: 1
          file: cakephp-mysql.json

    - num: 1
      basename: clusterloader-dancer-mysql
      tuning: default
      ifexists: reuse
      templates:
        - num: 1
          file: dancer-mysql.json

    - num: 1
      basename: clusterloader-django-postgresql
      tuning: default
      ifexists: reuse
      templates:
        - num: 1
          file: django-postgresql.json

    - num: 1
      basename: clusterloader-nodejs-mongodb
      tuning: default
      ifexists: reuse
      templates:
        - num: 1
          file: quickstarts/nodejs-mongodb.json

    - num: 1
      basename: clusterloader-rails-postgresql
      tuning: default
      templates:
        - num: 1
          file: rails-postgresql.json

  tuningsets: 2
    - name: default
      pods:
        stepping: 3
          stepsize: 5
          pause: 0 s
        rate_limit: 4
          delay: 0 ms

Red Hat performed various tests for different scale sizes.

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.

Lab environment

In a previous release, all experiments were performed in an OpenShift Container Platform on RHOSP environment:

Recommendations for OpenShift Container Platform

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.

In OpenShift Container Platform 4.2, half of a CPU core (500 millicore) is reserved by the system compared to OpenShift Container Platform 3.11 and previous versions.

Google Cloud Platform:

AWS cloud platform:

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.

2200 / 250 = 8.8

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

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.

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

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

The OpenShift Container Platform router is the Ingress point for all external traffic destined for OpenShift Container Platform services.

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

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:

In HTTP close (no keep-alive) scenarios:

Default router configuration with ROUTER_THREADS=4 was used and two different endpoint publishing strategies (LoadBalancerService/HostNetwork) tested. TLS session resumption was used for encrypted routes. With HTTP keep-alive, a single HAProxy router is capable of saturating 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 on how many applications to use behind the router:

In general, HAProxy can support routes for 5 to 1000 applications, depending on the technology in use. Router performance might be limited by the capabilities and performance of the applications behind it, such as language or static versus dynamic content.

Router sharding should be used to serve more routes towards applications and help horizontally scale the routing tier.

OpenShift Container Platform no longer supports modifying router deployments by setting environment variables such as ROUTER_THREADS, ROUTER_DEFAULT_TUNNEL_TIMEOUT, ROUTER_DEFAULT_CLIENT_TIMEOUT, ROUTER_DEFAULT_SERVER_TIMEOUT, and RELOAD_INTERVAL.

You can modify the router deployment, but if the Ingress Operator is enabled, the configuration is overwritten.

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. In order 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

Nodes must pre-allocate huge pages used in an OpenShift Container Platform cluster. Use the Node Tuning Operator to allocate huge pages on a specific node.