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Chapter 13. Low latency tuning


13.1. Understanding low latency tuning for cluster nodes

Edge computing has a key role in reducing latency and congestion problems and improving application performance for telco and 5G network applications. Maintaining a network architecture with the lowest possible latency 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 of 1 ms or less. This reduction in latency boosts wireless throughput by a factor of 10.

13.1.1. About low latency

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:

  • Low latency
  • Real-time capability
  • Efficient use of power

Ideally, all of the previously listed items are prioritized. Some of these items come at the expense of others however. 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.

13.1.2. About Hyper-Threading for low latency and real-time applications

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

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

Note

Hyper-Threading 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 Hyper-Threading implementation specific to that hardware. Disabling Hyper-Threading can increase the cost per core of the cluster.

13.2. Tuning nodes for low latency with the performance profile

Tune nodes for low latency by using the cluster performance profile. You can restrict CPUs for infra and application containers, configure huge pages, Hyper-Threading, and configure CPU partitions for latency-sensitive processes.

13.2.1. Creating a performance profile

You can create a cluster performance profile by using the Performance Profile Creator (PPC) tool. The PPC is a function of the Node Tuning Operator.

The PPC combines information about your cluster with user-supplied configurations to generate a performance profile that is appropriate to your hardware, topology and use-case.

Note

Performance profiles are applicable only to bare-metal environments where the cluster has direct access to the underlying hardware resources. You can configure performances profiles for both single-node OpenShift and multi-node clusters.

The following is a high-level workflow for creating and applying a performance profile in your cluster:

  • Create a machine config pool (MCP) for nodes that you want to target with performance configurations. In single-node OpenShift clusters, you must use the master MCP because there is only one node in the cluster.
  • Gather information about your cluster using the must-gather command.
  • Use the PPC tool to create a performance profile by using either of the following methods:

    • Run the PPC tool by using Podman.
    • Run the PPC tool by using a wrapper script.
  • Configure the performance profile for your use case and apply the performance profile to your cluster.

13.2.1.1. About the Performance Profile Creator

The Performance Profile Creator (PPC) is a command-line tool, delivered with the Node Tuning Operator, that can help you to create a performance profile for your cluster.

Initially, you can use the PPC tool to process the must-gather data to display key performance configurations for your cluster, including the following information:

  • NUMA cell partitioning with the allocated CPU IDs
  • Hyper-Threading node configuration

You can use this information to help you configure the performance profile.

Running the PPC

Specify performance configuration arguments to the PPC tool to generate a proposed performance profile that is appropriate for your hardware, topology, and use-case.

You can run the PPC by using one of the following methods:

  • Run the PPC by using Podman
  • Run the PPC by using the wrapper script
Note

Using the wrapper script abstracts some of the more granular Podman tasks into an executable script. For example, the wrapper script handles tasks such as pulling and running the required container image, mounting directories into the container, and providing parameters directly to the container through Podman. Both methods achieve the same result.

13.2.1.2. Creating a machine config pool to target nodes for performance tuning

For multi-node clusters, you can define a machine config pool (MCP) to identify the target nodes that you want to configure with a performance profile.

In single-node OpenShift clusters, you must use the master MCP because there is only one node in the cluster. You do not need to create a separate MCP for single-node OpenShift clusters.

Prerequisites

  • You have cluster-admin role access.
  • You installed the OpenShift CLI (oc).

Procedure

  1. Label the target nodes for configuration by running the following command:

    $ oc label node <node_name> node-role.kubernetes.io/worker-cnf="" 1
    1
    Replace <node_name> with the name of your node. This example applies the worker-cnf label.
  2. Create a MachineConfigPool resource containing the target nodes:

    1. Create a YAML file that defines the MachineConfigPool resource:

      Example mcp-worker-cnf.yaml file

      apiVersion: machineconfiguration.openshift.io/v1
      kind: MachineConfigPool
      metadata:
        name: worker-cnf 1
        labels:
          machineconfiguration.openshift.io/role: worker-cnf 2
      spec:
        machineConfigSelector:
          matchExpressions:
            - {
                 key: machineconfiguration.openshift.io/role,
                 operator: In,
                 values: [worker, worker-cnf],
              }
        paused: false
        nodeSelector:
          matchLabels:
            node-role.kubernetes.io/worker-cnf: "" 3

      1
      Specify a name for the MachineConfigPool resource.
      2
      Specify a unique label for the machine config pool.
      3
      Specify the nodes with the target label that you defined.
    2. Apply the MachineConfigPool resource by running the following command:

      $ oc apply -f mcp-worker-cnf.yaml

      Example output

      machineconfigpool.machineconfiguration.openshift.io/worker-cnf created

Verification

  • Check the machine config pools in your cluster by running the following command:

    $ oc get mcp

    Example output

    NAME         CONFIG                                                 UPDATED   UPDATING   DEGRADED   MACHINECOUNT   READYMACHINECOUNT   UPDATEDMACHINECOUNT   DEGRADEDMACHINECOUNT   AGE
    master       rendered-master-58433c7c3c1b4ed5ffef95234d451490       True      False      False      3              3                   3                     0                      6h46m
    worker       rendered-worker-168f52b168f151e4f853259729b6azc4       True      False      False      2              2                   2                     0                      6h46m
    worker-cnf   rendered-worker-cnf-168f52b168f151e4f853259729b6azc4   True      False      False      1              1                   1                     0                      73s

13.2.1.3. Gathering data about your cluster for the PPC

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.
  • You installed the OpenShift CLI (oc).
  • You identified a target MCP that you want to configure with a performance profile.

Procedure

  1. Navigate to the directory where you want to store the must-gather data.
  2. Collect cluster information by running the following command:

    $ oc adm must-gather

    The command creates a folder with the must-gather data in your local directory with a naming format similar to the following: must-gather.local.1971646453781853027.

  3. Optional: Create a compressed file from the must-gather directory:

    $ tar cvaf must-gather.tar.gz <must_gather_folder> 1
    1
    Replace with the name of the must-gather data folder.
    Note

    Compressed output is required if you are running the Performance Profile Creator wrapper script.

Additional resources

13.2.1.4. Running the Performance Profile Creator using Podman

As a cluster administrator, you can use Podman with the Performance Profile Creator (PPC) to create a performance profile.

For more information about the PPC arguments, see the section "Performance Profile Creator arguments".

Important

The PPC uses the must-gather data from your cluster to create the performance profile. If you make any changes to your cluster, such as relabeling a node targeted for performance configuration, you must re-create the must-gather data before running PPC again.

Prerequisites

  • Access to the cluster as a user with the cluster-admin role.
  • A cluster installed on bare-metal hardware.
  • You installed podman and the OpenShift CLI (oc).
  • Access to the Node Tuning Operator image.
  • You identified a machine config pool containing target nodes for configuration.
  • You have access to the must-gather data for your cluster.

Procedure

  1. Check the machine config pool by running the following command:

    $ oc get mcp

    Example output

    NAME         CONFIG                                                 UPDATED   UPDATING   DEGRADED   MACHINECOUNT   READYMACHINECOUNT   UPDATEDMACHINECOUNT   DEGRADEDMACHINECOUNT   AGE
    master       rendered-master-58433c8c3c0b4ed5feef95434d455490       True      False      False      3              3                   3                     0                      8h
    worker       rendered-worker-668f56a164f151e4a853229729b6adc4       True      False      False      2              2                   2                     0                      8h
    worker-cnf   rendered-worker-cnf-668f56a164f151e4a853229729b6adc4   True      False      False      1              1                   1                     0                      79m

  2. Use Podman to authenticate to registry.redhat.io by running the following command:

    $ podman login registry.redhat.io
    Username: <user_name>
    Password: <password>
  3. Optional: Display help for the PPC tool by running the following command:

    $ podman run --rm --entrypoint performance-profile-creator registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.17 -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")
          --offlined-cpu-count int            Number of offlined CPUs
          --per-pod-power-management          Enable Per Pod Power Management
          --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

  4. To display information about the cluster, run the PPC tool with the log argument by running the following command:

    $ podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.17 --info log --must-gather-dir-path /must-gather
    • --entrypoint performance-profile-creator defines the performance profile creator as a new entry point to podman.
    • -v <path_to_must_gather> specifies the path to either of the following components:

      • The directory containing the must-gather data.
      • An existing directory containing the must-gather decompressed .tar file.
    • --info log specifies a value for the output format.

      Example output

      level=info msg="Cluster info:"
      level=info msg="MCP 'master' nodes:"
      level=info msg=---
      level=info msg="MCP 'worker' nodes:"
      level=info msg="Node: host.example.com (NUMA cells: 1, HT: true)"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg="Node: host1.example.com (NUMA cells: 1, HT: true)"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg=---
      level=info msg="MCP 'worker-cnf' nodes:"
      level=info msg="Node: host2.example.com (NUMA cells: 1, HT: true)"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg=---

  5. Create a performance profile by running the following command. The example uses sample PPC arguments and values:

    $ podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.17 --mcp-name=worker-cnf --reserved-cpu-count=1 --rt-kernel=true --split-reserved-cpus-across-numa=false --must-gather-dir-path /must-gather --power-consumption-mode=ultra-low-latency --offlined-cpu-count=1 > my-performance-profile.yaml
    • -v <path_to_must_gather> specifies the path to either of the following components:

      • The directory containing the must-gather data.
      • The directory containing the must-gather decompressed .tar file.
    • --mcp-name=worker-cnf specifies the worker-=cnf machine config pool.
    • --reserved-cpu-count=1 specifies one reserved CPU.
    • --rt-kernel=true enables the real-time kernel.
    • --split-reserved-cpus-across-numa=false disables reserved CPUs splitting across NUMA nodes.
    • --power-consumption-mode=ultra-low-latency specifies minimal latency at the cost of increased power consumption.
    • --offlined-cpu-count=1 specifies one offlined CPU.

      Note

      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.

      Example output

      level=info msg="Nodes targeted by worker-cnf MCP are: [worker-2]"
      level=info msg="NUMA cell(s): 1"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg="1 reserved CPUs allocated: 0 "
      level=info msg="2 isolated CPUs allocated: 2-3"
      level=info msg="Additional Kernel Args based on configuration: []"

  6. Review the created YAML file by running the following command:

    $ cat my-performance-profile.yaml

    Example output

    ---
    apiVersion: performance.openshift.io/v2
    kind: PerformanceProfile
    metadata:
      name: performance
    spec:
      cpu:
        isolated: 2-3
        offlined: "1"
        reserved: "0"
      machineConfigPoolSelector:
        machineconfiguration.openshift.io/role: worker-cnf
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
      numa:
        topologyPolicy: restricted
      realTimeKernel:
        enabled: true
      workloadHints:
        highPowerConsumption: true
        perPodPowerManagement: false
        realTime: true

  7. Apply the generated profile:

    $ oc apply -f my-performance-profile.yaml

    Example output

    performanceprofile.performance.openshift.io/performance created

13.2.1.5. Running the Performance Profile Creator wrapper script

The wrapper script simplifies the process of creating a performance profile with the Performance Profile Creator (PPC) tool. The script handles tasks such as pulling and running the required container image, mounting directories into the container, and providing parameters directly to the container through Podman.

For more information about the Performance Profile Creator arguments, see the section "Performance Profile Creator arguments".

Important

The PPC uses the must-gather data from your cluster to create the performance profile. If you make any changes to your cluster, such as relabeling a node targeted for performance configuration, you must re-create the must-gather data before running PPC again.

Prerequisites

  • Access to the cluster as a user with the cluster-admin role.
  • A cluster installed on bare-metal hardware.
  • You installed podman and the OpenShift CLI (oc).
  • Access to the Node Tuning Operator image.
  • You identified a machine config pool containing target nodes for configuration.
  • Access to the must-gather tarball.

Procedure

  1. Create a file on your local machine named, for example, run-perf-profile-creator.sh:

    $ vi run-perf-profile-creator.sh
  2. 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"
    
    NTO_IMG="registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.17"
    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                 Node Tuning Operator image"
      print "   -t                 path to a must-gather tarball"
    
      ${IMG_EXISTS_CMD} "${NTO_IMG}" && ${CMD} "${NTO_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() {
      ${IMG_EXISTS_CMD} "${NTO_IMG}" || ${IMG_PULL_CMD} "${NTO_IMG}" || \
          exit_error "Node Tuning 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 "${OPT}" in
          h)
            usage
            exit 0
            ;;
          p)
            NTO_IMG="${OPTARG}"
            ;;
          t)
            MG_TARBALL="${OPTARG}"
            ;;
          ?)
            exit_error "invalid argument: ${OPTARG}"
            ;;
        esac
      done
      shift $((OPTIND - 1))
    
      check_requirements || exit 1
    
      ${CMD} -v "${DATA_DIR}:${MUST_GATHER_VOL}:z" "${NTO_IMG}" "$@" --must-gather-dir-path "${MUST_GATHER_VOL}"
      echo "" 1>&2
    }
    
    main "$@"
  3. Add execute permissions for everyone on this script:

    $ chmod a+x run-perf-profile-creator.sh
  4. Use Podman to authenticate to registry.redhat.io by running the following command:

    $ podman login registry.redhat.io
    Username: <user_name>
    Password: <password>
  5. Optional: Display help for the PPC tool by running the following command:

    $ ./run-perf-profile-creator.sh -h

    Example 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                 Node Tuning Operator image
       -t                 path to a must-gather tarball
    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")
          --offlined-cpu-count int            Number of offlined CPUs
          --per-pod-power-management          Enable Per Pod Power Management
          --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
          --enable-hardware-tuning            Enable setting maximum CPU frequencies

    Note

    You can optionally set a path for the Node Tuning Operator image using the -p option. If you do not set a path, the wrapper script uses the default image: registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.17.

  6. To display information about the cluster, run the PPC tool with the log argument by running the following command:

    $ ./run-perf-profile-creator.sh -t /<path_to_must_gather_dir>/must-gather.tar.gz -- --info=log
    • -t /<path_to_must_gather_dir>/must-gather.tar.gz specifies the path to directory containing the must-gather tarball. This is a required argument for the wrapper script.

      Example output

      level=info msg="Cluster info:"
      level=info msg="MCP 'master' nodes:"
      level=info msg=---
      level=info msg="MCP 'worker' nodes:"
      level=info msg="Node: host.example.com (NUMA cells: 1, HT: true)"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg="Node: host1.example.com (NUMA cells: 1, HT: true)"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg=---
      level=info msg="MCP 'worker-cnf' nodes:"
      level=info msg="Node: host2.example.com (NUMA cells: 1, HT: true)"
      level=info msg="NUMA cell 0 : [0 1 2 3]"
      level=info msg="CPU(s): 4"
      level=info msg=---

  7. Create a performance profile by running the following command.

    $ ./run-perf-profile-creator.sh -t /path-to-must-gather/must-gather.tar.gz -- --mcp-name=worker-cnf --reserved-cpu-count=1 --rt-kernel=true --split-reserved-cpus-across-numa=false --power-consumption-mode=ultra-low-latency --offlined-cpu-count=1 > my-performance-profile.yaml

    This example uses sample PPC arguments and values.

    • --mcp-name=worker-cnf specifies the worker-=cnf machine config pool.
    • --reserved-cpu-count=1 specifies one reserved CPU.
    • --rt-kernel=true enables the real-time kernel.
    • --split-reserved-cpus-across-numa=false disables reserved CPUs splitting across NUMA nodes.
    • --power-consumption-mode=ultra-low-latency specifies minimal latency at the cost of increased power consumption.
    • --offlined-cpu-count=1 specifies one offlined CPUs.

      Note

      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.

  8. Review the created YAML file by running the following command:

    $ cat my-performance-profile.yaml

    Example output

    ---
    apiVersion: performance.openshift.io/v2
    kind: PerformanceProfile
    metadata:
      name: performance
    spec:
      cpu:
        isolated: 2-3
        offlined: "1"
        reserved: "0"
      machineConfigPoolSelector:
        machineconfiguration.openshift.io/role: worker-cnf
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
      numa:
        topologyPolicy: restricted
      realTimeKernel:
        enabled: true
      workloadHints:
        highPowerConsumption: true
        perPodPowerManagement: false
        realTime: true

  9. Apply the generated profile:

    $ oc apply -f my-performance-profile.yaml

    Example output

    performanceprofile.performance.openshift.io/performance created

13.2.1.6. Performance Profile Creator arguments

Table 13.1. Required Performance Profile Creator arguments
ArgumentDescription

mcp-name

Name for MCP; for example, worker-cnf corresponding to the target machines.

must-gather-dir-path

The path of the must gather directory.

This argument is only required if you run the PPC tool by using Podman. If you use the PPC with the wrapper script, do not use this argument. Instead, specify the directory path to the must-gather tarball by using the -t option for the wrapper script.

reserved-cpu-count

Number of reserved CPUs. Use a natural number greater than zero.

rt-kernel

Enables real-time kernel.

Possible values: true or false.

Table 13.2. Optional Performance Profile Creator arguments
ArgumentDescription

disable-ht

Disable Hyper-Threading.

Possible values: true or false.

Default: false.

Warning

If this argument is set to true you should not disable Hyper-Threading in the BIOS. Disabling Hyper-Threading is accomplished with a kernel command line argument.

enable-hardware-tuning

Enable the setting of maximum CPU frequencies.

To enable this feature, set the maximum frequency for applications running on isolated and reserved CPUs for both of the following fields:

  • spec.hardwareTuning.isolatedCpuFreq
  • spec.hardwareTuning.reservedCpuFreq

This is an advanced feature. If you configure hardware tuning, the generated PerformanceProfile includes warnings and guidance on how to set frequency settings.

info

This captures cluster information. This argument also requires the must-gather-dir-path argument. If any other arguments are set they are ignored.

Possible values:

  • log
  • JSON

Default: log.

offlined-cpu-count

Number of offlined CPUs.

Note

Use a natural number greater than zero. If not enough logical processors are offlined, then error messages are logged. The messages are:

Error: failed to compute the reserved and isolated CPUs: please ensure that reserved-cpu-count plus offlined-cpu-count should be in the range [0,1]
Error: failed to compute the reserved and isolated CPUs: please specify the offlined CPU count in the range [0,1]

power-consumption-mode

The power consumption mode.

Possible values:

  • default: Performance achieved through CPU partitioning only.
  • low-latency: Enhanced measures to improve latency.
  • ultra-low-latency: Priority given to optimal latency, at the expense of power management.

Default: default.

per-pod-power-management

Enable per pod power management. You cannot use this argument if you configured ultra-low-latency as the power consumption mode.

Possible values: true or false.

Default: false.

profile-name

Name of the performance profile to create.

Default: performance.

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.

13.2.1.7. Reference performance profiles

Use the following reference performance profiles as the basis to develop your own custom profiles.

13.2.1.7.1. Performance profile template for clusters that use OVS-DPDK on OpenStack

To maximize machine performance in a cluster that uses Open vSwitch with the Data Plane Development Kit (OVS-DPDK) on Red Hat OpenStack Platform (RHOSP), you can use a performance profile.

You can use the following performance profile template to create a profile for your deployment.

Performance profile template for clusters that use OVS-DPDK

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  name: cnf-performanceprofile
spec:
  additionalKernelArgs:
    - nmi_watchdog=0
    - audit=0
    - mce=off
    - processor.max_cstate=1
    - idle=poll
    - intel_idle.max_cstate=0
    - default_hugepagesz=1GB
    - hugepagesz=1G
    - intel_iommu=on
  cpu:
    isolated: <CPU_ISOLATED>
    reserved: <CPU_RESERVED>
  hugepages:
    defaultHugepagesSize: 1G
    pages:
      - count: <HUGEPAGES_COUNT>
        node: 0
        size: 1G
  nodeSelector:
    node-role.kubernetes.io/worker: ''
  realTimeKernel:
    enabled: false
    globallyDisableIrqLoadBalancing: true

Insert values that are appropriate for your configuration for the CPU_ISOLATED, CPU_RESERVED, and HUGEPAGES_COUNT keys.

13.2.1.7.2. Telco RAN DU reference design performance profile

The following performance profile configures node-level performance settings for OpenShift Container Platform clusters on commodity hardware to host telco RAN DU workloads.

Telco RAN DU reference design performance profile

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  # if you change this name make sure the 'include' line in TunedPerformancePatch.yaml
  # matches this name: include=openshift-node-performance-${PerformanceProfile.metadata.name}
  # Also in file 'validatorCRs/informDuValidator.yaml':
  # name: 50-performance-${PerformanceProfile.metadata.name}
  name: openshift-node-performance-profile
  annotations:
    ran.openshift.io/reference-configuration: "ran-du.redhat.com"
spec:
  additionalKernelArgs:
    - "rcupdate.rcu_normal_after_boot=0"
    - "efi=runtime"
    - "vfio_pci.enable_sriov=1"
    - "vfio_pci.disable_idle_d3=1"
    - "module_blacklist=irdma"
  cpu:
    isolated: $isolated
    reserved: $reserved
  hugepages:
    defaultHugepagesSize: $defaultHugepagesSize
    pages:
      - size: $size
        count: $count
        node: $node
  machineConfigPoolSelector:
    pools.operator.machineconfiguration.openshift.io/$mcp: ""
  nodeSelector:
    node-role.kubernetes.io/$mcp: ''
  numa:
    topologyPolicy: "restricted"
  # To use the standard (non-realtime) kernel, set enabled to false
  realTimeKernel:
    enabled: true
  workloadHints:
    # WorkloadHints defines the set of upper level flags for different type of workloads.
    # See https://github.com/openshift/cluster-node-tuning-operator/blob/master/docs/performanceprofile/performance_profile.md#workloadhints
    # for detailed descriptions of each item.
    # The configuration below is set for a low latency, performance mode.
    realTime: true
    highPowerConsumption: false
    perPodPowerManagement: false

13.2.1.7.3. Telco core reference design performance profile

The following performance profile configures node-level performance settings for OpenShift Container Platform clusters on commodity hardware to host telco core workloads.

Telco core reference design performance profile

apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  # if you change this name make sure the 'include' line in TunedPerformancePatch.yaml
  # matches this name: include=openshift-node-performance-${PerformanceProfile.metadata.name}
  # Also in file 'validatorCRs/informDuValidator.yaml':
  # name: 50-performance-${PerformanceProfile.metadata.name}
  name: openshift-node-performance-profile
  annotations:
    ran.openshift.io/reference-configuration: "ran-du.redhat.com"
spec:
  additionalKernelArgs:
    - "rcupdate.rcu_normal_after_boot=0"
    - "efi=runtime"
    - "vfio_pci.enable_sriov=1"
    - "vfio_pci.disable_idle_d3=1"
    - "module_blacklist=irdma"
  cpu:
    isolated: $isolated
    reserved: $reserved
  hugepages:
    defaultHugepagesSize: $defaultHugepagesSize
    pages:
      - size: $size
        count: $count
        node: $node
  machineConfigPoolSelector:
    pools.operator.machineconfiguration.openshift.io/$mcp: ""
  nodeSelector:
    node-role.kubernetes.io/$mcp: ''
  numa:
    topologyPolicy: "restricted"
  # To use the standard (non-realtime) kernel, set enabled to false
  realTimeKernel:
    enabled: true
  workloadHints:
    # WorkloadHints defines the set of upper level flags for different type of workloads.
    # See https://github.com/openshift/cluster-node-tuning-operator/blob/master/docs/performanceprofile/performance_profile.md#workloadhints
    # for detailed descriptions of each item.
    # The configuration below is set for a low latency, performance mode.
    realTime: true
    highPowerConsumption: false
    perPodPowerManagement: false

13.2.2. Supported performance profile API versions

The Node Tuning 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.

Upgrading the performance profile to use device interrupt processing

When you upgrade the Node Tuning 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.

Upgrading Node Tuning Operator API from v1alpha1 to v1

When upgrading Node Tuning 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 Node Tuning Operator with API version v1.

Upgrading Node Tuning Operator API from v1alpha1 or v1 to v2

When upgrading from an older Node Tuning 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.

13.2.3. Configuring node power consumption and realtime processing with workload hints

Procedure

  • Create a PerformanceProfile appropriate for the environment’s hardware and topology by using the Performance Profile Creator (PPC) tool. The following table describes the possible values set for the power-consumption-mode flag associated with the PPC tool and the workload hint that is applied.
Table 13.3. Impact of combinations of power consumption and real-time settings on latency
Performance Profile creator settingHintEnvironmentDescription

Default

workloadHints:
highPowerConsumption: false
realTime: false

High throughput cluster without latency requirements

Performance achieved through CPU partitioning only.

Low-latency

workloadHints:
highPowerConsumption: false
realTime: true

Regional data-centers

Both energy savings and low-latency are desirable: compromise between power management, latency and throughput.

Ultra-low-latency

workloadHints:
highPowerConsumption: true
realTime: true

Far edge clusters, latency critical workloads

Optimized for absolute minimal latency and maximum determinism at the cost of increased power consumption.

Per-pod power management

workloadHints:
realTime: true
highPowerConsumption: false
perPodPowerManagement: true

Critical and non-critical workloads

Allows for power management per pod.

Example

The following configuration is commonly used in a telco RAN DU deployment.

    apiVersion: performance.openshift.io/v2
    kind: PerformanceProfile
    metadata:
      name: workload-hints
    spec:
      ...
      workloadHints:
        realTime: true
        highPowerConsumption: false
        perPodPowerManagement: false 1
1
Disables some debugging and monitoring features that can affect system latency.
Note

When the realTime workload hint flag is set to true in a performance profile, add the cpu-quota.crio.io: disable annotation to every guaranteed pod with pinned CPUs. This annotation is necessary to prevent the degradation of the process performance within the pod. If the realTime workload hint is not explicitly set, it defaults to true.

For more information how combinations of power consumption and real-time settings impact latency, see Understanding workload hints.

13.2.4. Configuring power saving for nodes that run colocated high and low priority workloads

You can enable power savings for a node that has low priority workloads that are colocated with high priority workloads without impacting the latency or throughput of the high priority workloads. Power saving is possible without modifications to the workloads themselves.

Important

The feature is supported on Intel Ice Lake and later generations of Intel CPUs. The capabilities of the processor might impact the latency and throughput of the high priority workloads.

Prerequisites

  • You enabled C-states and operating system controlled P-states in the BIOS

Procedure

  1. Generate a PerformanceProfile with the per-pod-power-management argument set to true:

    $ podman run --entrypoint performance-profile-creator -v \
    /must-gather:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-operator:v4.17 \
    --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=low-latency \ 1
    --per-pod-power-management=true > my-performance-profile.yaml
    1
    The power-consumption-mode argument must be default or low-latency when the per-pod-power-management argument is set to true.

    Example PerformanceProfile with perPodPowerManagement

    apiVersion: performance.openshift.io/v2
    kind: PerformanceProfile
    metadata:
         name: performance
    spec:
        [.....]
        workloadHints:
            realTime: true
            highPowerConsumption: false
            perPodPowerManagement: true

  2. Set the default cpufreq governor as an additional kernel argument in the PerformanceProfile custom resource (CR):

    apiVersion: performance.openshift.io/v2
    kind: PerformanceProfile
    metadata:
         name: performance
    spec:
        ...
        additionalKernelArgs:
        - cpufreq.default_governor=schedutil 1
    1
    Using the schedutil governor is recommended, however, you can use other governors such as the ondemand or powersave governors.
  3. Set the maximum CPU frequency in the TunedPerformancePatch CR:

    spec:
      profile:
      - data: |
          [sysfs]
          /sys/devices/system/cpu/intel_pstate/max_perf_pct = <x> 1
    1
    The max_perf_pct controls the maximum frequency that the cpufreq driver is allowed to set as a percentage of the maximum supported cpu frequency. This value applies to all CPUs. You can check the maximum supported frequency in /sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq. As a starting point, you can use a percentage that caps all CPUs at the All Cores Turbo frequency. The All Cores Turbo frequency is the frequency that all cores will run at when the cores are all fully occupied.

13.2.5. 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 Node Tuning Operator:

Table 13.4. Process' CPU assignments
Process typeDetails

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 4.17 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

  1. Create a performance profile appropriate for the environment’s hardware and topology.
  2. 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.

13.2.6. Configuring Hyper-Threading for a cluster

To configure Hyper-Threading 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 Hyper-Threading 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 Hyper-Threading 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

  1. 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 shell prompt and run the following:

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

    Example output

    0-4

  2. 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

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

When Hyper-Threading is enabled, all guaranteed pods must use multiples of the simultaneous multi-threading (SMT) level to avoid a "noisy neighbor" situation that can cause the pod to fail. See Static policy options for more information.

13.2.6.1. Disabling Hyper-Threading for low latency applications

When configuring clusters for low latency processing, consider whether you want to disable Hyper-Threading before you deploy the cluster. To disable Hyper-Threading, perform the following steps:

  1. Create a performance profile that is appropriate for your hardware and topology.
  2. 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.

13.2.7. Managing device interrupt processing for guaranteed pod isolated CPUs

The Node Tuning 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, Node Tuning Operator does not disable device interrupts on isolated CPUs.

13.2.7.1. Finding the effective IRQ affinity setting for a node

Some IRQ controllers lack support for IRQ affinity setting and will always expose all online CPUs as the IRQ mask. These IRQ controllers effectively run on CPU 0.

The following are examples of drivers and hardware that Red Hat are aware lack support for IRQ affinity setting. The list is, by no means, exhaustive:

  • Some RAID controller drivers, such as megaraid_sas
  • Many non-volatile memory express (NVMe) drivers
  • Some LAN on motherboard (LOM) network controllers
  • The driver uses managed_irqs
Note

The reason they do not support IRQ affinity setting might be associated with factors such as the type of processor, the IRQ controller, or the circuitry connections in the motherboard.

If the effective affinity of any IRQ is set to an isolated CPU, it might be a sign of some hardware or driver not supporting IRQ affinity setting. To find the effective affinity, log in to the host and run the following command:

$ find /proc/irq -name effective_affinity -printf "%p: " -exec cat {} \;

Example output

/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

Some drivers use managed_irqs, whose affinity is managed internally by the kernel and userspace cannot change the affinity. In some cases, these IRQs might be assigned to isolated CPUs. For more information about managed_irqs, see Affinity of managed interrupts cannot be changed even if they target isolated CPU.

13.2.7.2. Configuring node interrupt affinity

Configure a cluster node for IRQ dynamic load balancing to control which cores can receive device interrupt requests (IRQ).

Prerequisites

  • For core isolation, all server hardware components must support IRQ affinity. To check if the hardware components of your server support IRQ affinity, view the server’s hardware specifications or contact your hardware provider.

Procedure

  1. Log in to the OpenShift Container Platform cluster as a user with cluster-admin privileges.
  2. Set the performance profile apiVersion to use performance.openshift.io/v2.
  3. Remove the globallyDisableIrqLoadBalancing field or set it to false.
  4. 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, operating system processes, kernel processes, and systemd services run on reserved CPUs. Infrastructure pods run on any CPU except where the low latency workload is running. Low latency workload pods run on exclusive CPUs from the isolated pool. For more information, see "Restricting CPUs for infra and application containers".

13.2.8. Configuring huge pages

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.

OpenShift Container Platform provides a method for creating and allocating huge pages. Node Tuning 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-###:  ###

13.2.8.1. 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 Node Tuning 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

13.2.9. Reducing NIC queues using the Node Tuning Operator

The Node Tuning Operator facilitates reducing NIC queues for enhanced performance. Adjustments are made using the performance profile, allowing customization of queues for different network devices.

13.2.9.1. 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

  1. Log in to the OpenShift Container Platform cluster running the Node Tuning Operator as a user with cluster-admin privileges.
  2. 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.
  3. Edit this created performance profile:

    $ oc edit -f <your_profile_name>.yaml
  4. 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.

  5. 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: 3-51,55-103
        reserved: 0-2,52-54
      net:
        userLevelNetworking: true
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
  6. 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: 3-51,55-103
        reserved: 0-2,52-54
      net:
        userLevelNetworking: true
        devices:
        - interfaceName: "eth0"
        - interfaceName: "eth1"
        - vendorID: "0x1af4"
          deviceID: "0x1000"
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
  7. 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: 3-51,55-103
        reserved: 0-2,52-54
      net:
        userLevelNetworking: true
        devices:
        - interfaceName: "eth*"
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
  8. 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: 3-51,55-103
        reserved: 0-2,52-54
      net:
        userLevelNetworking: true
        devices:
        - interfaceName: "!eno1"
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
  9. 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: 3-51,55-103
        reserved: 0-2,52-54
      net:
        userLevelNetworking: true
        devices:
        - interfaceName: "eth0"
        - vendorID: "0x1af4"
          deviceID: "0x1000"
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: ""
  10. Apply the updated performance profile:

    $ oc apply -f <your_profile_name>.yaml

Additional resources

13.2.9.2. 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.

13.2.9.3. 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

13.3. Provisioning real-time and low latency workloads

Many organizations need high performance computing and low, predictable latency, especially in the financial and telecommunications industries.

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. You use the performance profile configuration to make these changes. You can update the kernel to kernel-rt, 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.

Note

When writing your applications, follow the general recommendations described in RHEL for Real Time processes and threads.

Additional resources

13.3.1. Scheduling a low latency workload onto a worker with real-time capabilities

You can schedule low latency workloads onto a worker node where a performance profile that configures real-time capabilities is applied.

Note

To schedule the workload on specific nodes, use label selectors in the Pod custom resource (CR). The label selectors must match the nodes that are attached to the machine config pool that was configured for low latency by the Node Tuning Operator.

Prerequisites

  • You have installed the OpenShift CLI (oc).
  • You have logged in as a user with cluster-admin privileges.
  • You have applied a performance profile in the cluster that tunes worker nodes for low latency workloads.

Procedure

  1. Create a Pod CR for the low latency workload and apply it in the cluster, for example:

    Example Pod spec configured to use real-time processing

    apiVersion: v1
    kind: Pod
    metadata:
      name: dynamic-low-latency-pod
      annotations:
        cpu-quota.crio.io: "disable" 1
        cpu-load-balancing.crio.io: "disable" 2
        irq-load-balancing.crio.io: "disable" 3
    spec:
      securityContext:
        runAsNonRoot: true
        seccompProfile:
          type: RuntimeDefault
      containers:
      - name: dynamic-low-latency-pod
        image: "registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17"
        command: ["sleep", "10h"]
        resources:
          requests:
            cpu: 2
            memory: "200M"
          limits:
            cpu: 2
            memory: "200M"
        securityContext:
          allowPrivilegeEscalation: false
          capabilities:
            drop: [ALL]
      nodeSelector:
        node-role.kubernetes.io/worker-cnf: "" 4
      runtimeClassName: performance-dynamic-low-latency-profile 5
    # ...

    1
    Disables the CPU completely fair scheduler (CFS) quota at the pod run time.
    2
    Disables CPU load balancing.
    3
    Opts the pod out of interrupt handling on the node.
    4
    The nodeSelector label must match the label that you specify in the Node CR.
    5
    runtimeClassName must match the name of the performance profile configured in the cluster.
  2. Enter the pod runtimeClassName in the form performance-<profile_name>, where <profile_name> is the name from the PerformanceProfile YAML. In the previous example, the name is performance-dynamic-low-latency-profile.
  3. 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
    dynamic-low-latency-pod  1/1     Running   0          5h33m   10.131.0.10  cnf-worker.example.com

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

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

    Expected output

    Cpus_allowed_list:  2-3

Verification

Ensure the node configuration is applied correctly.

  1. Log in to the node to verify the configuration.

    $ oc debug node/<node-name>
  2. Verify that you can use the node file system:

    sh-4.4# chroot /host

    Expected output

    sh-4.4#

  3. Ensure the default system CPU affinity mask does not include the dynamic-low-latency-pod CPUs, for example, CPUs 2 and 3.

    sh-4.4# cat /proc/irq/default_smp_affinity

    Example output

    33

  4. Ensure the system IRQs are not configured to run on the dynamic-low-latency-pod CPUs:

    sh-4.4# 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

Warning

When you tune nodes for low latency, the usage of execution probes in conjunction with applications that require guaranteed CPUs can cause latency spikes. Use other probes, such as a properly configured set of network probes, as an alternative.

13.3.2. Creating a pod with a guaranteed QoS class

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:
  securityContext:
    runAsNonRoot: true
    seccompProfile:
      type: RuntimeDefault
  containers:
  - name: qos-demo-ctr
    image: <image-pull-spec>
    resources:
      limits:
        memory: "200Mi"
        cpu: "1"
      requests:
        memory: "200Mi"
        cpu: "1"
    securityContext:
      allowPrivilegeEscalation: false
      capabilities:
        drop: [ALL]
  1. Create the pod:

    $ oc  apply -f qos-pod.yaml --namespace=qos-example
  2. 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 you specify a memory limit for a container, but do not specify a memory request, OpenShift Container Platform automatically assigns a memory request that matches the limit. Similarly, if you specify a CPU limit for a container, but do not specify a CPU request, OpenShift Container Platform automatically assigns a CPU request that matches the limit.

13.3.3. Disabling CPU load balancing in a Pod

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 Node Tuning 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 the default runtime handler except that 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.

13.3.4. Disabling power saving mode for high priority pods

You can configure pods to ensure that high priority workloads are unaffected when you configure power saving for the node that the workloads run on.

When you configure a node with a power saving configuration, you must configure high priority workloads with performance configuration at the pod level, which means that the configuration applies to all the cores used by the pod.

By disabling P-states and C-states at the pod level, you can configure high priority workloads for best performance and lowest latency.

Table 13.5. Configuration for high priority workloads
AnnotationPossible ValuesDescription

cpu-c-states.crio.io:

  • "enable"
  • "disable"
  • "max_latency:microseconds"

This annotation allows you to enable or disable C-states for each CPU. Alternatively, you can also specify a maximum latency in microseconds for the C-states. For example, enable C-states with a maximum latency of 10 microseconds with the setting cpu-c-states.crio.io: "max_latency:10". Set the value to "disable" to provide the best performance for a pod.

cpu-freq-governor.crio.io:

Any supported cpufreq governor.

Sets the cpufreq governor for each CPU. The "performance" governor is recommended for high priority workloads.

Prerequisites

  • You have configured power saving in the performance profile for the node where the high priority workload pods are scheduled.

Procedure

  1. Add the required annotations to your high priority workload pods. The annotations override the default settings.

    Example high priority workload annotation

    apiVersion: v1
    kind: Pod
    metadata:
      #...
      annotations:
        #...
        cpu-c-states.crio.io: "disable"
        cpu-freq-governor.crio.io: "performance"
        #...
      #...
    spec:
      #...
      runtimeClassName: performance-<profile_name>
      #...

  2. Restart the pods to apply the annotation.

13.3.5. Disabling CPU CFS quota

To eliminate CPU throttling for pinned pods, create a pod with the cpu-quota.crio.io: "disable" annotation. This annotation disables the CPU completely fair scheduler (CFS) quota when the pod runs.

Example pod specification with cpu-quota.crio.io disabled

apiVersion: v1
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. For example, pods that contain CPU-pinned containers. Otherwise, disabling CPU CFS quota can affect the performance of other containers in the cluster.

13.3.6. Disabling interrupt processing for CPUs where pinned containers are running

To achieve low latency for workloads, some containers require that the CPUs they are pinned to do not process device interrupts. A pod annotation, irq-load-balancing.crio.io, is used to define whether device interrupts are processed or not on the CPUs where the pinned containers are running. When configured, CRI-O disables device interrupts where the pod containers are running.

To disable interrupt processing for CPUs where containers belonging to individual pods are pinned, 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>
...

13.4. Debugging low latency node tuning status

Use the PerformanceProfile custom resource (CR) status fields for reporting tuning status and debugging latency issues in the cluster node.

13.4.1. 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 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:

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.

13.4.1.1. 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 profiles that encompass kernel args, kube config, huge pages allocation, and deployment of rt-kernel. The Performance Profile 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 performanceProfile.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:

  1. 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

  2. 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

  3. 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

13.4.2. 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.

13.4.2.1. 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.

13.4.2.2. Gathering 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 Node Tuning 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.

Prerequisites

  • Access to the cluster as a user with the cluster-admin role.
  • The OpenShift Container Platform CLI (oc) installed.

Procedure

  1. Navigate to the directory where you want to store the must-gather data.
  2. Collect debugging information by running the following command:

    $ oc adm must-gather

    Example output

    [must-gather      ] OUT Using must-gather plug-in image: quay.io/openshift-release
    When opening a support case, bugzilla, or issue please include the following summary data along with any other requested information:
    ClusterID: 829er0fa-1ad8-4e59-a46e-2644921b7eb6
    ClusterVersion: Stable at "<cluster_version>"
    ClusterOperators:
    	All healthy and stable
    
    
    [must-gather      ] OUT namespace/openshift-must-gather-8fh4x created
    [must-gather      ] OUT clusterrolebinding.rbac.authorization.k8s.io/must-gather-rhlgc created
    [must-gather-5564g] POD 2023-07-17T10:17:37.610340849Z Gathering data for ns/openshift-cluster-version...
    [must-gather-5564g] POD 2023-07-17T10:17:38.786591298Z Gathering data for ns/default...
    [must-gather-5564g] POD 2023-07-17T10:17:39.117418660Z Gathering data for ns/openshift...
    [must-gather-5564g] POD 2023-07-17T10:17:39.447592859Z Gathering data for ns/kube-system...
    [must-gather-5564g] POD 2023-07-17T10:17:39.803381143Z Gathering data for ns/openshift-etcd...
    
    ...
    
    Reprinting Cluster State:
    When opening a support case, bugzilla, or issue please include the following summary data along with any other requested information:
    ClusterID: 829er0fa-1ad8-4e59-a46e-2644921b7eb6
    ClusterVersion: Stable at "<cluster_version>"
    ClusterOperators:
    	All healthy and stable

  3. 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.54213423446277122891
    1
    Replace must-gather-local.5421342344627712289// with the directory name created by the must-gather tool.
    Note

    Create a compressed file to attach the data to a support case or to use with the Performance Profile Creator wrapper script when you create a performance profile.

  4. Attach the compressed file to your support case on the Red Hat Customer Portal.

13.5. 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:v4.17.

13.5.1. Prerequisites for running latency tests

Your cluster must meet the following requirements before you can run the latency tests:

  1. You have configured a performance profile with the Node Tuning Operator.
  2. You have applied all the required CNF configurations in the cluster.
  3. You have a pre-existing MachineConfigPool CR applied in the cluster. The default worker pool is worker-cnf.

13.5.2. 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 13.6. Latency test environment variables
Environment variablesDescription

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.

Note

To prevent the Ginkgo 2.0 test suite from timing out before the latency tests complete, set the -ginkgo.timeout flag to a value greater than LATENCY_TEST_RUNTIME + 2 minutes. If you also set a LATENCY_TEST_DELAY value then you must set -ginkgo.timeout to a value greater than LATENCY_TEST_RUNTIME + LATENCY_TEST_DELAY + 2 minutes. The default timeout value for the Ginkgo 2.0 test suite is 1 hour.

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.

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.

13.5.3. Running the latency tests

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

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

  1. 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.

  2. Run the latency tests by entering the following command:

    $ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
    -e LATENCY_TEST_RUNTIME=<time_in_seconds>\
    -e MAXIMUM_LATENCY=<time_in_microseconds> \
    registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 /usr/bin/test-run.sh \
    --ginkgo.v --ginkgo.timeout="24h"
  3. Optional: Append --ginkgo.dryRun flag to run the latency tests in dry-run mode. This is useful for checking what commands the tests run.
  4. Optional: Append --ginkgo.v flag to run the tests with increased verbosity.
  5. Optional: Append --ginkgo.timeout="24h" flag to ensure the Ginkgo 2.0 test suite does not timeout before the latency tests complete.

    Important

    The default runtime for each test is 300 seconds. For valid latency test results, run the tests for at least 12 hours by updating the LATENCY_TEST_RUNTIME variable.

13.5.3.1. Running hwlatdetect

The hwlatdetect tool is available in the rt-kernel package with a regular subscription of Red Hat Enterprise Linux (RHEL) 9.x.

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.

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_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
    registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 \
    /usr/bin/test-run.sh --ginkgo.focus="hwlatdetect" --ginkgo.v --ginkgo.timeout="24h"

    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/cnftests -ginkgo.v -ginkgo.focus=hwlatdetect
    I0908 15:25:20.023712      27 request.go:601] Waited for 1.046586367s due to client-side throttling, not priority and fairness, request: GET:https://api.hlxcl6.lab.eng.tlv2.redhat.com:6443/apis/imageregistry.operator.openshift.io/v1?timeout=32s
    Running Suite: CNF Features e2e integration tests
    =================================================
    Random Seed: 1662650718
    Will run 1 of 3 specs
    
    [...]
    
    • Failure [283.574 seconds]
    [performance] Latency Test
    /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:62
      with the hwlatdetect image
      /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:228
        should succeed [It]
        /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:236
    
        Log file created at: 2022/09/08 15:25:27
        Running on machine: hwlatdetect-b6n4n
        Binary: Built with gc go1.17.12 for linux/amd64
        Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg
        I0908 15:25:27.160620       1 node.go:39] Environment information: /proc/cmdline: BOOT_IMAGE=(hd1,gpt3)/ostree/rhcos-c6491e1eedf6c1f12ef7b95e14ee720bf48359750ac900b7863c625769ef5fb9/vmlinuz-4.18.0-372.19.1.el8_6.x86_64 random.trust_cpu=on console=tty0 console=ttyS0,115200n8 ignition.platform.id=metal ostree=/ostree/boot.1/rhcos/c6491e1eedf6c1f12ef7b95e14ee720bf48359750ac900b7863c625769ef5fb9/0 ip=dhcp root=UUID=5f80c283-f6e6-4a27-9b47-a287157483b2 rw rootflags=prjquota boot=UUID=773bf59a-bafd-48fc-9a87-f62252d739d3 skew_tick=1 nohz=on rcu_nocbs=0-3 tuned.non_isolcpus=0000ffff,ffffffff,fffffff0 systemd.cpu_affinity=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 intel_iommu=on iommu=pt isolcpus=managed_irq,0-3 nohz_full=0-3 tsc=nowatchdog nosoftlockup nmi_watchdog=0 mce=off skew_tick=1 rcutree.kthread_prio=11 + +
        I0908 15:25:27.160830       1 node.go:46] Environment information: kernel version 4.18.0-372.19.1.el8_6.x86_64
        I0908 15:25:27.160857       1 main.go:50] running the hwlatdetect command with arguments [/usr/bin/hwlatdetect --threshold 1 --hardlimit 1 --duration 100 --window 10000000us --width 950000us]
        F0908 15:27:10.603523       1 main.go:53] failed to run hwlatdetect command; out: hwlatdetect:  test duration 100 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: 326us 2
        Samples recorded: 5
        Samples exceeding threshold: 5
        ts: 1662650739.017274507, inner:6, outer:6
        ts: 1662650749.257272414, inner:14, outer:326
        ts: 1662650779.977272835, inner:314, outer:12
        ts: 1662650800.457272384, inner:3, outer:9
        ts: 1662650810.697273520, inner:3, outer:2
    
    [...]
    
    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/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:476
    
    Ran 1 of 194 Specs in 365.797 seconds
    FAIL! -- 0 Passed | 1 Failed | 0 Pending | 2 Skipped
    --- FAIL: TestTest (366.08s)
    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.

13.5.3.2. Running cyclictest

The cyclictest tool measures the real-time kernel scheduler latency on the specified CPUs.

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.

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 Node Tuning 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_CPUS=10 -e LATENCY_TEST_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
    registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 \
    /usr/bin/test-run.sh --ginkgo.focus="cyclictest" --ginkgo.v --ginkgo.timeout="24h"

    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

    running /usr/bin/cnftests -ginkgo.v -ginkgo.focus=cyclictest
    I0908 13:01:59.193776      27 request.go:601] Waited for 1.046228824s due to client-side throttling, not priority and fairness, request: GET:https://api.compute-1.example.com:6443/apis/packages.operators.coreos.com/v1?timeout=32s
    Running Suite: CNF Features e2e integration tests
    =================================================
    Random Seed: 1662642118
    Will run 1 of 3 specs
    
    [...]
    
    Summarizing 1 Failure:
    
    [Fail] [performance] Latency Test with the cyclictest image [It] should succeed
    /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:220
    
    Ran 1 of 194 Specs in 161.151 seconds
    FAIL! -- 0 Passed | 1 Failed | 0 Pending | 2 Skipped
    --- FAIL: TestTest (161.48s)
    FAIL

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

13.5.3.3. 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.

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.

Prerequisites

  • You have logged in to registry.redhat.io with your Customer Portal credentials.
  • You have applied a cluster performance profile by using the Node Tuning 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_CPUS=10 -e LATENCY_TEST_RUNTIME=600 -e MAXIMUM_LATENCY=20 \
    registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 \
    /usr/bin/test-run.sh --ginkgo.focus="oslat" --ginkgo.v --ginkgo.timeout="24h"

    LATENCY_TEST_CPUS specifies the number 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/cnftests -ginkgo.v -ginkgo.focus=oslat
    I0908 12:51:55.999393      27 request.go:601] Waited for 1.044848101s due to client-side throttling, not priority and fairness, request: GET:https://compute-1.example.com:6443/apis/machineconfiguration.openshift.io/v1?timeout=32s
    Running Suite: CNF Features e2e integration tests
    =================================================
    Random Seed: 1662641514
    Will run 1 of 3 specs
    
    [...]
    
    • Failure [77.833 seconds]
    [performance] Latency Test
    /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:62
      with the oslat image
      /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:128
        should succeed [It]
        /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:153
    
        The current latency 304 is bigger than the expected one 1 : 1
    
    [...]
    
    Summarizing 1 Failure:
    
    [Fail] [performance] Latency Test with the oslat image [It] should succeed
    /remote-source/app/vendor/github.com/openshift/cluster-node-tuning-operator/test/e2e/performanceprofile/functests/4_latency/latency.go:177
    
    Ran 1 of 194 Specs in 161.091 seconds
    FAIL! -- 0 Passed | 1 Failed | 0 Pending | 2 Skipped
    --- FAIL: TestTest (161.42s)
    FAIL

    1
    In this example, the measured latency is outside the maximum allowed value.

13.5.4. 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 registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 \
    /usr/bin/test-run.sh --report <report_folder_path> --ginkgo.v

    where:

    <report_folder_path>
    Is the path to the folder where the report is generated.

13.5.5. 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:

    Note

    You must create the junit folder before running this command.

    $ podman run -v $(pwd)/:/kubeconfig:Z -v $(pwd)/junit:/junit \
    -e KUBECONFIG=/kubeconfig/kubeconfig registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 \
    /usr/bin/test-run.sh --ginkgo.junit-report junit/<file-name>.xml --ginkgo.v

    where:

    junit
    Is the folder where the junit report is stored.

13.5.6. Running latency tests on a single-node OpenShift cluster

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

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.

Prerequisites

  • You have installed the OpenShift CLI (oc).
  • You have logged in as a user with cluster-admin privileges.
  • You have applied a cluster performance profile by using the Node Tuning Operator.

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 LATENCY_TEST_RUNTIME=<time_in_seconds> registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 \
    /usr/bin/test-run.sh --ginkgo.v --ginkgo.timeout="24h"
    Note

    The default runtime for each test is 300 seconds. For valid latency test results, run the tests for at least 12 hours by updating the LATENCY_TEST_RUNTIME variable. To run the buckets latency validation step, you must specify a maximum latency. For details on maximum latency variables, see the table in the "Measuring latency" section.

    After running the test suite, all the dangling resources are cleaned up.

13.5.7. 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:

  1. Mirroring the cnf-tests image to the custom disconnected registry.
  2. 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.

  1. 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:v4.17 \
    /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/.
  2. 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 IMAGE_REGISTRY="<disconnected_registry>" \
    -e CNF_TESTS_IMAGE="cnf-tests-rhel8:v4.17" \
    -e LATENCY_TEST_RUNTIME=<time_in_seconds> \
    <disconnected_registry>/cnf-tests-rhel8:v4.17 /usr/bin/test-run.sh --ginkgo.v --ginkgo.timeout="24h"
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>" \
    -e LATENCY_TEST_RUNTIME=<time_in_seconds> \
    registry.redhat.io/openshift4/cnf-tests-rhel8:v4.17 /usr/bin/test-run.sh --ginkgo.v --ginkgo.timeout="24h"

    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

  1. 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
  2. Fetch the registry endpoint by running the following command:

    $ REGISTRY=$(oc get route default-route -n openshift-image-registry --template='{{ .spec.host }}')
  3. Create a namespace for exposing the images:

    $ oc create ns cnftests
  4. 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
  5. 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')
  6. Create a dockerauth.json file, for example:

    $ echo "{\"auths\": { \"$REGISTRY\": { \"auth\": $TOKEN } }}" > dockerauth.json
  7. Do the image mirroring:

    $ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
    registry.redhat.io/openshift4/cnf-tests-rhel8:4.17 \
    /usr/bin/mirror -registry $REGISTRY/cnftests |  oc image mirror --insecure=true \
    -a=$(pwd)/dockerauth.json -f -
  8. Run the tests:

    $ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \
    -e LATENCY_TEST_RUNTIME=<time_in_seconds> \
    -e IMAGE_REGISTRY=image-registry.openshift-image-registry.svc:5000/cnftests cnf-tests-local:latest /usr/bin/test-run.sh --ginkgo.v --ginkgo.timeout="24h"
Mirroring a different set of test images

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

Procedure

  1. 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:4.17"
        }
    ]
  2. 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:v4.17 /usr/bin/mirror \
    --registry "my.local.registry:5000/" --images "/kubeconfig/images.json" \
    |  oc image mirror -f -

13.5.8. 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:v4.17 \
    oc get nodes

    If this command does not work, an error related to spanning across DNS, MTU size, or firewall access might be occurring.

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