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Chapter 20. Workload partitioning

In resource-constrained environments, you can use workload partitioning to isolate OpenShift Container Platform services, cluster management workloads, and infrastructure pods to run on a reserved set of CPUs.

The minimum number of reserved CPUs required for the cluster management is four CPU Hyper-Threads (HTs). With workload partitioning, you annotate the set of cluster management pods and a set of typical add-on Operators for inclusion in the cluster management workload partition. These pods operate normally within the minimum size CPU configuration. Additional Operators or workloads outside of the set of minimum cluster management pods require additional CPUs to be added to the workload partition.

Workload partitioning isolates user workloads from platform workloads using standard Kubernetes scheduling capabilities.

The following changes are required for workload partitioning:

  1. In the 

    install-config.yaml
    file, add the additional field: 
    cpuPartitioningMode
    . 

    apiVersion: v1
baseDomain: devcluster.openshift.com
cpuPartitioningMode: AllNodes
    1 
    
compute:
  - architecture: amd64
    hyperthreading: Enabled
    name: worker
    platform: {}
    replicas: 3
controlPlane:
  architecture: amd64
  hyperthreading: Enabled
  name: master
  platform: {}
  replicas: 3
    1
    Sets up a cluster for CPU partitioning at install time. The default value is None.
    Note

    Workload partitioning can only be enabled during cluster installation. You cannot disable workload partitioning postinstallation.

  2. In the performance profile, specify the 

    isolated
    and 
    reserved
    CPUs.

    Recommended performance profile configuration

    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

    Expand
    Table 20.1. PerformanceProfile CR options for single-node OpenShift clusters
    PerformanceProfile CR fieldDescription

    metadata.name

    Ensure that 

    name
    matches the following fields set in related GitOps ZTP custom resources (CRs): 

    • include=openshift-node-performance-${PerformanceProfile.metadata.name}
      in 
      TunedPerformancePatch.yaml
       
    • name: 50-performance-${PerformanceProfile.metadata.name}
      in 
      validatorCRs/informDuValidator.yaml
       

    spec.additionalKernelArgs
     

    "efi=runtime"
    Configures UEFI secure boot for the cluster host. 

    spec.cpu.isolated

    Set the isolated CPUs. Ensure all of the Hyper-Threading pairs match.

    Important

    The reserved and isolated CPU pools must not overlap and together must span all available cores. CPU cores that are not accounted for cause an undefined behaviour in the system. 

    spec.cpu.reserved

    Set the reserved CPUs. When workload partitioning is enabled, system processes, kernel threads, and system container threads are restricted to these CPUs. All CPUs that are not isolated should be reserved. 

    spec.hugepages.pages

    • Set the number of huge pages (
      count
      )
    • Set the huge pages size (
      size
      ).
    • Set 
      node
      to the NUMA node where the 
      hugepages
      are allocated (
      node
      ) 

    spec.realTimeKernel

    Set 

    enabled
    to 
    true
    to use the realtime kernel. 

    spec.workloadHints

    Use 

    workloadHints
    to define the set of top level flags for different type of workloads. The example configuration configures the cluster for low latency and high performance.

Workload partitioning introduces an extended 

management.workload.openshift.io/cores
resource type for platform pods. kubelet advertises the resources and CPU requests by pods allocated to the pool within the corresponding resource. When workload partitioning is enabled, the 
management.workload.openshift.io/cores
resource allows the scheduler to correctly assign pods based on the 
cpushares
capacity of the host, not just the default 
cpuset
.

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