Chapter 15. 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. 15.1. 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. Note In Telco, clusters using PerformanceProfile for low latency, real-time, and Data Plane Development Kit (DPDK) workloads automatically revert to cgroups v1 due to the lack of cgroups v2 support. Enabling cgroup v2 is not supported if you are using PerformanceProfile . 15.1.1. 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. 15.1.2. 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 Label the target nodes for configuration by running the following command: USD 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. Create a MachineConfigPool resource containing the target nodes: 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. Apply the MachineConfigPool resource by running the following command: USD 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: USD 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 15.1.3. 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 Navigate to the directory where you want to store the must-gather data. Collect cluster information by running the following command: USD 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 . Optional: Create a compressed file from the must-gather directory: USD 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 For more information about the must-gather tool, see Gathering data about your cluster . 15.1.4. 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 Check the machine config pool by running the following command: USD 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 Use Podman to authenticate to registry.redhat.io by running the following command: USD podman login registry.redhat.io Username: <user_name> Password: <password> Optional: Display help for the PPC tool by running the following command: USD podman run --rm --entrypoint performance-profile-creator registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 -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 To display information about the cluster, run the PPC tool with the log argument by running the following command: USD podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 --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=--- Create a performance profile by running the following command. The example uses sample PPC arguments and values: USD podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 --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: []" Review the created YAML file by running the following command: USD 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 Apply the generated profile: USD oc apply -f my-performance-profile.yaml Example output performanceprofile.performance.openshift.io/performance created 15.1.5. 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 Create a file on your local machine named, for example, run-perf-profile-creator.sh : USD vi run-perf-profile-creator.sh Paste the following code into the file: #!/bin/bash readonly CONTAINER_RUNTIME=USD{CONTAINER_RUNTIME:-podman} readonly CURRENT_SCRIPT=USD(basename "USD0") readonly CMD="USD{CONTAINER_RUNTIME} run --entrypoint performance-profile-creator" readonly IMG_EXISTS_CMD="USD{CONTAINER_RUNTIME} image exists" readonly IMG_PULL_CMD="USD{CONTAINER_RUNTIME} image pull" readonly MUST_GATHER_VOL="/must-gather" NTO_IMG="registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15" MG_TARBALL="" DATA_DIR="" usage() { print "Wrapper usage:" print " USD{CURRENT_SCRIPT} [-h] [-p image][-t path] -- [performance-profile-creator flags]" print "" print "Options:" print " -h help for USD{CURRENT_SCRIPT}" print " -p Node Tuning Operator image" print " -t path to a must-gather tarball" USD{IMG_EXISTS_CMD} "USD{NTO_IMG}" && USD{CMD} "USD{NTO_IMG}" -h } function cleanup { [ -d "USD{DATA_DIR}" ] && rm -rf "USD{DATA_DIR}" } trap cleanup EXIT exit_error() { print "error: USD*" usage exit 1 } print() { echo "USD*" >&2 } check_requirements() { USD{IMG_EXISTS_CMD} "USD{NTO_IMG}" || USD{IMG_PULL_CMD} "USD{NTO_IMG}" || \ exit_error "Node Tuning Operator image not found" [ -n "USD{MG_TARBALL}" ] || exit_error "Must-gather tarball file path is mandatory" [ -f "USD{MG_TARBALL}" ] || exit_error "Must-gather tarball file not found" DATA_DIR=USD(mktemp -d -t "USD{CURRENT_SCRIPT}XXXX") || exit_error "Cannot create the data directory" tar -zxf "USD{MG_TARBALL}" --directory "USD{DATA_DIR}" || exit_error "Cannot decompress the must-gather tarball" chmod a+rx "USD{DATA_DIR}" return 0 } main() { while getopts ':hp:t:' OPT; do case "USD{OPT}" in h) usage exit 0 ;; p) NTO_IMG="USD{OPTARG}" ;; t) MG_TARBALL="USD{OPTARG}" ;; ?) exit_error "invalid argument: USD{OPTARG}" ;; esac done shift USD((OPTIND - 1)) check_requirements || exit 1 USD{CMD} -v "USD{DATA_DIR}:USD{MUST_GATHER_VOL}:z" "USD{NTO_IMG}" "USD@" --must-gather-dir-path "USD{MUST_GATHER_VOL}" echo "" 1>&2 } main "USD@" Add execute permissions for everyone on this script: USD chmod a+x run-perf-profile-creator.sh Use Podman to authenticate to registry.redhat.io by running the following command: USD podman login registry.redhat.io Username: <user_name> Password: <password> Optional: Display help for the PPC tool by running the following command: USD ./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 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:v4.15 . To display information about the cluster, run the PPC tool with the log argument by running the following command: USD ./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=--- Create a performance profile by running the following command. USD ./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 . Review the created YAML file by running the following command: USD 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 Apply the generated profile: USD oc apply -f my-performance-profile.yaml Example output performanceprofile.performance.openshift.io/performance created 15.1.6. Performance Profile Creator arguments Table 15.1. Required Performance Profile Creator arguments Argument Description 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 15.2. Optional Performance Profile Creator arguments Argument Description 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. 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 . 15.1.7. Reference performance profiles Use the following reference performance profiles as the basis to develop your own custom profiles. 15.1.7.1. 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. 15.1.7.2. 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-USD{PerformanceProfile.metadata.name} # Also in file 'validatorCRs/informDuValidator.yaml': # name: 50-performance-USD{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: USDisolated reserved: USDreserved hugepages: defaultHugepagesSize: USDdefaultHugepagesSize pages: - size: USDsize count: USDcount node: USDnode machineConfigPoolSelector: pools.operator.machineconfiguration.openshift.io/USDmcp: "" nodeSelector: node-role.kubernetes.io/USDmcp: '' 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 15.1.7.3. 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-USD{PerformanceProfile.metadata.name} # Also in file 'validatorCRs/informDuValidator.yaml': # name: 50-performance-USD{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: USDisolated reserved: USDreserved hugepages: defaultHugepagesSize: USDdefaultHugepagesSize pages: - size: USDsize count: USDcount node: USDnode machineConfigPoolSelector: pools.operator.machineconfiguration.openshift.io/USDmcp: "" nodeSelector: node-role.kubernetes.io/USDmcp: '' 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 15.2. 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 . 15.3. 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 15.3. Impact of combinations of power consumption and real-time settings on latency Performance Profile creator setting Hint Environment Description 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 . 15.4. 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 Generate a PerformanceProfile with the per-pod-power-management argument set to true : USD podman run --entrypoint performance-profile-creator -v \ /must-gather:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 \ --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 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. 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. Additional resources About the Performance Profile Creator Disabling power saving mode for high priority pods Managing device interrupt processing for guaranteed pod isolated CPUs 15.5. 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 15.4. Process' CPU assignments Process type Details Burstable and BestEffort pods Runs on any CPU except where low latency workload is running Infrastructure pods Runs on any CPU except where low latency workload is running Interrupts Redirects to reserved CPUs (optional in OpenShift Container Platform 4.7 and later) Kernel processes Pins to reserved CPUs Latency-sensitive workload pods Pins to a specific set of exclusive CPUs from the isolated pool OS processes/systemd services Pins to reserved CPUs The allocatable capacity of cores on a node for pods of all QoS process types, Burstable , BestEffort , or Guaranteed , is equal to the capacity of the isolated pool. The capacity of the reserved pool is removed from the node's total core capacity for use by the cluster and operating system housekeeping duties. Example 1 A node features a capacity of 100 cores. Using a performance profile, the cluster administrator allocates 50 cores to the isolated pool and 50 cores to the reserved pool. The cluster administrator assigns 25 cores to QoS Guaranteed pods and 25 cores for BestEffort or Burstable pods. This matches the capacity of the isolated pool. Example 2 A node features a capacity of 100 cores. Using a performance profile, the cluster administrator allocates 50 cores to the isolated pool and 50 cores to the reserved pool. The cluster administrator assigns 50 cores to QoS Guaranteed pods and one core for BestEffort or Burstable pods. This exceeds the capacity of the isolated pool by one core. Pod scheduling fails because of insufficient CPU capacity. The exact partitioning pattern to use depends on many factors like hardware, workload characteristics and the expected system load. Some sample use cases are as follows: If the latency-sensitive workload uses specific hardware, such as a network interface controller (NIC), ensure that the CPUs in the isolated pool are as close as possible to this hardware. At a minimum, you should place the workload in the same Non-Uniform Memory Access (NUMA) node. The reserved pool is used for handling all interrupts. When depending on system networking, allocate a sufficiently-sized reserve pool to handle all the incoming packet interrupts. In 4.15 and later versions, workloads can optionally be labeled as sensitive. The decision regarding which specific CPUs should be used for reserved and isolated partitions requires detailed analysis and measurements. Factors like NUMA affinity of devices and memory play a role. The selection also depends on the workload architecture and the specific use case. Important The reserved and isolated CPU pools must not overlap and together must span all available cores in the worker node. To ensure that housekeeping tasks and workloads do not interfere with each other, specify two groups of CPUs in the spec section of the performance profile. isolated - Specifies the CPUs for the application container workloads. These CPUs have the lowest latency. Processes in this group have no interruptions and can, for example, reach much higher DPDK zero packet loss bandwidth. reserved - Specifies the CPUs for the cluster and operating system housekeeping duties. Threads in the reserved group are often busy. Do not run latency-sensitive applications in the reserved group. Latency-sensitive applications run in the isolated group. Procedure Create a performance profile appropriate for the environment's hardware and topology. Add the reserved and isolated parameters with the CPUs you want reserved and isolated for the infra and application containers: \ufeffapiVersion: 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. 15.6. 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 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: USD 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: USD cat /sys/devices/system/cpu/cpu0/topology/thread_siblings_list Example output 0-4 Apply the isolated and reserved CPUs in the PerformanceProfile YAML. For example, you can set logical cores CPU0 and CPU4 as isolated , and logical cores CPU1 to CPU3 and CPU5 to CPU7 as reserved . When you configure reserved and isolated CPUs, the infra containers in pods use the reserved CPUs and the application containers use the isolated CPUs. ... cpu: isolated: 0,4 reserved: 1-3,5-7 ... Note The reserved and isolated CPU pools must not overlap and together must span all available cores in the worker node. Important 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. 15.6.1. 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: Create a performance profile that is appropriate for your hardware and topology. Set nosmt as an additional kernel argument. The following example performance profile illustrates this setting: apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: example-performanceprofile spec: additionalKernelArgs: - nmi_watchdog=0 - audit=0 - mce=off - processor.max_cstate=1 - idle=poll - intel_idle.max_cstate=0 - nosmt cpu: isolated: 2-3 reserved: 0-1 hugepages: defaultHugepagesSize: 1G pages: - count: 2 node: 0 size: 1G nodeSelector: node-role.kubernetes.io/performance: '' realTimeKernel: enabled: true Note When you configure reserved and isolated CPUs, the infra containers in pods use the reserved CPUs and the application containers use the isolated CPUs. 15.7. 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. 15.7.1. 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: USD 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 . 15.7.2. 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 Log in to the OpenShift Container Platform cluster as a user with cluster-admin privileges. Set the performance profile apiVersion to use performance.openshift.io/v2 . Remove the globallyDisableIrqLoadBalancing field or set it to false . Set the appropriate isolated and reserved CPUs. The following snippet illustrates a profile that reserves 2 CPUs. IRQ load-balancing is enabled for pods running on the isolated CPU set: apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: dynamic-irq-profile spec: cpu: isolated: 2-5 reserved: 0-1 ... Note When you configure reserved and isolated CPUs, 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". 15.8. 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: USD 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: USD oc describe node worker-0.ocp4poc.example.com | grep -i huge Example output hugepages-1g=true hugepages-###: ### hugepages-###: ### 15.8.1. 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 15.9. 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. 15.9.1. 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 Log in to the OpenShift Container Platform cluster running the Node Tuning Operator as a user with cluster-admin privileges. Create and apply a performance profile appropriate for your hardware and topology. For guidance on creating a profile, see the "Creating a performance profile" section. Edit this created performance profile: USD oc edit -f <your_profile_name>.yaml Populate the spec field with the net object. The object list can contain two fields: userLevelNetworking is a required field specified as a boolean flag. If userLevelNetworking is true , the queue count is set to the reserved CPU count for all supported devices. The default is false . devices is an optional field specifying a list of devices that will have the queues set to the reserved CPU count. If the device list is empty, the configuration applies to all network devices. The configuration is as follows: interfaceName : This field specifies the interface name, and it supports shell-style wildcards, which can be positive or negative. Example wildcard syntax is as follows: <string> .* Negative rules are prefixed with an exclamation mark. To apply the net queue changes to all devices other than the excluded list, use !<device> , for example, !eno1 . vendorID : The network device vendor ID represented as a 16-bit hexadecimal number with a 0x prefix. deviceID : The network device ID (model) represented as a 16-bit hexadecimal number with a 0x prefix. Note When a deviceID is specified, the vendorID must also be defined. A device that matches all of the device identifiers specified in a device entry interfaceName , vendorID , or a pair of vendorID plus deviceID qualifies as a network device. This network device then has its net queues count set to the reserved CPU count. When two or more devices are specified, the net queues count is set to any net device that matches one of them. Set the queue count to the reserved CPU count for all devices by using this example performance profile: apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: manual spec: cpu: isolated: 3-51,55-103 reserved: 0-2,52-54 net: userLevelNetworking: true nodeSelector: node-role.kubernetes.io/worker-cnf: "" Set the queue count to the reserved CPU count for all devices matching any of the defined device identifiers by using this example performance profile: apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: manual spec: cpu: isolated: 3-51,55-103 reserved: 0-2,52-54 net: userLevelNetworking: true devices: - interfaceName: "eth0" - interfaceName: "eth1" - vendorID: "0x1af4" deviceID: "0x1000" nodeSelector: node-role.kubernetes.io/worker-cnf: "" 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: "" 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: "" 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: "" Apply the updated performance profile: USD oc apply -f <your_profile_name>.yaml Additional resources Creating a performance profile . 15.9.2. 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. USD ethtool -l <device> Verify the queue status before the profile is applied: USD 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: USD 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. USD ethtool -l <device> Verify the queue status after the profile is applied: USD 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: USD 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. 15.9.3. 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

Commands:
oc label node <node_name> node-role.kubernetes.io/worker-cnf="" 1
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
oc apply -f mcp-worker-cnf.yaml
machineconfigpool.machineconfiguration.openshift.io/worker-cnf created
oc get mcp
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
oc adm must-gather
tar cvaf must-gather.tar.gz <must_gather_folder> 1
oc get mcp
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
podman login registry.redhat.io
Username: <user_name> Password: <password>
podman run --rm --entrypoint performance-profile-creator registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 -h
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
podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 --info log --must-gather-dir-path /must-gather
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=---
podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 --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
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: []"
cat my-performance-profile.yaml
--- 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
oc apply -f my-performance-profile.yaml
performanceprofile.performance.openshift.io/performance created
vi run-perf-profile-creator.sh
#!/bin/bash readonly CONTAINER_RUNTIME=USD{CONTAINER_RUNTIME:-podman} readonly CURRENT_SCRIPT=USD(basename "USD0") readonly CMD="USD{CONTAINER_RUNTIME} run --entrypoint performance-profile-creator" readonly IMG_EXISTS_CMD="USD{CONTAINER_RUNTIME} image exists" readonly IMG_PULL_CMD="USD{CONTAINER_RUNTIME} image pull" readonly MUST_GATHER_VOL="/must-gather" NTO_IMG="registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15" MG_TARBALL="" DATA_DIR="" usage() { print "Wrapper usage:" print " USD{CURRENT_SCRIPT} [-h] [-p image][-t path] -- [performance-profile-creator flags]" print "" print "Options:" print " -h help for USD{CURRENT_SCRIPT}" print " -p Node Tuning Operator image" print " -t path to a must-gather tarball" USD{IMG_EXISTS_CMD} "USD{NTO_IMG}" && USD{CMD} "USD{NTO_IMG}" -h } function cleanup { [ -d "USD{DATA_DIR}" ] && rm -rf "USD{DATA_DIR}" } trap cleanup EXIT exit_error() { print "error: USD*" usage exit 1 } print() { echo "USD*" >&2 } check_requirements() { USD{IMG_EXISTS_CMD} "USD{NTO_IMG}" || USD{IMG_PULL_CMD} "USD{NTO_IMG}" || exit_error "Node Tuning Operator image not found" [ -n "USD{MG_TARBALL}" ] || exit_error "Must-gather tarball file path is mandatory" [ -f "USD{MG_TARBALL}" ] || exit_error "Must-gather tarball file not found" DATA_DIR=USD(mktemp -d -t "USD{CURRENT_SCRIPT}XXXX") || exit_error "Cannot create the data directory" tar -zxf "USD{MG_TARBALL}" --directory "USD{DATA_DIR}" || exit_error "Cannot decompress the must-gather tarball" chmod a+rx "USD{DATA_DIR}" return 0 } main() { while getopts ':hp:t:' OPT; do case "USD{OPT}" in h) usage exit 0 ;; p) NTO_IMG="USD{OPTARG}" ;; t) MG_TARBALL="USD{OPTARG}" ;; ?) exit_error "invalid argument: USD{OPTARG}" ;; esac done shift USD((OPTIND - 1)) check_requirements || exit 1 USD{CMD} -v "USD{DATA_DIR}:USD{MUST_GATHER_VOL}:z" "USD{NTO_IMG}" "USD@" --must-gather-dir-path "USD{MUST_GATHER_VOL}" echo "" 1>&2 } main "USD@"
chmod a+x run-perf-profile-creator.sh
podman login registry.redhat.io
Username: <user_name> Password: <password>
./run-perf-profile-creator.sh -h
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
./run-perf-profile-creator.sh -t /<path_to_must_gather_dir>/must-gather.tar.gz -- --info=log
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=---
./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
cat my-performance-profile.yaml
--- 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
oc apply -f my-performance-profile.yaml
performanceprofile.performance.openshift.io/performance created
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]
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
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-USD{PerformanceProfile.metadata.name} # Also in file 'validatorCRs/informDuValidator.yaml': # name: 50-performance-USD{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: USDisolated reserved: USDreserved hugepages: defaultHugepagesSize: USDdefaultHugepagesSize pages: - size: USDsize count: USDcount node: USDnode machineConfigPoolSelector: pools.operator.machineconfiguration.openshift.io/USDmcp: "" nodeSelector: node-role.kubernetes.io/USDmcp: '' 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
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-USD{PerformanceProfile.metadata.name} # Also in file 'validatorCRs/informDuValidator.yaml': # name: 50-performance-USD{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: USDisolated reserved: USDreserved hugepages: defaultHugepagesSize: USDdefaultHugepagesSize pages: - size: USDsize count: USDcount node: USDnode machineConfigPoolSelector: pools.operator.machineconfiguration.openshift.io/USDmcp: "" nodeSelector: node-role.kubernetes.io/USDmcp: '' 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
workloadHints: highPowerConsumption: false realTime: false
workloadHints: highPowerConsumption: false realTime: true
workloadHints: highPowerConsumption: true realTime: true
workloadHints: realTime: true highPowerConsumption: false perPodPowerManagement: true
apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: workload-hints spec: workloadHints: realTime: true highPowerConsumption: false perPodPowerManagement: false 1
podman run --entrypoint performance-profile-creator -v /must-gather:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.15 --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
apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: performance spec: [.....] workloadHints: realTime: true highPowerConsumption: false perPodPowerManagement: true
apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: performance spec: additionalKernelArgs: - cpufreq.default_governor=schedutil 1
spec: profile: - data: | [sysfs] /sys/devices/system/cpu/intel_pstate/max_perf_pct = <x> 1
\ufeffapiVersion: 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: ""
lscpu --all --extended
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
cat /sys/devices/system/cpu/cpu0/topology/thread_siblings_list
0-4
cpu: isolated: 0,4 reserved: 1-3,5-7
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
find /proc/irq -name effective_affinity -printf "%p: " -exec cat {} \;
/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
apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: name: dynamic-irq-profile spec: cpu: isolated: 2-5 reserved: 0-1
hugepages: defaultHugepagesSize: "1G" pages: - size: "1G" count: 4 node: 0 1
oc debug node/ip-10-0-141-105.ec2.internal
grep -i huge /proc/meminfo
AnonHugePages: ###### ## ShmemHugePages: 0 kB HugePages_Total: 2 HugePages_Free: 2 HugePages_Rsvd: 0 HugePages_Surp: 0 Hugepagesize: #### ## Hugetlb: #### ##
oc describe node worker-0.ocp4poc.example.com | grep -i huge
hugepages-1g=true hugepages-###: ### hugepages-###: ###
spec: hugepages: defaultHugepagesSize: 1G pages: - count: 1024 node: 0 size: 2M - count: 4 node: 1 size: 1G
oc edit -f <your_profile_name>.yaml
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: ""
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: ""
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: ""
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: ""
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: ""
oc apply -f <your_profile_name>.yaml
apiVersion: performance.openshift.io/v2 metadata: name: performance spec: kind: PerformanceProfile spec: cpu: reserved: 0-1 #total = 2 isolated: 2-8 net: userLevelNetworking: true
ethtool -l <device>
ethtool -l ens4
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
ethtool -l ens4
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
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
ethtool -l <device>
ethtool -l ens4
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
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
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
ethtool -l ens4
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
INFO tuned.plugins.base: instance net_test (net): assigning devices ens1, ens2, ens3
WARNING tuned.plugins.base: instance net_test: no matching devices available