Este contenido no está disponible en el idioma seleccionado.
Chapter 14. Low latency tuning
14.1. 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.
14.1.1. 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.
14.1.2. 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.
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.
Additional resources
14.2. 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.
14.2.1. 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.
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.
14.2.1.1. 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
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.
14.2.1.2. 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
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 theworker-cnf
label.
Create a
MachineConfigPool
resource containing the target nodes:Create a YAML file that defines the
MachineConfigPool
resource:Example
mcp-worker-cnf.yaml
fileapiVersion: 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
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
14.2.1.3. 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
-
Navigate to the directory where you want to store the
must-gather
data. 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
.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.
NoteCompressed 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.
14.2.1.4. 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".
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:
$ 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:$ podman login registry.redhat.io
Username: <user_name> Password: <password>
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:v4.16 -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:$ podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.16 --info log --must-gather-dir-path /must-gather
-
--entrypoint performance-profile-creator
defines the performance profile creator as a new entry point topodman
. -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.
-
The directory containing the
--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:
$ podman run --entrypoint performance-profile-creator -v <path_to_must_gather>:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-rhel9-operator:v4.16 --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.
-
The directory containing the
-
--mcp-name=worker-cnf
specifies theworker-=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.NoteThe
mcp-name
argument in this example is set toworker-cnf
based on the output of the commandoc 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:
$ 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:
$ oc apply -f my-performance-profile.yaml
Example output
performanceprofile.performance.openshift.io/performance created
14.2.1.5. 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".
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
:$ vi run-perf-profile-creator.sh
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:v4.16" 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 "$@"
Add execute permissions for everyone on this script:
$ chmod a+x run-perf-profile-creator.sh
Use Podman to authenticate to
registry.redhat.io
by running the following command:$ podman login registry.redhat.io
Username: <user_name> Password: <password>
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
NoteYou 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:v4.16
.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=---
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 theworker-=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.NoteThe
mcp-name
argument in this example is set toworker-cnf
based on the output of the commandoc get mcp
. For single-node OpenShift use--mcp-name=master
.
-
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
Apply the generated profile:
$ oc apply -f my-performance-profile.yaml
Example output
performanceprofile.performance.openshift.io/performance created
14.2.1.6. Performance Profile Creator arguments
Argument | Description |
---|---|
|
Name for MCP; for example, |
| 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 |
| Number of reserved CPUs. Use a natural number greater than zero. |
| Enables real-time kernel.
Possible values: |
Argument | Description |
---|---|
| Disable Hyper-Threading.
Possible values:
Default: Warning
If this argument is set to |
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:
This is an advanced feature. If you configure hardware tuning, the generated |
|
This captures cluster information. This argument also requires the Possible values:
Default: |
| 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] |
| The power consumption mode. Possible values:
Default: |
|
Enable per pod power management. You cannot use this argument if you configured
Possible values:
Default: |
| Name of the performance profile to create.
Default: |
| Split the reserved CPUs across NUMA nodes.
Possible values:
Default: |
| Kubelet Topology Manager policy of the performance profile to be created. Possible values:
Default: |
| Run with user level networking (DPDK) enabled.
Possible values:
Default: |
14.2.1.7. Reference performance profiles
Use the following reference performance profiles as the basis to develop your own custom profiles.
14.2.1.7.1. 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.
14.2.1.7.2. 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/$mcp: '' numa: topologyPolicy: "restricted" # To use the standard (non-realtime) kernel, set enabled to false realTimeKernel: enabled: true workloadHints: # WorkloadHints defines the set of upper level flags for different type of workloads. # See https://github.com/openshift/cluster-node-tuning-operator/blob/master/docs/performanceprofile/performance_profile.md#workloadhints # for detailed descriptions of each item. # The configuration below is set for a low latency, performance mode. realTime: true highPowerConsumption: false perPodPowerManagement: false
14.2.1.7.3. Telco core reference design performance profile
The following performance profile configures node-level performance settings for OpenShift Container Platform clusters on commodity hardware to host telco core workloads.
Telco core reference design performance profile
apiVersion: performance.openshift.io/v2 kind: PerformanceProfile metadata: # if you change this name make sure the 'include' line in TunedPerformancePatch.yaml # matches this name: include=openshift-node-performance-${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
14.2.2. 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.
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
.
14.2.3. 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 thepower-consumption-mode
flag associated with the PPC tool and the workload hint that is applied.
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.
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.
14.2.4. 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.
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 theper-pod-power-management
argument set totrue
:$ podman run --entrypoint performance-profile-creator -v \ /must-gather:/must-gather:z registry.redhat.io/openshift4/ose-cluster-node-tuning-operator:v4.16 \ --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 bedefault
orlow-latency
when theper-pod-power-management
argument is set totrue
.
Example
PerformanceProfile
withperPodPowerManagement
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 thePerformanceProfile
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 theondemand
orpowersave
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 thecpufreq
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 theAll Cores Turbo
frequency. TheAll Cores Turbo
frequency is the frequency that all cores will run at when the cores are all fully occupied.
14.2.5. 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:
Process type | Details |
---|---|
| 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 4.16 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.
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 thereserved
group are often busy. Do not run latency-sensitive applications in thereserved
group. Latency-sensitive applications run in theisolated
group.
Procedure
- Create a performance profile appropriate for the environment’s hardware and topology.
Add the
reserved
andisolated
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: ""
14.2.6. 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.
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.
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:
$ 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
Apply the isolated and reserved CPUs in the
PerformanceProfile
YAML. For example, you can set logical cores CPU0 and CPU4 asisolated
, and logical cores CPU1 to CPU3 and CPU5 to CPU7 asreserved
. 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 ...
NoteThe reserved and isolated CPU pools must not overlap and together must span all available cores in the worker node.
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.
14.2.6.1. 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:
- 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
NoteWhen you configure reserved and isolated CPUs, the infra containers in pods use the reserved CPUs and the application containers use the isolated CPUs.
14.2.7. 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.
14.2.7.1. 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
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.
14.2.7.2. 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
- Log in to the OpenShift Container Platform cluster as a user with cluster-admin privileges.
-
Set the performance profile
apiVersion
to useperformance.openshift.io/v2
. -
Remove the
globallyDisableIrqLoadBalancing
field or set it tofalse
. 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 ...
NoteWhen 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".
14.2.8. 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 omitnode
, the pages are evenly spread across all NUMA nodes.
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-###: ###
14.2.8.1. 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
14.2.9. 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.
14.2.9.1. 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
-
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:
$ oc edit -f <your_profile_name>.yaml
Populate the
spec
field with thenet
object. The object list can contain two fields:-
userLevelNetworking
is a required field specified as a boolean flag. IfuserLevelNetworking
istrue
, the queue count is set to the reserved CPU count for all supported devices. The default isfalse
. 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
.
-
Example wildcard syntax is as follows:
-
vendorID
: The network device vendor ID represented as a 16-bit hexadecimal number with a0x
prefix. deviceID
: The network device ID (model) represented as a 16-bit hexadecimal number with a0x
prefix.NoteWhen a
deviceID
is specified, thevendorID
must also be defined. A device that matches all of the device identifiers specified in a device entryinterfaceName
,vendorID
, or a pair ofvendorID
plusdeviceID
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
of0x1af4
, anddeviceID
of0x1000
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:
$ oc apply -f <your_profile_name>.yaml
Additional resources
14.2.9.2. 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:
NoteRun 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:
NoteRun 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 deviceens2
withvendorID=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 toeth0
and any devices that have avendorID=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 deviceens2
withvendorID=0x1af4
, it will also have the total net queues set to 2. Similarly, a device withinterfaceName
equal toeth0
will have total net queues set to 2.
14.2.9.3. 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
14.3. 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.
When writing your applications, follow the general recommendations described in RHEL for Real Time processes and threads.
Additional resources
14.3.1. 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.
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
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 processingapiVersion: 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:v4.16" 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 theNode
CR. - 5
runtimeClassName
must match the name of the performance profile configured in the cluster.
-
Enter the pod
runtimeClassName
in the form performance-<profile_name>, where <profile_name> is thename
from thePerformanceProfile
YAML. In the previous example, thename
isperformance-dynamic-low-latency-profile
. 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
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.
Log in to the node to verify the configuration.
$ oc debug node/<node-name>
Verify that you can use the node file system:
sh-4.4# chroot /host
Expected output
sh-4.4#
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
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
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.
Additional resources
14.3.2. 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]
Create the pod:
$ oc apply -f qos-pod.yaml --namespace=qos-example
View detailed information about the pod:
$ oc get pod qos-demo --namespace=qos-example --output=yaml
Example output
spec: containers: ... status: qosClass: Guaranteed
NoteIf 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.
14.3.3. 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> #...
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.
14.3.4. 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.
Annotation | Possible Values | Description |
---|---|---|
|
|
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 |
|
Any supported |
Sets the |
Prerequisites
- You have configured power saving in the performance profile for the node where the high priority workload pods are scheduled.
Procedure
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> #...
- Restart the pods to apply the annotation.
14.3.5. 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> #...
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.
Additional resources
14.3.6. 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> ...
Additional resources
14.4. 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.
14.4.1. 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
orfalse
). Timestamp
- The transaction timestamp.
Reason string
- The machine readable reason.
Message string
- The human readable reason describing the state and error details, if any.
14.4.1.1. 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:
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
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
The degraded state should also appear under the performance profile
status
field marked asdegraded = 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
14.4.2. 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.
14.4.2.1. 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.
14.4.2.2. 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 associatedMachineConfig
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
-
Navigate to the directory where you want to store the
must-gather
data. 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
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 themust-gather
tool.
NoteCreate 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.
- Attach the compressed file to your support case on the Red Hat Customer Portal.
14.5. 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:v4.16
.
14.5.1. Prerequisites for running latency tests
Your cluster must meet the following requirements before you can run the latency tests:
- You have configured a performance profile with the Node Tuning Operator.
- You have applied all the required CNF configurations in the cluster.
-
You have a pre-existing
MachineConfigPool
CR applied in the cluster. The default worker pool isworker-cnf
.
Additional resources
14.5.2. 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. Thecyclictest
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:
Environment variables | Description |
---|---|
| 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. |
| 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. |
| 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 |
|
Specifies the maximum acceptable hardware latency in microseconds for the workload and operating system. If you do not set the value of |
|
Specifies the maximum latency in microseconds that all threads expect before waking up during the |
|
Specifies the maximum acceptable latency in microseconds for the |
| Unified variable that specifies the maximum acceptable latency in microseconds. Applicable for all available latency tools. |
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.
14.5.3. Running the latency tests
Run the cluster latency tests to validate node tuning for your Cloud-native Network Functions (CNF) workload.
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 $(pwd)/:/kubeconfig:Z
. This allows podman
to do the proper SELinux relabeling.
Procedure
Open a shell prompt in the directory containing the
kubeconfig
file.You provide the test image with a
kubeconfig
file in current directory and its related$KUBECONFIG
environment variable, mounted through a volume. This allows the running container to use thekubeconfig
file from inside the container.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:v4.16 /usr/bin/test-run.sh \ --ginkgo.v --ginkgo.timeout="24h"
-
Optional: Append
--ginkgo.dryRun
flag to run the latency tests in dry-run mode. This is useful for checking what commands the tests run. -
Optional: Append
--ginkgo.v
flag to run the tests with increased verbosity. Optional: Append
--ginkgo.timeout="24h"
flag to ensure the Ginkgo 2.0 test suite does not timeout before the latency tests complete.ImportantThe 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.
14.5.3.1. Running hwlatdetect
The hwlatdetect
tool is available in the rt-kernel
package with a regular subscription of Red Hat Enterprise Linux (RHEL) 9.x.
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:v4.16 \ /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 thanMAXIMUM_LATENCY
(20 μs).If the results exceed the latency threshold, the test fails.
ImportantFor 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
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
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.
14.5.3.2. Running cyclictest
The cyclictest
tool measures the real-time kernel scheduler latency on the specified CPUs.
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:v4.16 \ /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 thanMAXIMUM_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.
ImportantFor 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
14.5.3.3. 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.
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:v4.16 \ /usr/bin/test-run.sh --ginkgo.focus="oslat" --ginkgo.v --ginkgo.timeout="24h"
LATENCY_TEST_CPUS
specifies the number of CPUs to test with theoslat
command.The command runs the
oslat
tool for 10 minutes (600 seconds). The test runs successfully when the maximum observed latency is lower thanMAXIMUM_LATENCY
(20 μs).If the results exceed the latency threshold, the test fails.
ImportantFor 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.
14.5.4. 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 $(pwd)/reportdest:<report_folder_path> \ -e KUBECONFIG=/kubeconfig/kubeconfig registry.redhat.io/openshift4/cnf-tests-rhel8:v4.16 \ /usr/bin/test-run.sh --report <report_folder_path> --ginkgo.v
where:
- <report_folder_path>
- Is the path to the folder where the report is generated.
14.5.5. Generating a JUnit latency test report
Use the following procedures to generate a JUnit latency test output and test failure report.
Prerequisites
-
You have installed the OpenShift CLI (
oc
). -
You have logged in as a user with
cluster-admin
privileges.
Procedure
Create a JUnit-compliant XML report by passing the
--junit
parameter together with the path to where the report is dumped:NoteYou must create the
junit
folder before running this command.$ podman run -v $(pwd)/:/kubeconfig:Z -v $(pwd)/junit:/junit \ -e KUBECONFIG=/kubeconfig/kubeconfig registry.redhat.io/openshift4/cnf-tests-rhel8:v4.16 \ /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.
14.5.6. Running latency tests on a single-node OpenShift cluster
You can run latency tests on single-node OpenShift clusters.
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:v4.16 \ /usr/bin/test-run.sh --ginkgo.v --ginkgo.timeout="24h"
NoteThe 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.
14.5.7. 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:
-
Mirroring the
cnf-tests
image to the custom disconnected registry. - 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.
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:v4.16 \ /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/
.
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:v4.16" \ -e LATENCY_TEST_RUNTIME=<time_in_seconds> \ <disconnected_registry>/cnf-tests-rhel8:v4.16 /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:v4.16 /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
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
Fetch the registry endpoint by running the following command:
$ REGISTRY=$(oc get route default-route -n openshift-image-registry --template='{{ .spec.host }}')
Create a namespace for exposing the images:
$ oc create ns cnftests
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
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 $SECRET -o jsonpath="{.data['\.dockercfg']}" | base64 --decode | jq '.["image-registry.openshift-image-registry.svc:5000"].auth')
Create a
dockerauth.json
file, for example:$ echo "{\"auths\": { \"$REGISTRY\": { \"auth\": $TOKEN } }}" > dockerauth.json
Do the image mirroring:
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \ registry.redhat.io/openshift4/cnf-tests-rhel8:4.16 \ /usr/bin/mirror -registry $REGISTRY/cnftests | oc image mirror --insecure=true \ -a=$(pwd)/dockerauth.json -f -
Run the tests:
$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \ -e LATENCY_TEST_RUNTIME=<time_in_seconds> \ -e IMAGE_REGISTRY=image-registry.openshift-image-registry.svc:5000/cnftests cnf-tests-local:latest /usr/bin/test-run.sh --ginkgo.v --ginkgo.timeout="24h"
Mirroring a different set of test images
You can optionally change the default upstream images that are mirrored for the latency tests.
Procedure
The
mirror
command tries to mirror the upstream images by default. This can be overridden by passing a file with the following format to the image:[ { "registry": "public.registry.io:5000", "image": "imageforcnftests:4.16" } ]
Pass the file to the
mirror
command, for example saving it locally asimages.json
. With the following command, the local path is mounted in/kubeconfig
inside the container and that can be passed to the mirror command.$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \ registry.redhat.io/openshift4/cnf-tests-rhel8:v4.16 /usr/bin/mirror \ --registry "my.local.registry:5000/" --images "/kubeconfig/images.json" \ | oc image mirror -f -
14.5.8. Troubleshooting errors with the cnf-tests container
To run latency tests, the cluster must be accessible from within the cnf-tests
container.
Prerequisites
-
You have installed the OpenShift CLI (
oc
). -
You have logged in as a user with
cluster-admin
privileges.
Procedure
Verify that the cluster is accessible from inside the
cnf-tests
container by running the following command:$ podman run -v $(pwd)/:/kubeconfig:Z -e KUBECONFIG=/kubeconfig/kubeconfig \ registry.redhat.io/openshift4/cnf-tests-rhel8:v4.16 \ oc get nodes
If this command does not work, an error related to spanning across DNS, MTU size, or firewall access might be occurring.