Chapter 14. Support


14.1. Support overview

You can collect data about your environment, monitor the health of your cluster and virtual machines (VMs), and troubleshoot OpenShift Virtualization resources with the following tools.

14.1.1. Web console

The OpenShift Container Platform web console displays resource usage, alerts, events, and trends for your cluster and for OpenShift Virtualization components and resources.

Table 14.1. Web console pages for monitoring and troubleshooting
PageDescription

Overview page

Cluster details, status, alerts, inventory, and resource usage

Virtualization Overview tab

OpenShift Virtualization resources, usage, alerts, and status

Virtualization Top consumers tab

Top consumers of CPU, memory, and storage

Virtualization Migrations tab

Progress of live migrations

VirtualMachines VirtualMachine VirtualMachine details Metrics tab

VM resource usage, storage, network, and migration

VirtualMachines VirtualMachine VirtualMachine details Events tab

List of VM events

VirtualMachines VirtualMachine VirtualMachine details Diagnostics tab

VM status conditions and volume snapshot status

14.1.2. Collecting data for Red Hat Support

When you submit a support case to Red Hat Support, it is helpful to provide debugging information. You can gather debugging information by performing the following steps:

Collecting data about your environment
Configure Prometheus and Alertmanager and collect must-gather data for OpenShift Container Platform and OpenShift Virtualization.
Collecting data about VMs
Collect must-gather data and memory dumps from VMs.
must-gather tool for OpenShift Virtualization
Configure and use the must-gather tool.

14.1.3. Monitoring

You can monitor the health of your cluster and VMs. For details about monitoring tools, see the Monitoring overview.

14.1.4. Troubleshooting

Troubleshoot OpenShift Virtualization components and VMs and resolve issues that trigger alerts in the web console.

Events
View important life-cycle information for VMs, namespaces, and resources.
Logs
View and configure logs for OpenShift Virtualization components and VMs.
Runbooks
Diagnose and resolve issues that trigger OpenShift Virtualization alerts in the web console.
Troubleshooting data volumes
Troubleshoot data volumes by analyzing conditions and events.

14.2. Collecting data for Red Hat Support

When you submit a support case to Red Hat Support, it is helpful to provide debugging information for OpenShift Container Platform and OpenShift Virtualization by using the following tools:

must-gather tool
The must-gather tool collects diagnostic information, including resource definitions and service logs.
Prometheus
Prometheus is a time-series database and a rule evaluation engine for metrics. Prometheus sends alerts to Alertmanager for processing.
Alertmanager
The Alertmanager service handles alerts received from Prometheus. The Alertmanager is also responsible for sending the alerts to external notification systems.

For information about the OpenShift Container Platform monitoring stack, see About OpenShift Container Platform monitoring.

14.2.1. Collecting data about your environment

Collecting data about your environment minimizes the time required to analyze and determine the root cause.

Prerequisites

14.2.2. Collecting data about virtual machines

Collecting data about malfunctioning virtual machines (VMs) minimizes the time required to analyze and determine the root cause.

Prerequisites

Procedure

  1. Collect must-gather data for the VMs using the /usr/bin/gather script.
  2. Collect screenshots of VMs that have crashed before you restart them.
  3. Collect memory dumps from VMs before remediation attempts.
  4. Record factors that the malfunctioning VMs have in common. For example, the VMs have the same host or network.

14.2.3. Using the must-gather tool for OpenShift Virtualization

You can collect data about OpenShift Virtualization resources by running the must-gather command with the OpenShift Virtualization image.

The default data collection includes information about the following resources:

  • OpenShift Virtualization Operator namespaces, including child objects
  • OpenShift Virtualization custom resource definitions
  • Namespaces that contain virtual machines
  • Basic virtual machine definitions

Procedure

  • Run the following command to collect data about OpenShift Virtualization:

    $ oc adm must-gather \
      --image=registry.redhat.io/container-native-virtualization/cnv-must-gather-rhel9:v4.13.10 \
      -- /usr/bin/gather

14.2.3.1. must-gather tool options

You can specify a combination of scripts and environment variables for the following options:

  • Collecting detailed virtual machine (VM) information from a namespace
  • Collecting detailed information about specified VMs
  • Collecting image, image-stream, and image-stream-tags information
  • Limiting the maximum number of parallel processes used by the must-gather tool
14.2.3.1.1. Parameters

Environment variables

You can specify environment variables for a compatible script.

NS=<namespace_name>
Collect virtual machine information, including virt-launcher pod details, from the namespace that you specify. The VirtualMachine and VirtualMachineInstance CR data is collected for all namespaces.
VM=<vm_name>
Collect details about a particular virtual machine. To use this option, you must also specify a namespace by using the NS environment variable.
PROS=<number_of_processes>

Modify the maximum number of parallel processes that the must-gather tool uses. The default value is 5.

Important

Using too many parallel processes can cause performance issues. Increasing the maximum number of parallel processes is not recommended.

Scripts

Each script is compatible only with certain environment variable combinations.

/usr/bin/gather
Use the default must-gather script, which collects cluster data from all namespaces and includes only basic VM information. This script is compatible only with the PROS variable.
/usr/bin/gather --vms_details
Collect VM log files, VM definitions, control-plane logs, and namespaces that belong to OpenShift Virtualization resources. Specifying namespaces includes their child objects. If you use this parameter without specifying a namespace or VM, the must-gather tool collects this data for all VMs in the cluster. This script is compatible with all environment variables, but you must specify a namespace if you use the VM variable.
/usr/bin/gather --images
Collect image, image-stream, and image-stream-tags custom resource information. This script is compatible only with the PROS variable.
14.2.3.1.2. Usage and examples

Environment variables are optional. You can run a script by itself or with one or more compatible environment variables.

Table 14.2. Compatible parameters
ScriptCompatible environment variable

/usr/bin/gather

  • PROS=<number_of_processes>

/usr/bin/gather --vms_details

  • For a namespace: NS=<namespace_name>
  • For a VM: VM=<vm_name> NS=<namespace_name>
  • PROS=<number_of_processes>

/usr/bin/gather --images

  • PROS=<number_of_processes>

Syntax

$ oc adm must-gather \
  --image=registry.redhat.io/container-native-virtualization/cnv-must-gather-rhel9:v4.13.10 \
  -- <environment_variable_1> <environment_variable_2> <script_name>

Default data collection parallel processes

By default, five processes run in parallel.

$ oc adm must-gather \
  --image=registry.redhat.io/container-native-virtualization/cnv-must-gather-rhel9:v4.13.10 \
  -- PROS=5 /usr/bin/gather 1
1
You can modify the number of parallel processes by changing the default.

Detailed VM information

The following command collects detailed VM information for the my-vm VM in the mynamespace namespace:

$ oc adm must-gather \
  --image=registry.redhat.io/container-native-virtualization/cnv-must-gather-rhel9:v4.13.10 \
  -- NS=mynamespace VM=my-vm /usr/bin/gather --vms_details 1
1
The NS environment variable is mandatory if you use the VM environment variable.

Image, image-stream, and image-stream-tags information

The following command collects image, image-stream, and image-stream-tags information from the cluster:

$ oc adm must-gather \
  --image=registry.redhat.io/container-native-virtualization/cnv-must-gather-rhel9:v4.13.10 \
  /usr/bin/gather --images

14.3. Monitoring

14.3.1. Monitoring overview

You can monitor the health of your cluster and virtual machines (VMs) with the following tools:

OpenShift Container Platform cluster checkup framework

Run automated tests on your cluster with the OpenShift Container Platform cluster checkup framework to check the following conditions:

  • Network connectivity and latency between two VMs attached to a secondary network interface
  • VM running a Data Plane Development Kit (DPDK) workload with zero packet loss
Important

The OpenShift Container Platform cluster checkup framework is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

Prometheus queries for virtual resources
Query vCPU, network, storage, and guest memory swapping usage and live migration progress.
VM custom metrics
Configure the node-exporter service to expose internal VM metrics and processes.
xref:[VM health checks]
Configure readiness, liveness, and guest agent ping probes and a watchdog for VMs.
Important

The guest agent ping probe is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

14.3.2. OpenShift Container Platform cluster checkup framework

OpenShift Virtualization includes predefined checkups that can be used for cluster maintenance and troubleshooting.

Important

The OpenShift Container Platform cluster checkup framework is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

14.3.2.1. About the OpenShift Container Platform cluster checkup framework

A checkup is an automated test workload that allows you to verify if a specific cluster functionality works as expected. The cluster checkup framework uses native Kubernetes resources to configure and execute the checkup.

By using predefined checkups, cluster administrators and developers can improve cluster maintainability, troubleshoot unexpected behavior, minimize errors, and save time. They can also review the results of the checkup and share them with experts for further analysis. Vendors can write and publish checkups for features or services that they provide and verify that their customer environments are configured correctly.

Running a predefined checkup in an existing namespace involves setting up a service account for the checkup, creating the Role and RoleBinding objects for the service account, enabling permissions for the checkup, and creating the input config map and the checkup job. You can run a checkup multiple times.

Important

You must always:

  • Verify that the checkup image is from a trustworthy source before applying it.
  • Review the checkup permissions before creating the Role and RoleBinding objects.

14.3.2.2. Virtual machine latency checkup

You use a predefined checkup to verify network connectivity and measure latency between two virtual machines (VMs) that are attached to a secondary network interface. The latency checkup uses the ping utility.

You run a latency checkup by performing the following steps:

  1. Create a service account, roles, and rolebindings to provide cluster access permissions to the latency checkup.
  2. Create a config map to provide the input to run the checkup and to store the results.
  3. Create a job to run the checkup.
  4. Review the results in the config map.
  5. Optional: To rerun the checkup, delete the existing config map and job and then create a new config map and job.
  6. When you are finished, delete the latency checkup resources.

Prerequisites

  • You installed the OpenShift CLI (oc).
  • The cluster has at least two worker nodes.
  • The Multus Container Network Interface (CNI) plugin is installed on the cluster.
  • You configured a network attachment definition for a namespace.

Procedure

  1. Create a ServiceAccount, Role, and RoleBinding manifest for the latency checkup:

    Example 14.1. Example role manifest file

    ---
    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: vm-latency-checkup-sa
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: Role
    metadata:
      name: kubevirt-vm-latency-checker
    rules:
    - apiGroups: ["kubevirt.io"]
      resources: ["virtualmachineinstances"]
      verbs: ["get", "create", "delete"]
    - apiGroups: ["subresources.kubevirt.io"]
      resources: ["virtualmachineinstances/console"]
      verbs: ["get"]
    - apiGroups: ["k8s.cni.cncf.io"]
      resources: ["network-attachment-definitions"]
      verbs: ["get"]
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: RoleBinding
    metadata:
      name: kubevirt-vm-latency-checker
    subjects:
    - kind: ServiceAccount
      name: vm-latency-checkup-sa
    roleRef:
      kind: Role
      name: kubevirt-vm-latency-checker
      apiGroup: rbac.authorization.k8s.io
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: Role
    metadata:
      name: kiagnose-configmap-access
    rules:
    - apiGroups: [ "" ]
      resources: [ "configmaps" ]
      verbs: ["get", "update"]
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: RoleBinding
    metadata:
      name: kiagnose-configmap-access
    subjects:
    - kind: ServiceAccount
      name: vm-latency-checkup-sa
    roleRef:
      kind: Role
      name: kiagnose-configmap-access
      apiGroup: rbac.authorization.k8s.io
  2. Apply the ServiceAccount, Role, and RoleBinding manifest:

    $ oc apply -n <target_namespace> -f <latency_sa_roles_rolebinding>.yaml 1
    1
    <target_namespace> is the namespace where the checkup is to be run. This must be an existing namespace where the NetworkAttachmentDefinition object resides.
  3. Create a ConfigMap manifest that contains the input parameters for the checkup:

    Example input config map

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: kubevirt-vm-latency-checkup-config
    data:
      spec.timeout: 5m
      spec.param.networkAttachmentDefinitionNamespace: <target_namespace>
      spec.param.networkAttachmentDefinitionName: "blue-network" 1
      spec.param.maxDesiredLatencyMilliseconds: "10" 2
      spec.param.sampleDurationSeconds: "5" 3
      spec.param.sourceNode: "worker1" 4
      spec.param.targetNode: "worker2" 5

    1
    The name of the NetworkAttachmentDefinition object.
    2
    Optional: The maximum desired latency, in milliseconds, between the virtual machines. If the measured latency exceeds this value, the checkup fails.
    3
    Optional: The duration of the latency check, in seconds.
    4
    Optional: When specified, latency is measured from this node to the target node. If the source node is specified, the spec.param.targetNode field cannot be empty.
    5
    Optional: When specified, latency is measured from the source node to this node.
  4. Apply the config map manifest in the target namespace:

    $ oc apply -n <target_namespace> -f <latency_config_map>.yaml
  5. Create a Job manifest to run the checkup:

    Example job manifest

    apiVersion: batch/v1
    kind: Job
    metadata:
      name: kubevirt-vm-latency-checkup
    spec:
      backoffLimit: 0
      template:
        spec:
          serviceAccountName: vm-latency-checkup-sa
          restartPolicy: Never
          containers:
            - name: vm-latency-checkup
              image: registry.redhat.io/container-native-virtualization/vm-network-latency-checkup-rhel9:v4.13.0
              securityContext:
                allowPrivilegeEscalation: false
                capabilities:
                  drop: ["ALL"]
                runAsNonRoot: true
                seccompProfile:
                  type: "RuntimeDefault"
              env:
                - name: CONFIGMAP_NAMESPACE
                  value: <target_namespace>
                - name: CONFIGMAP_NAME
                  value: kubevirt-vm-latency-checkup-config
                - name: POD_UID
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.uid

  6. Apply the Job manifest:

    $ oc apply -n <target_namespace> -f <latency_job>.yaml
  7. Wait for the job to complete:

    $ oc wait job kubevirt-vm-latency-checkup -n <target_namespace> --for condition=complete --timeout 6m
  8. Review the results of the latency checkup by running the following command. If the maximum measured latency is greater than the value of the spec.param.maxDesiredLatencyMilliseconds attribute, the checkup fails and returns an error.

    $ oc get configmap kubevirt-vm-latency-checkup-config -n <target_namespace> -o yaml

    Example output config map (success)

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: kubevirt-vm-latency-checkup-config
      namespace: <target_namespace>
    data:
      spec.timeout: 5m
      spec.param.networkAttachmentDefinitionNamespace: <target_namespace>
      spec.param.networkAttachmentDefinitionName: "blue-network"
      spec.param.maxDesiredLatencyMilliseconds: "10"
      spec.param.sampleDurationSeconds: "5"
      spec.param.sourceNode: "worker1"
      spec.param.targetNode: "worker2"
      status.succeeded: "true"
      status.failureReason: ""
      status.completionTimestamp: "2022-01-01T09:00:00Z"
      status.startTimestamp: "2022-01-01T09:00:07Z"
      status.result.avgLatencyNanoSec: "177000"
      status.result.maxLatencyNanoSec: "244000" 1
      status.result.measurementDurationSec: "5"
      status.result.minLatencyNanoSec: "135000"
      status.result.sourceNode: "worker1"
      status.result.targetNode: "worker2"

    1
    The maximum measured latency in nanoseconds.
  9. Optional: To view the detailed job log in case of checkup failure, use the following command:

    $ oc logs job.batch/kubevirt-vm-latency-checkup -n <target_namespace>
  10. Delete the job and config map that you previously created by running the following commands:

    $ oc delete job -n <target_namespace> kubevirt-vm-latency-checkup
    $ oc delete config-map -n <target_namespace> kubevirt-vm-latency-checkup-config
  11. Optional: If you do not plan to run another checkup, delete the roles manifest:

    $ oc delete -f <latency_sa_roles_rolebinding>.yaml

14.3.2.3. DPDK checkup

Use a predefined checkup to verify that your OpenShift Container Platform cluster node can run a virtual machine (VM) with a Data Plane Development Kit (DPDK) workload with zero packet loss. The DPDK checkup runs traffic between a traffic generator pod and a VM running a test DPDK application.

You run a DPDK checkup by performing the following steps:

  1. Create a service account, role, and role bindings for the DPDK checkup and a service account for the traffic generator pod.
  2. Create a security context constraints resource for the traffic generator pod.
  3. Create a config map to provide the input to run the checkup and to store the results.
  4. Create a job to run the checkup.
  5. Review the results in the config map.
  6. Optional: To rerun the checkup, delete the existing config map and job and then create a new config map and job.
  7. When you are finished, delete the DPDK checkup resources.

Prerequisites

  • You have access to the cluster as a user with cluster-admin permissions.
  • You have installed the OpenShift CLI (oc).
  • You have configured the compute nodes to run DPDK applications on VMs with zero packet loss.
Important

The traffic generator pod created by the checkup has elevated privileges:

  • It runs as root.
  • It has a bind mount to the node’s file system.

The container image of the traffic generator is pulled from the upstream Project Quay container registry.

Procedure

  1. Create a ServiceAccount, Role, and RoleBinding manifest for the DPDK checkup and the traffic generator pod:

    Example 14.2. Example service account, role, and rolebinding manifest file

    ---
    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: dpdk-checkup-sa
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: Role
    metadata:
      name: kiagnose-configmap-access
    rules:
      - apiGroups: [ "" ]
        resources: [ "configmaps" ]
        verbs: [ "get", "update" ]
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: RoleBinding
    metadata:
      name: kiagnose-configmap-access
    subjects:
      - kind: ServiceAccount
        name: dpdk-checkup-sa
    roleRef:
      apiGroup: rbac.authorization.k8s.io
      kind: Role
      name: kiagnose-configmap-access
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: Role
    metadata:
      name: kubevirt-dpdk-checker
    rules:
      - apiGroups: [ "kubevirt.io" ]
        resources: [ "virtualmachineinstances" ]
        verbs: [ "create", "get", "delete" ]
      - apiGroups: [ "subresources.kubevirt.io" ]
        resources: [ "virtualmachineinstances/console" ]
        verbs: [ "get" ]
      - apiGroups: [ "" ]
        resources: [ "pods" ]
        verbs: [ "create", "get", "delete" ]
      - apiGroups: [ "" ]
        resources: [ "pods/exec" ]
        verbs: [ "create" ]
      - apiGroups: [ "k8s.cni.cncf.io" ]
        resources: [ "network-attachment-definitions" ]
        verbs: [ "get" ]
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: RoleBinding
    metadata:
      name: kubevirt-dpdk-checker
    subjects:
      - kind: ServiceAccount
        name: dpdk-checkup-sa
    roleRef:
      apiGroup: rbac.authorization.k8s.io
      kind: Role
      name: kubevirt-dpdk-checker
    ---
    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: dpdk-checkup-traffic-gen-sa
  2. Apply the ServiceAccount, Role, and RoleBinding manifest:

    $ oc apply -n <target_namespace> -f <dpdk_sa_roles_rolebinding>.yaml
  3. Create a SecurityContextConstraints manifest for the traffic generator pod:

    Example security context constraints manifest

    apiVersion: security.openshift.io/v1
    kind: SecurityContextConstraints
    metadata:
      name: dpdk-checkup-traffic-gen
    allowHostDirVolumePlugin: true
    allowHostIPC: false
    allowHostNetwork: false
    allowHostPID: false
    allowHostPorts: false
    allowPrivilegeEscalation: false
    allowPrivilegedContainer: false
    allowedCapabilities:
    - IPC_LOCK
    - NET_ADMIN
    - NET_RAW
    - SYS_RESOURCE
    defaultAddCapabilities: null
    fsGroup:
      type: RunAsAny
    groups: []
    readOnlyRootFilesystem: false
    requiredDropCapabilities: null
    runAsUser:
      type: RunAsAny
    seLinuxContext:
      type: RunAsAny
    seccompProfiles:
    - runtime/default
    - unconfined
    supplementalGroups:
      type: RunAsAny
    users:
    - system:serviceaccount:dpdk-checkup-ns:dpdk-checkup-traffic-gen-sa

  4. Apply the SecurityContextConstraints manifest:

    $ oc apply -f <dpdk_scc>.yaml
  5. Create a ConfigMap manifest that contains the input parameters for the checkup:

    Example input config map

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: dpdk-checkup-config
    data:
      spec.timeout: 10m
      spec.param.networkAttachmentDefinitionName: <network_name> 1
      spec.param.trafficGeneratorRuntimeClassName: <runtimeclass_name> 2
      spec.param.trafficGeneratorImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-traffic-gen:v0.1.1" 3
      spec.param.vmContainerDiskImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-vm:v0.1.1" 4

    1
    The name of the NetworkAttachmentDefinition object.
    2
    The RuntimeClass resource that the traffic generator pod uses.
    3
    The container image for the traffic generator. In this example, the image is pulled from the upstream Project Quay Container Registry.
    4
    The container disk image for the VM. In this example, the image is pulled from the upstream Project Quay Container Registry.
  6. Apply the ConfigMap manifest in the target namespace:

    $ oc apply -n <target_namespace> -f <dpdk_config_map>.yaml
  7. Create a Job manifest to run the checkup:

    Example job manifest

    apiVersion: batch/v1
    kind: Job
    metadata:
      name: dpdk-checkup
    spec:
      backoffLimit: 0
      template:
        spec:
          serviceAccountName: dpdk-checkup-sa
          restartPolicy: Never
          containers:
            - name: dpdk-checkup
              image: registry.redhat.io/container-native-virtualization/kubevirt-dpdk-checkup-rhel9:v4.13.0
              imagePullPolicy: Always
              securityContext:
                allowPrivilegeEscalation: false
                capabilities:
                  drop: ["ALL"]
                runAsNonRoot: true
                seccompProfile:
                  type: "RuntimeDefault"
              env:
                - name: CONFIGMAP_NAMESPACE
                  value: <target-namespace>
                - name: CONFIGMAP_NAME
                  value: dpdk-checkup-config
                - name: POD_UID
                  valueFrom:
                    fieldRef:
                      fieldPath: metadata.uid

  8. Apply the Job manifest:

    $ oc apply -n <target_namespace> -f <dpdk_job>.yaml
  9. Wait for the job to complete:

    $ oc wait job dpdk-checkup -n <target_namespace> --for condition=complete --timeout 10m
  10. Review the results of the checkup by running the following command:

    $ oc get configmap dpdk-checkup-config -n <target_namespace> -o yaml

    Example output config map (success)

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: dpdk-checkup-config
    data:
      spec.timeout: 1h2m
      spec.param.NetworkAttachmentDefinitionName: "mlx-dpdk-network-1"
      spec.param.trafficGeneratorRuntimeClassName: performance-performance-zeus10
      spec.param.trafficGeneratorImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-traffic-gen:v0.1.1"
      spec.param.vmContainerDiskImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-vm:v0.1.1"
      status.succeeded: true
      status.failureReason: " "
      status.startTimestamp: 2022-12-21T09:33:06+00:00
      status.completionTimestamp: 2022-12-21T11:33:06+00:00
      status.result.actualTrafficGeneratorTargetNode: worker-dpdk1
      status.result.actualDPDKVMTargetNode: worker-dpdk2
      status.result.dropRate: 0

  11. Delete the job and config map that you previously created by running the following commands:

    $ oc delete job -n <target_namespace> dpdk-checkup
    $ oc delete config-map -n <target_namespace> dpdk-checkup-config
  12. Optional: If you do not plan to run another checkup, delete the ServiceAccount, Role, and RoleBinding manifest:

    $ oc delete -f <dpdk_sa_roles_rolebinding>.yaml
14.3.2.3.1. DPDK checkup config map parameters

The following table shows the mandatory and optional parameters that you can set in the data stanza of the input ConfigMap manifest when you run a cluster DPDK readiness checkup:

Table 14.3. DPDK checkup config map parameters
ParameterDescriptionIs Mandatory

spec.timeout

The time, in minutes, before the checkup fails.

True

spec.param.networkAttachmentDefinitionName

The name of the NetworkAttachmentDefinition object of the SR-IOV NICs connected.

True

spec.param.trafficGeneratorRuntimeClassName

The RuntimeClass resource that the traffic generator pod uses.

True

spec.param.trafficGeneratorImage

The container image for the traffic generator. The default value is quay.io/kiagnose/kubevirt-dpdk-checkup-traffic-gen:main.

False

spec.param.trafficGeneratorNodeSelector

The node on which the traffic generator pod is to be scheduled. The node should be configured to allow DPDK traffic.

False

spec.param.trafficGeneratorPacketsPerSecond

The number of packets per second, in kilo (k) or million(m). The default value is 14m.

False

spec.param.trafficGeneratorEastMacAddress

The MAC address of the NIC connected to the traffic generator pod or VM. The default value is a random MAC address in the format 50:xx:xx:xx:xx:01.

False

spec.param.trafficGeneratorWestMacAddress

The MAC address of the NIC connected to the traffic generator pod or VM. The default value is a random MAC address in the format 50:xx:xx:xx:xx:02.

False

spec.param.vmContainerDiskImage

The container disk image for the VM. The default value is quay.io/kiagnose/kubevirt-dpdk-checkup-vm:main.

False

spec.param.DPDKLabelSelector

The label of the node on which the VM runs. The node should be configured to allow DPDK traffic.

False

spec.param.DPDKEastMacAddress

The MAC address of the NIC that is connected to the VM. The default value is a random MAC address in the format 60:xx:xx:xx:xx:01.

False

spec.param.DPDKWestMacAddress

The MAC address of the NIC that is connected to the VM. The default value is a random MAC address in the format 60:xx:xx:xx:xx:02.

False

spec.param.testDuration

The duration, in minutes, for which the traffic generator runs. The default value is 5 minutes.

False

spec.param.portBandwidthGB

The maximum bandwidth of the SR-IOV NIC. The default value is 10GB.

False

spec.param.verbose

When set to true, it increases the verbosity of the checkup log. The default value is false.

False

14.3.2.3.2. Building a container disk image for RHEL virtual machines

You can build a custom Red Hat Enterprise Linux (RHEL) 8 OS image in qcow2 format and use it to create a container disk image. You can store the container disk image in a registry that is accessible from your cluster and specify the image location in the spec.param.vmContainerDiskImage attribute of the DPDK checkup config map.

To build a container disk image, you must create an image builder virtual machine (VM). The image builder VM is a RHEL 8 VM that can be used to build custom RHEL images.

Prerequisites

  • The image builder VM must run RHEL 8.7 and must have a minimum of 2 CPU cores, 4 GiB RAM, and 20 GB of free space in the /var directory.
  • You have installed the image builder tool and its CLI (composer-cli) on the VM.
  • You have installed the virt-customize tool:

    # dnf install libguestfs-tools
  • You have installed the Podman CLI tool (podman).

Procedure

  1. Verify that you can build a RHEL 8.7 image:

    # composer-cli distros list
    Note

    To run the composer-cli commands as non-root, add your user to the weldr or root groups:

    # usermod -a -G weldr user
    $ newgrp weldr
  2. Enter the following command to create an image blueprint file in TOML format that contains the packages to be installed, kernel customizations, and the services to be disabled during boot time:

    $ cat << EOF > dpdk-vm.toml
    name = "dpdk_image"
    description = "Image to use with the DPDK checkup"
    version = "0.0.1"
    distro = "rhel-87"
    
    [[packages]]
    name = "dpdk"
    
    [[packages]]
    name = "dpdk-tools"
    
    [[packages]]
    name = "driverctl"
    
    [[packages]]
    name = "tuned-profiles-cpu-partitioning"
    
    [customizations.kernel]
    append = "default_hugepagesz=1GB hugepagesz=1G hugepages=8 isolcpus=2-7"
    
    [customizations.services]
    disabled = ["NetworkManager-wait-online", "sshd"]
    EOF
  3. Push the blueprint file to the image builder tool by running the following command:

    # composer-cli blueprints push dpdk-vm.toml
  4. Generate the system image by specifying the blueprint name and output file format. The Universally Unique Identifier (UUID) of the image is displayed when you start the compose process.

    # composer-cli compose start dpdk_image qcow2
  5. Wait for the compose process to complete. The compose status must show FINISHED before you can continue to the next step.

    # composer-cli compose status
  6. Enter the following command to download the qcow2 image file by specifying its UUID:

    # composer-cli compose image <UUID>
  7. Create the customization scripts by running the following commands:

    $ cat <<EOF >customize-vm
    echo  isolated_cores=2-7 > /etc/tuned/cpu-partitioning-variables.conf
    tuned-adm profile cpu-partitioning
    echo "options vfio enable_unsafe_noiommu_mode=1" > /etc/modprobe.d/vfio-noiommu.conf
    EOF
    $ cat <<EOF >first-boot
    driverctl set-override 0000:06:00.0 vfio-pci
    driverctl set-override 0000:07:00.0 vfio-pci
    
    mkdir /mnt/huge
    mount /mnt/huge --source nodev -t hugetlbfs -o pagesize=1GB
    EOF
  8. Use the virt-customize tool to customize the image generated by the image builder tool:

    $ virt-customize -a <UUID>.qcow2 --run=customize-vm --firstboot=first-boot --selinux-relabel
  9. To create a Dockerfile that contains all the commands to build the container disk image, enter the following command:

    $ cat << EOF > Dockerfile
    FROM scratch
    COPY <uuid>-disk.qcow2 /disk/
    EOF

    where:

    <uuid>-disk.qcow2
    Specifies the name of the custom image in qcow2 format.
  10. Build and tag the container by running the following command:

    $ podman build . -t dpdk-rhel:latest
  11. Push the container disk image to a registry that is accessible from your cluster by running the following command:

    $ podman push dpdk-rhel:latest
  12. Provide a link to the container disk image in the spec.param.vmContainerDiskImage attribute in the DPDK checkup config map.

14.3.2.4. Additional resources

14.3.3. Prometheus queries for virtual resources

OpenShift Virtualization provides metrics that you can use to monitor the consumption of cluster infrastructure resources, including vCPU, network, storage, and guest memory swapping. You can also use metrics to query live migration status.

Use the OpenShift Container Platform monitoring dashboard to query virtualization metrics.

14.3.3.1. Prerequisites

  • To use the vCPU metric, the schedstats=enable kernel argument must be applied to the MachineConfig object. This kernel argument enables scheduler statistics used for debugging and performance tuning and adds a minor additional load to the scheduler. For more information, see Adding kernel arguments to nodes.
  • For guest memory swapping queries to return data, memory swapping must be enabled on the virtual guests.

14.3.3.2. Querying metrics

The OpenShift Container Platform monitoring dashboard enables you to run Prometheus Query Language (PromQL) queries to examine metrics visualized on a plot. This functionality provides information about the state of a cluster and any user-defined workloads that you are monitoring.

As a cluster administrator, you can query metrics for all core OpenShift Container Platform and user-defined projects.

As a developer, you must specify a project name when querying metrics. You must have the required privileges to view metrics for the selected project.

14.3.3.2.1. Querying metrics for all projects as a cluster administrator

As a cluster administrator or as a user with view permissions for all projects, you can access metrics for all default OpenShift Container Platform and user-defined projects in the Metrics UI.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin cluster role or with view permissions for all projects.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. From the Administrator perspective in the OpenShift Container Platform web console, select Observe Metrics.
  2. To add one or more queries, do any of the following:

    OptionDescription

    Create a custom query.

    Add your Prometheus Query Language (PromQL) query to the Expression field.

    As you type a PromQL expression, autocomplete suggestions appear in a drop-down list. These suggestions include functions, metrics, labels, and time tokens. You can use the keyboard arrows to select one of these suggested items and then press Enter to add the item to your expression. You can also move your mouse pointer over a suggested item to view a brief description of that item.

    Add multiple queries.

    Select Add query.

    Duplicate an existing query.

    Select the Options menu kebab next to the query, then choose Duplicate query.

    Disable a query from being run.

    Select the Options menu kebab next to the query and choose Disable query.

  3. To run queries that you created, select Run queries. The metrics from the queries are visualized on the plot. If a query is invalid, the UI shows an error message.

    Note

    Queries that operate on large amounts of data might time out or overload the browser when drawing time series graphs. To avoid this, select Hide graph and calibrate your query using only the metrics table. Then, after finding a feasible query, enable the plot to draw the graphs.

    Note

    By default, the query table shows an expanded view that lists every metric and its current value. You can select ˅ to minimize the expanded view for a query.

  4. Optional: The page URL now contains the queries you ran. To use this set of queries again in the future, save this URL.
  5. Explore the visualized metrics. Initially, all metrics from all enabled queries are shown on the plot. You can select which metrics are shown by doing any of the following:

    OptionDescription

    Hide all metrics from a query.

    Click the Options menu kebab for the query and click Hide all series.

    Hide a specific metric.

    Go to the query table and click the colored square near the metric name.

    Zoom into the plot and change the time range.

    Either:

    • Visually select the time range by clicking and dragging on the plot horizontally.
    • Use the menu in the left upper corner to select the time range.

    Reset the time range.

    Select Reset zoom.

    Display outputs for all queries at a specific point in time.

    Hold the mouse cursor on the plot at that point. The query outputs will appear in a pop-up box.

    Hide the plot.

    Select Hide graph.

14.3.3.2.2. Querying metrics for user-defined projects as a developer

You can access metrics for a user-defined project as a developer or as a user with view permissions for the project.

In the Developer perspective, the Metrics UI includes some predefined CPU, memory, bandwidth, and network packet queries for the selected project. You can also run custom Prometheus Query Language (PromQL) queries for CPU, memory, bandwidth, network packet and application metrics for the project.

Note

Developers can only use the Developer perspective and not the Administrator perspective. As a developer, you can only query metrics for one project at a time.

Prerequisites

  • You have access to the cluster as a developer or as a user with view permissions for the project that you are viewing metrics for.
  • You have enabled monitoring for user-defined projects.
  • You have deployed a service in a user-defined project.
  • You have created a ServiceMonitor custom resource definition (CRD) for the service to define how the service is monitored.

Procedure

  1. From the Developer perspective in the OpenShift Container Platform web console, select Observe Metrics.
  2. Select the project that you want to view metrics for in the Project: list.
  3. Select a query from the Select query list, or create a custom PromQL query based on the selected query by selecting Show PromQL. The metrics from the queries are visualized on the plot.

    Note

    In the Developer perspective, you can only run one query at a time.

  4. Explore the visualized metrics by doing any of the following:

    OptionDescription

    Zoom into the plot and change the time range.

    Either:

    • Visually select the time range by clicking and dragging on the plot horizontally.
    • Use the menu in the left upper corner to select the time range.

    Reset the time range.

    Select Reset zoom.

    Display outputs for all queries at a specific point in time.

    Hold the mouse cursor on the plot at that point. The query outputs appear in a pop-up box.

14.3.3.3. Virtualization metrics

The following metric descriptions include example Prometheus Query Language (PromQL) queries. These metrics are not an API and might change between versions.

Note

The following examples use topk queries that specify a time period. If virtual machines are deleted during that time period, they can still appear in the query output.

14.3.3.3.1. vCPU metrics

The following query can identify virtual machines that are waiting for Input/Output (I/O):

kubevirt_vmi_vcpu_wait_seconds
Returns the wait time (in seconds) for a virtual machine’s vCPU. Type: Counter.

A value above '0' means that the vCPU wants to run, but the host scheduler cannot run it yet. This inability to run indicates that there is an issue with I/O.

Note

To query the vCPU metric, the schedstats=enable kernel argument must first be applied to the MachineConfig object. This kernel argument enables scheduler statistics used for debugging and performance tuning and adds a minor additional load to the scheduler.

Example vCPU wait time query

topk(3, sum by (name, namespace) (rate(kubevirt_vmi_vcpu_wait_seconds[6m]))) > 0 1

1
This query returns the top 3 VMs waiting for I/O at every given moment over a six-minute time period.
14.3.3.3.2. Network metrics

The following queries can identify virtual machines that are saturating the network:

kubevirt_vmi_network_receive_bytes_total
Returns the total amount of traffic received (in bytes) on the virtual machine’s network. Type: Counter.
kubevirt_vmi_network_transmit_bytes_total
Returns the total amount of traffic transmitted (in bytes) on the virtual machine’s network. Type: Counter.

Example network traffic query

topk(3, sum by (name, namespace) (rate(kubevirt_vmi_network_receive_bytes_total[6m])) + sum by (name, namespace) (rate(kubevirt_vmi_network_transmit_bytes_total[6m]))) > 0 1

1
This query returns the top 3 VMs transmitting the most network traffic at every given moment over a six-minute time period.
14.3.3.3.3. Storage metrics
14.3.3.3.3.1. Storage-related traffic

The following queries can identify VMs that are writing large amounts of data:

kubevirt_vmi_storage_read_traffic_bytes_total
Returns the total amount (in bytes) of the virtual machine’s storage-related traffic. Type: Counter.
kubevirt_vmi_storage_write_traffic_bytes_total
Returns the total amount of storage writes (in bytes) of the virtual machine’s storage-related traffic. Type: Counter.

Example storage-related traffic query

topk(3, sum by (name, namespace) (rate(kubevirt_vmi_storage_read_traffic_bytes_total[6m])) + sum by (name, namespace) (rate(kubevirt_vmi_storage_write_traffic_bytes_total[6m]))) > 0 1

1
This query returns the top 3 VMs performing the most storage traffic at every given moment over a six-minute time period.
14.3.3.3.3.2. Storage snapshot data
kubevirt_vmsnapshot_disks_restored_from_source_total
Returns the total number of virtual machine disks restored from the source virtual machine. Type: Gauge.
kubevirt_vmsnapshot_disks_restored_from_source_bytes
Returns the amount of space in bytes restored from the source virtual machine. Type: Gauge.

Examples of storage snapshot data queries

kubevirt_vmsnapshot_disks_restored_from_source_total{vm_name="simple-vm", vm_namespace="default"} 1

1
This query returns the total number of virtual machine disks restored from the source virtual machine.
kubevirt_vmsnapshot_disks_restored_from_source_bytes{vm_name="simple-vm", vm_namespace="default"} 1
1
This query returns the amount of space in bytes restored from the source virtual machine.
14.3.3.3.3.3. I/O performance

The following queries can determine the I/O performance of storage devices:

kubevirt_vmi_storage_iops_read_total
Returns the amount of write I/O operations the virtual machine is performing per second. Type: Counter.
kubevirt_vmi_storage_iops_write_total
Returns the amount of read I/O operations the virtual machine is performing per second. Type: Counter.

Example I/O performance query

topk(3, sum by (name, namespace) (rate(kubevirt_vmi_storage_iops_read_total[6m])) + sum by (name, namespace) (rate(kubevirt_vmi_storage_iops_write_total[6m]))) > 0 1

1
This query returns the top 3 VMs performing the most I/O operations per second at every given moment over a six-minute time period.
14.3.3.3.4. Guest memory swapping metrics

The following queries can identify which swap-enabled guests are performing the most memory swapping:

kubevirt_vmi_memory_swap_in_traffic_bytes_total
Returns the total amount (in bytes) of memory the virtual guest is swapping in. Type: Gauge.
kubevirt_vmi_memory_swap_out_traffic_bytes_total
Returns the total amount (in bytes) of memory the virtual guest is swapping out. Type: Gauge.

Example memory swapping query

topk(3, sum by (name, namespace) (rate(kubevirt_vmi_memory_swap_in_traffic_bytes_total[6m])) + sum by (name, namespace) (rate(kubevirt_vmi_memory_swap_out_traffic_bytes_total[6m]))) > 0 1

1
This query returns the top 3 VMs where the guest is performing the most memory swapping at every given moment over a six-minute time period.
Note

Memory swapping indicates that the virtual machine is under memory pressure. Increasing the memory allocation of the virtual machine can mitigate this issue.

14.3.3.3.5. Live migration metrics

The following metrics can be queried to show live migration status:

kubevirt_migrate_vmi_data_processed_bytes
The amount of guest operating system data that has migrated to the new virtual machine (VM). Type: Gauge.
kubevirt_migrate_vmi_data_remaining_bytes
The amount of guest operating system data that remains to be migrated. Type: Gauge.
kubevirt_migrate_vmi_dirty_memory_rate_bytes
The rate at which memory is becoming dirty in the guest operating system. Dirty memory is data that has been changed but not yet written to disk. Type: Gauge.
kubevirt_migrate_vmi_pending_count
The number of pending migrations. Type: Gauge.
kubevirt_migrate_vmi_scheduling_count
The number of scheduling migrations. Type: Gauge.
kubevirt_migrate_vmi_running_count
The number of running migrations. Type: Gauge.
kubevirt_migrate_vmi_succeeded
The number of successfully completed migrations. Type: Gauge.
kubevirt_migrate_vmi_failed
The number of failed migrations. Type: Gauge.

14.3.3.4. Additional resources

14.3.4. Exposing custom metrics for virtual machines

OpenShift Container Platform includes a preconfigured, preinstalled, and self-updating monitoring stack that provides monitoring for core platform components. This monitoring stack is based on the Prometheus monitoring system. Prometheus is a time-series database and a rule evaluation engine for metrics.

In addition to using the OpenShift Container Platform monitoring stack, you can enable monitoring for user-defined projects by using the CLI and query custom metrics that are exposed for virtual machines through the node-exporter service.

14.3.4.1. Configuring the node exporter service

The node-exporter agent is deployed on every virtual machine in the cluster from which you want to collect metrics. Configure the node-exporter agent as a service to expose internal metrics and processes that are associated with virtual machines.

Prerequisites

  • Install the OpenShift Container Platform CLI oc.
  • Log in to the cluster as a user with cluster-admin privileges.
  • Create the cluster-monitoring-config ConfigMap object in the openshift-monitoring project.
  • Configure the user-workload-monitoring-config ConfigMap object in the openshift-user-workload-monitoring project by setting enableUserWorkload to true.

Procedure

  1. Create the Service YAML file. In the following example, the file is called node-exporter-service.yaml.

    kind: Service
    apiVersion: v1
    metadata:
      name: node-exporter-service 1
      namespace: dynamation 2
      labels:
        servicetype: metrics 3
    spec:
      ports:
        - name: exmet 4
          protocol: TCP
          port: 9100 5
          targetPort: 9100 6
      type: ClusterIP
      selector:
        monitor: metrics 7
    1
    The node-exporter service that exposes the metrics from the virtual machines.
    2
    The namespace where the service is created.
    3
    The label for the service. The ServiceMonitor uses this label to match this service.
    4
    The name given to the port that exposes metrics on port 9100 for the ClusterIP service.
    5
    The target port used by node-exporter-service to listen for requests.
    6
    The TCP port number of the virtual machine that is configured with the monitor label.
    7
    The label used to match the virtual machine’s pods. In this example, any virtual machine’s pod with the label monitor and a value of metrics will be matched.
  2. Create the node-exporter service:

    $ oc create -f node-exporter-service.yaml

14.3.4.2. Configuring a virtual machine with the node exporter service

Download the node-exporter file on to the virtual machine. Then, create a systemd service that runs the node-exporter service when the virtual machine boots.

Prerequisites

  • The pods for the component are running in the openshift-user-workload-monitoring project.
  • Grant the monitoring-edit role to users who need to monitor this user-defined project.

Procedure

  1. Log on to the virtual machine.
  2. Download the node-exporter file on to the virtual machine by using the directory path that applies to the version of node-exporter file.

    $ wget https://github.com/prometheus/node_exporter/releases/download/v1.3.1/node_exporter-1.3.1.linux-amd64.tar.gz
  3. Extract the executable and place it in the /usr/bin directory.

    $ sudo tar xvf node_exporter-1.3.1.linux-amd64.tar.gz \
        --directory /usr/bin --strip 1 "*/node_exporter"
  4. Create a node_exporter.service file in this directory path: /etc/systemd/system. This systemd service file runs the node-exporter service when the virtual machine reboots.

    [Unit]
    Description=Prometheus Metrics Exporter
    After=network.target
    StartLimitIntervalSec=0
    
    [Service]
    Type=simple
    Restart=always
    RestartSec=1
    User=root
    ExecStart=/usr/bin/node_exporter
    
    [Install]
    WantedBy=multi-user.target
  5. Enable and start the systemd service.

    $ sudo systemctl enable node_exporter.service
    $ sudo systemctl start node_exporter.service

Verification

  • Verify that the node-exporter agent is reporting metrics from the virtual machine.

    $ curl http://localhost:9100/metrics

    Example output

    go_gc_duration_seconds{quantile="0"} 1.5244e-05
    go_gc_duration_seconds{quantile="0.25"} 3.0449e-05
    go_gc_duration_seconds{quantile="0.5"} 3.7913e-05

14.3.4.3. Creating a custom monitoring label for virtual machines

To enable queries to multiple virtual machines from a single service, add a custom label in the virtual machine’s YAML file.

Prerequisites

  • Install the OpenShift Container Platform CLI oc.
  • Log in as a user with cluster-admin privileges.
  • Access to the web console for stop and restart a virtual machine.

Procedure

  1. Edit the template spec of your virtual machine configuration file. In this example, the label monitor has the value metrics.

    spec:
      template:
        metadata:
          labels:
            monitor: metrics
  2. Stop and restart the virtual machine to create a new pod with the label name given to the monitor label.
14.3.4.3.1. Querying the node-exporter service for metrics

Metrics are exposed for virtual machines through an HTTP service endpoint under the /metrics canonical name. When you query for metrics, Prometheus directly scrapes the metrics from the metrics endpoint exposed by the virtual machines and presents these metrics for viewing.

Prerequisites

  • You have access to the cluster as a user with cluster-admin privileges or the monitoring-edit role.
  • You have enabled monitoring for the user-defined project by configuring the node-exporter service.

Procedure

  1. Obtain the HTTP service endpoint by specifying the namespace for the service:

    $ oc get service -n <namespace> <node-exporter-service>
  2. To list all available metrics for the node-exporter service, query the metrics resource.

    $ curl http://<172.30.226.162:9100>/metrics | grep -vE "^#|^$"

    Example output

    node_arp_entries{device="eth0"} 1
    node_boot_time_seconds 1.643153218e+09
    node_context_switches_total 4.4938158e+07
    node_cooling_device_cur_state{name="0",type="Processor"} 0
    node_cooling_device_max_state{name="0",type="Processor"} 0
    node_cpu_guest_seconds_total{cpu="0",mode="nice"} 0
    node_cpu_guest_seconds_total{cpu="0",mode="user"} 0
    node_cpu_seconds_total{cpu="0",mode="idle"} 1.10586485e+06
    node_cpu_seconds_total{cpu="0",mode="iowait"} 37.61
    node_cpu_seconds_total{cpu="0",mode="irq"} 233.91
    node_cpu_seconds_total{cpu="0",mode="nice"} 551.47
    node_cpu_seconds_total{cpu="0",mode="softirq"} 87.3
    node_cpu_seconds_total{cpu="0",mode="steal"} 86.12
    node_cpu_seconds_total{cpu="0",mode="system"} 464.15
    node_cpu_seconds_total{cpu="0",mode="user"} 1075.2
    node_disk_discard_time_seconds_total{device="vda"} 0
    node_disk_discard_time_seconds_total{device="vdb"} 0
    node_disk_discarded_sectors_total{device="vda"} 0
    node_disk_discarded_sectors_total{device="vdb"} 0
    node_disk_discards_completed_total{device="vda"} 0
    node_disk_discards_completed_total{device="vdb"} 0
    node_disk_discards_merged_total{device="vda"} 0
    node_disk_discards_merged_total{device="vdb"} 0
    node_disk_info{device="vda",major="252",minor="0"} 1
    node_disk_info{device="vdb",major="252",minor="16"} 1
    node_disk_io_now{device="vda"} 0
    node_disk_io_now{device="vdb"} 0
    node_disk_io_time_seconds_total{device="vda"} 174
    node_disk_io_time_seconds_total{device="vdb"} 0.054
    node_disk_io_time_weighted_seconds_total{device="vda"} 259.79200000000003
    node_disk_io_time_weighted_seconds_total{device="vdb"} 0.039
    node_disk_read_bytes_total{device="vda"} 3.71867136e+08
    node_disk_read_bytes_total{device="vdb"} 366592
    node_disk_read_time_seconds_total{device="vda"} 19.128
    node_disk_read_time_seconds_total{device="vdb"} 0.039
    node_disk_reads_completed_total{device="vda"} 5619
    node_disk_reads_completed_total{device="vdb"} 96
    node_disk_reads_merged_total{device="vda"} 5
    node_disk_reads_merged_total{device="vdb"} 0
    node_disk_write_time_seconds_total{device="vda"} 240.66400000000002
    node_disk_write_time_seconds_total{device="vdb"} 0
    node_disk_writes_completed_total{device="vda"} 71584
    node_disk_writes_completed_total{device="vdb"} 0
    node_disk_writes_merged_total{device="vda"} 19761
    node_disk_writes_merged_total{device="vdb"} 0
    node_disk_written_bytes_total{device="vda"} 2.007924224e+09
    node_disk_written_bytes_total{device="vdb"} 0

14.3.4.4. Creating a ServiceMonitor resource for the node exporter service

You can use a Prometheus client library and scrape metrics from the /metrics endpoint to access and view the metrics exposed by the node-exporter service. Use a ServiceMonitor custom resource definition (CRD) to monitor the node exporter service.

Prerequisites

  • You have access to the cluster as a user with cluster-admin privileges or the monitoring-edit role.
  • You have enabled monitoring for the user-defined project by configuring the node-exporter service.

Procedure

  1. Create a YAML file for the ServiceMonitor resource configuration. In this example, the service monitor matches any service with the label metrics and queries the exmet port every 30 seconds.

    apiVersion: monitoring.coreos.com/v1
    kind: ServiceMonitor
    metadata:
      labels:
        k8s-app: node-exporter-metrics-monitor
      name: node-exporter-metrics-monitor 1
      namespace: dynamation 2
    spec:
      endpoints:
      - interval: 30s 3
        port: exmet 4
        scheme: http
      selector:
        matchLabels:
          servicetype: metrics
    1
    The name of the ServiceMonitor.
    2
    The namespace where the ServiceMonitor is created.
    3
    The interval at which the port will be queried.
    4
    The name of the port that is queried every 30 seconds
  2. Create the ServiceMonitor configuration for the node-exporter service.

    $ oc create -f node-exporter-metrics-monitor.yaml
14.3.4.4.1. Accessing the node exporter service outside the cluster

You can access the node-exporter service outside the cluster and view the exposed metrics.

Prerequisites

  • You have access to the cluster as a user with cluster-admin privileges or the monitoring-edit role.
  • You have enabled monitoring for the user-defined project by configuring the node-exporter service.

Procedure

  1. Expose the node-exporter service.

    $ oc expose service -n <namespace> <node_exporter_service_name>
  2. Obtain the FQDN (Fully Qualified Domain Name) for the route.

    $ oc get route -o=custom-columns=NAME:.metadata.name,DNS:.spec.host

    Example output

    NAME                    DNS
    node-exporter-service   node-exporter-service-dynamation.apps.cluster.example.org

  3. Use the curl command to display metrics for the node-exporter service.

    $ curl -s http://node-exporter-service-dynamation.apps.cluster.example.org/metrics

    Example output

    go_gc_duration_seconds{quantile="0"} 1.5382e-05
    go_gc_duration_seconds{quantile="0.25"} 3.1163e-05
    go_gc_duration_seconds{quantile="0.5"} 3.8546e-05
    go_gc_duration_seconds{quantile="0.75"} 4.9139e-05
    go_gc_duration_seconds{quantile="1"} 0.000189423

14.3.4.5. Additional resources

14.3.5. Virtual machine health checks

You can configure virtual machine (VM) health checks by defining readiness and liveness probes in the VirtualMachine resource.

14.3.5.1. About readiness and liveness probes

Use readiness and liveness probes to detect and handle unhealthy virtual machines (VMs). You can include one or more probes in the specification of the VM to ensure that traffic does not reach a VM that is not ready for it and that a new VM is created when a VM becomes unresponsive.

A readiness probe determines whether a VM is ready to accept service requests. If the probe fails, the VM is removed from the list of available endpoints until the VM is ready.

A liveness probe determines whether a VM is responsive. If the probe fails, the VM is deleted and a new VM is created to restore responsiveness.

You can configure readiness and liveness probes by setting the spec.readinessProbe and the spec.livenessProbe fields of the VirtualMachine object. These fields support the following tests:

HTTP GET
The probe determines the health of the VM by using a web hook. The test is successful if the HTTP response code is between 200 and 399. You can use an HTTP GET test with applications that return HTTP status codes when they are completely initialized.
TCP socket
The probe attempts to open a socket to the VM. The VM is only considered healthy if the probe can establish a connection. You can use a TCP socket test with applications that do not start listening until initialization is complete.
Guest agent ping
The probe uses the guest-ping command to determine if the QEMU guest agent is running on the virtual machine.
14.3.5.1.1. Defining an HTTP readiness probe

Define an HTTP readiness probe by setting the spec.readinessProbe.httpGet field of the virtual machine (VM) configuration.

Procedure

  1. Include details of the readiness probe in the VM configuration file.

    Sample readiness probe with an HTTP GET test

    # ...
    spec:
      readinessProbe:
        httpGet: 1
          port: 1500 2
          path: /healthz 3
          httpHeaders:
          - name: Custom-Header
            value: Awesome
        initialDelaySeconds: 120 4
        periodSeconds: 20 5
        timeoutSeconds: 10 6
        failureThreshold: 3 7
        successThreshold: 3 8
    # ...

    1
    The HTTP GET request to perform to connect to the VM.
    2
    The port of the VM that the probe queries. In the above example, the probe queries port 1500.
    3
    The path to access on the HTTP server. In the above example, if the handler for the server’s /healthz path returns a success code, the VM is considered to be healthy. If the handler returns a failure code, the VM is removed from the list of available endpoints.
    4
    The time, in seconds, after the VM starts before the readiness probe is initiated.
    5
    The delay, in seconds, between performing probes. The default delay is 10 seconds. This value must be greater than timeoutSeconds.
    6
    The number of seconds of inactivity after which the probe times out and the VM is assumed to have failed. The default value is 1. This value must be lower than periodSeconds.
    7
    The number of times that the probe is allowed to fail. The default is 3. After the specified number of attempts, the pod is marked Unready.
    8
    The number of times that the probe must report success, after a failure, to be considered successful. The default is 1.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml
14.3.5.1.2. Defining a TCP readiness probe

Define a TCP readiness probe by setting the spec.readinessProbe.tcpSocket field of the virtual machine (VM) configuration.

Procedure

  1. Include details of the TCP readiness probe in the VM configuration file.

    Sample readiness probe with a TCP socket test

    # ...
    spec:
      readinessProbe:
        initialDelaySeconds: 120 1
        periodSeconds: 20 2
        tcpSocket: 3
          port: 1500 4
        timeoutSeconds: 10 5
    # ...

    1
    The time, in seconds, after the VM starts before the readiness probe is initiated.
    2
    The delay, in seconds, between performing probes. The default delay is 10 seconds. This value must be greater than timeoutSeconds.
    3
    The TCP action to perform.
    4
    The port of the VM that the probe queries.
    5
    The number of seconds of inactivity after which the probe times out and the VM is assumed to have failed. The default value is 1. This value must be lower than periodSeconds.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml
14.3.5.1.3. Defining an HTTP liveness probe

Define an HTTP liveness probe by setting the spec.livenessProbe.httpGet field of the virtual machine (VM) configuration. You can define both HTTP and TCP tests for liveness probes in the same way as readiness probes. This procedure configures a sample liveness probe with an HTTP GET test.

Procedure

  1. Include details of the HTTP liveness probe in the VM configuration file.

    Sample liveness probe with an HTTP GET test

    # ...
    spec:
      livenessProbe:
        initialDelaySeconds: 120 1
        periodSeconds: 20 2
        httpGet: 3
          port: 1500 4
          path: /healthz 5
          httpHeaders:
          - name: Custom-Header
            value: Awesome
        timeoutSeconds: 10 6
    # ...

    1
    The time, in seconds, after the VM starts before the liveness probe is initiated.
    2
    The delay, in seconds, between performing probes. The default delay is 10 seconds. This value must be greater than timeoutSeconds.
    3
    The HTTP GET request to perform to connect to the VM.
    4
    The port of the VM that the probe queries. In the above example, the probe queries port 1500. The VM installs and runs a minimal HTTP server on port 1500 via cloud-init.
    5
    The path to access on the HTTP server. In the above example, if the handler for the server’s /healthz path returns a success code, the VM is considered to be healthy. If the handler returns a failure code, the VM is deleted and a new VM is created.
    6
    The number of seconds of inactivity after which the probe times out and the VM is assumed to have failed. The default value is 1. This value must be lower than periodSeconds.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml

14.3.5.2. Defining a watchdog

You can define a watchdog to monitor the health of the guest operating system by performing the following steps:

  1. Configure a watchdog device for the virtual machine (VM).
  2. Install the watchdog agent on the guest.

The watchdog device monitors the agent and performs one of the following actions if the guest operating system is unresponsive:

  • poweroff: The VM powers down immediately. If spec.running is set to true or spec.runStrategy is not set to manual, then the VM reboots.
  • reset: The VM reboots in place and the guest operating system cannot react.

    Note

    The reboot time might cause liveness probes to time out. If cluster-level protections detect a failed liveness probe, the VM might be forcibly rescheduled, increasing the reboot time.

  • shutdown: The VM gracefully powers down by stopping all services.
Note

Watchdog is not available for Windows VMs.

14.3.5.2.1. Configuring a watchdog device for the virtual machine

You configure a watchdog device for the virtual machine (VM).

Prerequisites

  • The VM must have kernel support for an i6300esb watchdog device. Red Hat Enterprise Linux (RHEL) images support i6300esb.

Procedure

  1. Create a YAML file with the following contents:

    apiVersion: kubevirt.io/v1
    kind: VirtualMachine
    metadata:
      labels:
        kubevirt.io/vm: vm2-rhel84-watchdog
      name: <vm-name>
    spec:
      running: false
      template:
        metadata:
          labels:
            kubevirt.io/vm: vm2-rhel84-watchdog
        spec:
          domain:
            devices:
              watchdog:
                name: <watchdog>
                i6300esb:
                  action: "poweroff" 1
    # ...
    1
    Specify poweroff, reset, or shutdown.

    The example above configures the i6300esb watchdog device on a RHEL8 VM with the poweroff action and exposes the device as /dev/watchdog.

    This device can now be used by the watchdog binary.

  2. Apply the YAML file to your cluster by running the following command:

    $ oc apply -f <file_name>.yaml
Important

This procedure is provided for testing watchdog functionality only and must not be run on production machines.

  1. Run the following command to verify that the VM is connected to the watchdog device:

    $ lspci | grep watchdog -i
  2. Run one of the following commands to confirm the watchdog is active:

    • Trigger a kernel panic:

      # echo c > /proc/sysrq-trigger
    • Stop the watchdog service:

      # pkill -9 watchdog
14.3.5.2.2. Installing the watchdog agent on the guest

You install the watchdog agent on the guest and start the watchdog service.

Procedure

  1. Log in to the virtual machine as root user.
  2. Install the watchdog package and its dependencies:

    # yum install watchdog
  3. Uncomment the following line in the /etc/watchdog.conf file and save the changes:

    #watchdog-device = /dev/watchdog
  4. Enable the watchdog service to start on boot:

    # systemctl enable --now watchdog.service

14.3.5.3. Defining a guest agent ping probe

Define a guest agent ping probe by setting the spec.readinessProbe.guestAgentPing field of the virtual machine (VM) configuration.

Important

The guest agent ping probe is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

Prerequisites

  • The QEMU guest agent must be installed and enabled on the virtual machine.

Procedure

  1. Include details of the guest agent ping probe in the VM configuration file. For example:

    Sample guest agent ping probe

    # ...
    spec:
      readinessProbe:
        guestAgentPing: {} 1
        initialDelaySeconds: 120 2
        periodSeconds: 20 3
        timeoutSeconds: 10 4
        failureThreshold: 3 5
        successThreshold: 3 6
    # ...

    1
    The guest agent ping probe to connect to the VM.
    2
    Optional: The time, in seconds, after the VM starts before the guest agent probe is initiated.
    3
    Optional: The delay, in seconds, between performing probes. The default delay is 10 seconds. This value must be greater than timeoutSeconds.
    4
    Optional: The number of seconds of inactivity after which the probe times out and the VM is assumed to have failed. The default value is 1. This value must be lower than periodSeconds.
    5
    Optional: The number of times that the probe is allowed to fail. The default is 3. After the specified number of attempts, the pod is marked Unready.
    6
    Optional: The number of times that the probe must report success, after a failure, to be considered successful. The default is 1.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml

14.3.5.4. Additional resources

14.4. Troubleshooting

OpenShift Virtualization provides tools and logs for troubleshooting virtual machines and virtualization components.

You can troubleshoot OpenShift Virtualization components by using the tools provided in the web console or by using the oc CLI tool.

14.4.1. Events

OpenShift Container Platform events are records of important life-cycle information and are useful for monitoring and troubleshooting virtual machine, namespace, and resource issues.

  • VM events: Navigate to the Events tab of the VirtualMachine details page in the web console.

    Namespace events

    You can view namespace events by running the following command:

    $ oc get events -n <namespace>

    See the list of events for details about specific events.

    Resource events

    You can view resource events by running the following command:

    $ oc describe <resource> <resource_name>

14.4.2. Logs

You can review the following logs for troubleshooting:

14.4.2.1. Viewing virtual machine logs with the web console

You can view virtual machine logs with the OpenShift Container Platform web console.

Procedure

  1. Navigate to Virtualization VirtualMachines.
  2. Select a virtual machine to open the VirtualMachine details page.
  3. On the Details tab, click the pod name to open the Pod details page.
  4. Click the Logs tab to view the logs.

14.4.2.2. Viewing OpenShift Virtualization pod logs

You can view logs for OpenShift Virtualization pods by using the oc CLI tool.

You can configure the verbosity level of the logs by editing the HyperConverged custom resource (CR).

14.4.2.2.1. Viewing OpenShift Virtualization pod logs with the CLI

You can view logs for the OpenShift Virtualization pods by using the oc CLI tool.

Procedure

  1. View a list of pods in the OpenShift Virtualization namespace by running the following command:

    $ oc get pods -n openshift-cnv

    Example 14.3. Example output

    NAME                               READY   STATUS    RESTARTS   AGE
    disks-images-provider-7gqbc        1/1     Running   0          32m
    disks-images-provider-vg4kx        1/1     Running   0          32m
    virt-api-57fcc4497b-7qfmc          1/1     Running   0          31m
    virt-api-57fcc4497b-tx9nc          1/1     Running   0          31m
    virt-controller-76c784655f-7fp6m   1/1     Running   0          30m
    virt-controller-76c784655f-f4pbd   1/1     Running   0          30m
    virt-handler-2m86x                 1/1     Running   0          30m
    virt-handler-9qs6z                 1/1     Running   0          30m
    virt-operator-7ccfdbf65f-q5snk     1/1     Running   0          32m
    virt-operator-7ccfdbf65f-vllz8     1/1     Running   0          32m
  2. View the pod log by running the following command:

    $ oc logs -n openshift-cnv <pod_name>
    Note

    If a pod fails to start, you can use the --previous option to view logs from the last attempt.

    To monitor log output in real time, use the -f option.

    Example 14.4. Example output

    {"component":"virt-handler","level":"info","msg":"set verbosity to 2","pos":"virt-handler.go:453","timestamp":"2022-04-17T08:58:37.373695Z"}
    {"component":"virt-handler","level":"info","msg":"set verbosity to 2","pos":"virt-handler.go:453","timestamp":"2022-04-17T08:58:37.373726Z"}
    {"component":"virt-handler","level":"info","msg":"setting rate limiter to 5 QPS and 10 Burst","pos":"virt-handler.go:462","timestamp":"2022-04-17T08:58:37.373782Z"}
    {"component":"virt-handler","level":"info","msg":"CPU features of a minimum baseline CPU model: map[apic:true clflush:true cmov:true cx16:true cx8:true de:true fpu:true fxsr:true lahf_lm:true lm:true mca:true mce:true mmx:true msr:true mtrr:true nx:true pae:true pat:true pge:true pni:true pse:true pse36:true sep:true sse:true sse2:true sse4.1:true ssse3:true syscall:true tsc:true]","pos":"cpu_plugin.go:96","timestamp":"2022-04-17T08:58:37.390221Z"}
    {"component":"virt-handler","level":"warning","msg":"host model mode is expected to contain only one model","pos":"cpu_plugin.go:103","timestamp":"2022-04-17T08:58:37.390263Z"}
    {"component":"virt-handler","level":"info","msg":"node-labeller is running","pos":"node_labeller.go:94","timestamp":"2022-04-17T08:58:37.391011Z"}
14.4.2.2.2. Configuring OpenShift Virtualization pod log verbosity

You can configure the verbosity level of OpenShift Virtualization pod logs by editing the HyperConverged custom resource (CR).

Procedure

  1. To set log verbosity for specific components, open the HyperConverged CR in your default text editor by running the following command:

    $ oc edit hyperconverged kubevirt-hyperconverged -n openshift-cnv
  2. Set the log level for one or more components by editing the spec.logVerbosityConfig stanza. For example:

    apiVersion: hco.kubevirt.io/v1beta1
    kind: HyperConverged
    metadata:
      name: kubevirt-hyperconverged
    spec:
      logVerbosityConfig:
        kubevirt:
          virtAPI: 5 1
          virtController: 4
          virtHandler: 3
          virtLauncher: 2
          virtOperator: 6
    1
    The log verbosity value must be an integer in the range 1–9, where a higher number indicates a more detailed log. In this example, the virtAPI component logs are exposed if their priority level is 5 or higher.
  3. Apply your changes by saving and exiting the editor.
14.4.2.2.3. Common error messages

The following error messages might appear in OpenShift Virtualization logs:

ErrImagePull or ImagePullBackOff
Indicates an incorrect deployment configuration or problems with the images that are referenced.

14.4.2.3. Viewing aggregated OpenShift Virtualization logs with the LokiStack

You can view aggregated logs for OpenShift Virtualization pods and containers by using the LokiStack in the web console.

Prerequisites

  • You deployed the LokiStack.

Procedure

  1. Navigate to Observe Logs in the web console.
  2. Select application, for virt-launcher pod logs, or infrastructure, for OpenShift Virtualization control plane pods and containers, from the log type list.
  3. Click Show Query to display the query field.
  4. Enter the LogQL query in the query field and click Run Query to display the filtered logs.
14.4.2.3.1. OpenShift Virtualization LogQL queries

You can view and filter aggregated logs for OpenShift Virtualization components by running Loki Query Language (LogQL) queries on the Observe Logs page in the web console.

The default log type is infrastructure. The virt-launcher log type is application.

Optional: You can include or exclude strings or regular expressions by using line filter expressions.

Note

If the query matches a large number of logs, the query might time out.

Table 14.4. OpenShift Virtualization LogQL example queries
ComponentLogQL query

All

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"

cdi-apiserver

cdi-deployment

cdi-operator

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
|kubernetes_labels_app_kubernetes_io_component="storage"

hco-operator

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
|kubernetes_labels_app_kubernetes_io_component="deployment"

kubemacpool

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
|kubernetes_labels_app_kubernetes_io_component="network"

virt-api

virt-controller

virt-handler

virt-operator

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
|kubernetes_labels_app_kubernetes_io_component="compute"

ssp-operator

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
|kubernetes_labels_app_kubernetes_io_component="schedule"

Container

{log_type=~".+",kubernetes_container_name=~"<container>|<container>"} 1
|json|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
1
Specify one or more containers separated by a pipe (|).

virt-launcher

You must select application from the log type list before running this query.

{log_type=~".+", kubernetes_container_name="compute"}|json
|!= "custom-ga-command" 1
1
|!= "custom-ga-command" excludes libvirt logs that contain the string custom-ga-command. (BZ#2177684)

You can filter log lines to include or exclude strings or regular expressions by using line filter expressions.

Table 14.5. Line filter expressions
Line filter expressionDescription

|= "<string>"

Log line contains string

!= "<string>"

Log line does not contain string

|~ "<regex>"

Log line contains regular expression

!~ "<regex>"

Log line does not contain regular expression

Example line filter expression

{log_type=~".+"}|json
|kubernetes_labels_app_kubernetes_io_part_of="hyperconverged-cluster"
|= "error" != "timeout"

14.4.2.3.2. Additional resources for LokiStack and LogQL

14.4.3. Troubleshooting data volumes

You can check the Conditions and Events sections of the DataVolume object to analyze and resolve issues.

14.4.3.1. About data volume conditions and events

You can diagnose data volume issues by examining the output of the Conditions and Events sections generated by the command:

$ oc describe dv <DataVolume>

The Conditions section displays the following Types:

  • Bound
  • Running
  • Ready

The Events section provides the following additional information:

  • Type of event
  • Reason for logging
  • Source of the event
  • Message containing additional diagnostic information.

The output from oc describe does not always contains Events.

An event is generated when the Status, Reason, or Message changes. Both conditions and events react to changes in the state of the data volume.

For example, if you misspell the URL during an import operation, the import generates a 404 message. That message change generates an event with a reason. The output in the Conditions section is updated as well.

14.4.3.2. Analyzing data volume conditions and events

By inspecting the Conditions and Events sections generated by the describe command, you determine the state of the data volume in relation to persistent volume claims (PVCs), and whether or not an operation is actively running or completed. You might also receive messages that offer specific details about the status of the data volume, and how it came to be in its current state.

There are many different combinations of conditions. Each must be evaluated in its unique context.

Examples of various combinations follow.

  • Bound - A successfully bound PVC displays in this example.

    Note that the Type is Bound, so the Status is True. If the PVC is not bound, the Status is False.

    When the PVC is bound, an event is generated stating that the PVC is bound. In this case, the Reason is Bound and Status is True. The Message indicates which PVC owns the data volume.

    Message, in the Events section, provides further details including how long the PVC has been bound (Age) and by what resource (From), in this case datavolume-controller:

    Example output

    Status:
      Conditions:
        Last Heart Beat Time:  2020-07-15T03:58:24Z
        Last Transition Time:  2020-07-15T03:58:24Z
        Message:               PVC win10-rootdisk Bound
        Reason:                Bound
        Status:                True
        Type:                  Bound
    ...
      Events:
        Type     Reason     Age    From                   Message
        ----     ------     ----   ----                   -------
        Normal   Bound      24s    datavolume-controller  PVC example-dv Bound

  • Running - In this case, note that Type is Running and Status is False, indicating that an event has occurred that caused an attempted operation to fail, changing the Status from True to False.

    However, note that Reason is Completed and the Message field indicates Import Complete.

    In the Events section, the Reason and Message contain additional troubleshooting information about the failed operation. In this example, the Message displays an inability to connect due to a 404, listed in the Events section’s first Warning.

    From this information, you conclude that an import operation was running, creating contention for other operations that are attempting to access the data volume:

    Example output

    Status:
      Conditions:
        Last Heart Beat Time:  2020-07-15T04:31:39Z
        Last Transition Time:  2020-07-15T04:31:39Z
        Message:               Import Complete
        Reason:                Completed
        Status:                False
        Type:                  Running
    ...
      Events:
        Type     Reason       Age                From                   Message
        ----     ------       ----               ----                   -------
        Warning  Error        12s (x2 over 14s)  datavolume-controller  Unable to connect
        to http data source: expected status code 200, got 404. Status: 404 Not Found

  • Ready – If Type is Ready and Status is True, then the data volume is ready to be used, as in the following example. If the data volume is not ready to be used, the Status is False:

    Example output

    Status:
      Conditions:
        Last Heart Beat Time: 2020-07-15T04:31:39Z
        Last Transition Time:  2020-07-15T04:31:39Z
        Status:                True
        Type:                  Ready

14.5. OpenShift Virtualization runbooks

Runbooks for the OpenShift Virtualization Operator are maintained in the openshift/runbooks Git repository, and you can view them on GitHub. To diagnose and resolve issues that trigger OpenShift Virtualization alerts, follow the procedures in the runbooks.

OpenShift Virtualization alerts are displayed in the Virtualization Overview tab in the web console.

14.5.1. CDIDataImportCronOutdated

14.5.2. CDIDataVolumeUnusualRestartCount

14.5.3. CDIDefaultStorageClassDegraded

14.5.4. CDIMultipleDefaultVirtStorageClasses

14.5.5. CDINoDefaultStorageClass

14.5.6. CDINotReady

14.5.7. CDIOperatorDown

14.5.8. CDIStorageProfilesIncomplete

14.5.9. CnaoDown

14.5.10. CnaoNMstateMigration

14.5.11. HCOInstallationIncomplete

14.5.12. HPPNotReady

14.5.13. HPPOperatorDown

14.5.14. HPPSharingPoolPathWithOS

14.5.15. KubemacpoolDown

14.5.16. KubeMacPoolDuplicateMacsFound

14.5.17. KubeVirtComponentExceedsRequestedCPU

  • The KubeVirtComponentExceedsRequestedCPU alert is deprecated.

14.5.18. KubeVirtComponentExceedsRequestedMemory

  • The KubeVirtComponentExceedsRequestedMemory alert is deprecated.

14.5.19. KubeVirtCRModified

14.5.20. KubeVirtDeprecatedAPIRequested

14.5.21. KubeVirtNoAvailableNodesToRunVMs

14.5.22. KubevirtVmHighMemoryUsage

14.5.23. KubeVirtVMIExcessiveMigrations

14.5.24. LowKVMNodesCount

14.5.25. LowReadyVirtControllersCount

14.5.26. LowReadyVirtOperatorsCount

14.5.27. LowVirtAPICount

14.5.28. LowVirtControllersCount

14.5.29. LowVirtOperatorCount

14.5.30. NetworkAddonsConfigNotReady

14.5.31. NoLeadingVirtOperator

14.5.32. NoReadyVirtController

14.5.33. NoReadyVirtOperator

14.5.34. OrphanedVirtualMachineInstances

14.5.35. OutdatedVirtualMachineInstanceWorkloads

14.5.36. SingleStackIPv6Unsupported

14.5.37. SSPCommonTemplatesModificationReverted

14.5.38. SSPDown

14.5.39. SSPFailingToReconcile

14.5.40. SSPHighRateRejectedVms

14.5.41. SSPTemplateValidatorDown

14.5.42. UnsupportedHCOModification

14.5.43. VirtAPIDown

14.5.44. VirtApiRESTErrorsBurst

14.5.45. VirtApiRESTErrorsHigh

14.5.46. VirtControllerDown

14.5.47. VirtControllerRESTErrorsBurst

14.5.48. VirtControllerRESTErrorsHigh

14.5.49. VirtHandlerDaemonSetRolloutFailing

14.5.50. VirtHandlerRESTErrorsBurst

14.5.51. VirtHandlerRESTErrorsHigh

14.5.52. VirtOperatorDown

14.5.53. VirtOperatorRESTErrorsBurst

14.5.54. VirtOperatorRESTErrorsHigh

14.5.55. VirtualMachineCRCErrors

  • The runbook for the VirtualMachineCRCErrors alert is deprecated because the alert was renamed to VMStorageClassWarning.

14.5.56. VMCannotBeEvicted

14.5.57. VMStorageClassWarning

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