Chapter 12. Monitoring

12.1. Monitoring overview

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

Monitoring OpenShift Virtualization VMs health status

View the overall health of your OpenShift Virtualization environment in the web console by navigating to the Home

Overview page in the OpenShift Container Platform web console. The Status card displays the overall health of OpenShift Virtualization based on the alerts and conditions.

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

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.
VM health checks: Configure readiness, liveness, and guest agent ping probes and a watchdog for VMs.
Runbooks: Diagnose and resolve issues that trigger OpenShift Virtualization alerts in the OpenShift Container Platform web console.

12.2. OpenShift Virtualization cluster checkup framework

OpenShift Virtualization includes the following predefined checkups that can be used for cluster maintenance and troubleshooting:

Latency checkup: Verifies network connectivity and measures latency between two virtual machines (VMs) that are attached to a secondary network interface.
DPDK checkup: Verifies that a node can run a VM with a Data Plane Development Kit (DPDK) workload with zero packet loss.

Important

The OpenShift Virtualization 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.

12.2.1. About the OpenShift Virtualization 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.

12.2.1.1. Running a 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:

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

Prerequisites

You installed the OpenShift CLI (oc).
The cluster has at least two worker nodes.
You configured a network attachment definition for a namespace.

Procedure

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

Example 12.1. 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

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.
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.

Apply the config map manifest in the target namespace:

$ oc apply -n <target_namespace> -f <latency_config_map>.yaml

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:v4.14.0
          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

Apply the Job manifest:

$ oc apply -n <target_namespace> -f <latency_job>.yaml

Wait for the job to complete:

$ oc wait job kubevirt-vm-latency-checkup -n <target_namespace> --for condition=complete --timeout 6m

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.

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>
```

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

Optional: If you do not plan to run another checkup, delete the roles manifest:
```
$ oc delete -f <latency_sa_roles_rolebinding>.yaml
```

12.2.1.2. 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 and a VM running a test DPDK application.

You run a DPDK checkup by performing the following steps:

Create a service account, role, and role bindings for the DPDK checkup.
Create a config map to provide the input to run the checkup and to store the results.
Create a job to run the checkup.
Review the results in the config map.
Optional: To rerun the checkup, delete the existing config map and job and then create a new config map and job.
When you are finished, delete the DPDK checkup resources.

Prerequisites

You have installed the OpenShift CLI (oc).
The cluster is configured to run DPDK applications.
The project is configured to run DPDK applications.

Procedure

Create a ServiceAccount, Role, and RoleBinding manifest for the DPDK checkup:

Example 12.2. 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: [ "configmaps" ]
    verbs: [ "create", "delete" ]
---
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

Apply the ServiceAccount, Role, and RoleBinding manifest:

$ oc apply -n <target_namespace> -f <dpdk_sa_roles_rolebinding>.yaml

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.trafficGenContainerDiskImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-traffic-gen:v0.2.0 2
  spec.param.vmUnderTestContainerDiskImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-vm:v0.2.0" 3
```
1
The name of the NetworkAttachmentDefinition object.
2
The container disk image for the traffic generator. In this example, the image is pulled from the upstream Project Quay Container Registry.
3
The container disk image for the VM under test. In this example, the image is pulled from the upstream Project Quay Container Registry.

Apply the ConfigMap manifest in the target namespace:

$ oc apply -n <target_namespace> -f <dpdk_config_map>.yaml

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:v4.14.0
          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

Apply the Job manifest:

$ oc apply -n <target_namespace> -f <dpdk_job>.yaml

Wait for the job to complete:

$ oc wait job dpdk-checkup -n <target_namespace> --for condition=complete --timeout 10m

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: 10m
  spec.param.NetworkAttachmentDefinitionName: "dpdk-network-1"
  spec.param.trafficGenContainerDiskImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-traffic-gen:v0.2.0"
  spec.param.vmUnderTestContainerDiskImage: "quay.io/kiagnose/kubevirt-dpdk-checkup-vm:v0.2.0"
  status.succeeded: "true" 1
  status.failureReason: "" 2
  status.startTimestamp: "2023-07-31T13:14:38Z" 3
  status.completionTimestamp: "2023-07-31T13:19:41Z" 4
  status.result.trafficGenSentPackets: "480000000" 5
  status.result.trafficGenOutputErrorPackets: "0" 6
  status.result.trafficGenInputErrorPackets: "0" 7
  status.result.trafficGenActualNodeName: worker-dpdk1 8
  status.result.vmUnderTestActualNodeName: worker-dpdk2 9
  status.result.vmUnderTestReceivedPackets: "480000000" 10
  status.result.vmUnderTestRxDroppedPackets: "0" 11
  status.result.vmUnderTestTxDroppedPackets: "0" 12

1: Specifies if the checkup is successful (true) or not (false).
2: The reason for failure if the checkup fails.
3: The time when the checkup started, in RFC 3339 time format.
4: The time when the checkup has completed, in RFC 3339 time format.
5: The number of packets sent from the traffic generator.
6: The number of error packets sent from the traffic generator.
7: The number of error packets received by the traffic generator.
8: The node on which the traffic generator VM was scheduled.
9: The node on which the VM under test was scheduled.
10: The number of packets received on the VM under test.
11: The ingress traffic packets that were dropped by the DPDK application.
12: The egress traffic packets that were dropped from the DPDK application.

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

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
```

12.2.1.2.1. 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 12.1. DPDK checkup config map input parameters
Parameter	Description	Is 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.trafficGenContainerDiskImage`	The container disk image for the traffic generator. The default value is `quay.io/kiagnose/kubevirt-dpdk-checkup-traffic-gen:main`.	False
`spec.param.trafficGenTargetNodeName`	The node on which the traffic generator VM is to be scheduled. The node should be configured to allow DPDK traffic.	False
`spec.param.trafficGenPacketsPerSecond`	The number of packets per second, in kilo (k) or million(m). The default value is 8m.	False
`spec.param.vmUnderTestContainerDiskImage`	The container disk image for the VM under test. The default value is `quay.io/kiagnose/kubevirt-dpdk-checkup-vm:main`.	False
`spec.param.vmUnderTestTargetNodeName`	The node on which the VM under test is to be scheduled. The node should be configured to allow DPDK traffic.	False
`spec.param.testDuration`	The duration, in minutes, for which the traffic generator runs. The default value is 5 minutes.	False
`spec.param.portBandwidthGbps`	The maximum bandwidth of the SR-IOV NIC. The default value is 10Gbps.	False
`spec.param.verbose`	When set to `true`, it increases the verbosity of the checkup log. The default value is `false`.	False

12.2.1.2.2. 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

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
```

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

Push the blueprint file to the image builder tool by running the following command:
```
# composer-cli blueprints push dpdk-vm.toml
```
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
```
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
```
Enter the following command to download the qcow2 image file by specifying its UUID:
```
# composer-cli compose image <UUID>
```

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

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

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.
Build and tag the container by running the following command:
```
$ podman build . -t dpdk-rhel:latest
```
Push the container disk image to a registry that is accessible from your cluster by running the following command:
```
$ podman push dpdk-rhel:latest
```
Provide a link to the container disk image in the spec.param.vmContainerDiskImage attribute in the DPDK checkup config map.

12.2.2. Additional resources

12.3. 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.

12.3.1. 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.

12.3.2. 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.

12.3.2.1. 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

From the Administrator perspective in the OpenShift Container Platform web console, select Observe Metrics.

To add one or more queries, do any of the following:

Option	Description
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 next to the query, then choose Duplicate query.
Disable a query from being run.	Select the Options menu next to the query and choose Disable query.

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.
Optional: The page URL now contains the queries you ran. To use this set of queries again in the future, save this URL.

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:

Option	Description
Hide all metrics from a query.	Click the Options menu 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.

12.3.2.2. 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

From the Developer perspective in the OpenShift Container Platform web console, select Observe Metrics.
Select the project that you want to view metrics for in the Project: list.
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.

Explore the visualized metrics by doing any of the following:

Option	Description
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.

12.3.3. Virtualization metrics

The following metric descriptions include example Prometheus Query Language (PromQL) queries. These metrics are not an API and might change between versions. For a complete list of virtualization metrics, see KubeVirt components metrics.

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.

12.3.3.1. 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.

12.3.3.2. 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.

12.3.3.3. Storage metrics

12.3.3.3.1. 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.

12.3.3.3.2. 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.

12.3.3.3.3. 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.

12.3.3.4. 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.

12.3.3.5. 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.

12.3.4. Additional resources

12.4. 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.

12.4.1. 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

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.

Create the node-exporter service:

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

12.4.2. 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

Log on to the virtual machine.
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
```

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"

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

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

12.4.3. 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

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
```
Stop and restart the virtual machine to create a new pod with the label name given to the monitor label.

12.4.3.1. 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

Obtain the HTTP service endpoint by specifying the namespace for the service:
```
$ oc get service -n <namespace> <node-exporter-service>
```

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

12.4.4. 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

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
Create the ServiceMonitor configuration for the node-exporter service.
```
$ oc create -f node-exporter-metrics-monitor.yaml
```

12.4.4.1. 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

Expose the node-exporter service.

$ oc expose service -n <namespace> <node_exporter_service_name>

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

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

12.4.5. Additional resources

12.5. Virtual machine health checks

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

12.5.1. 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.

12.5.1.1. Defining an HTTP readiness probe

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

Procedure

Include details of the readiness probe in the VM configuration file.
Sample readiness probe with an HTTP GET test
```
apiVersion: kubevirt.io/v1
kind: VirtualMachine
metadata:
  annotations:
  name: fedora-vm
  namespace: example-namespace
# ...
spec:
  template:
    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.
Create the VM by running the following command:
```
$ oc create -f <file_name>.yaml
```

12.5.1.2. Defining a TCP readiness probe

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

Procedure

Include details of the TCP readiness probe in the VM configuration file.
Sample readiness probe with a TCP socket test
```
apiVersion: kubevirt.io/v1
kind: VirtualMachine
metadata:
  annotations:
  name: fedora-vm
  namespace: example-namespace
# ...
spec:
  template:
    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.
Create the VM by running the following command:
```
$ oc create -f <file_name>.yaml
```

12.5.1.3. 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

Include details of the HTTP liveness probe in the VM configuration file.
Sample liveness probe with an HTTP GET test
```
apiVersion: kubevirt.io/v1
kind: VirtualMachine
metadata:
  annotations:
  name: fedora-vm
  namespace: example-namespace
# ...
spec:
  template:
    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.
Create the VM by running the following command:
```
$ oc create -f <file_name>.yaml
```

12.5.2. Defining a watchdog

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

Configure a watchdog device for the virtual machine (VM).
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.

12.5.2.1. 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

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.

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.

Run the following command to verify that the VM is connected to the watchdog device:
```
$ lspci | grep watchdog -i
```
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
```

12.5.2.2. Installing the watchdog agent on the guest

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

Procedure

Log in to the virtual machine as root user.
Install the watchdog package and its dependencies:
```
# yum install watchdog
```
Uncomment the following line in the /etc/watchdog.conf file and save the changes:
```
#watchdog-device = /dev/watchdog
```
Enable the watchdog service to start on boot:
```
# systemctl enable --now watchdog.service
```

12.5.3. 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

Include details of the guest agent ping probe in the VM configuration file. For example:
Sample guest agent ping probe
```
apiVersion: kubevirt.io/v1
kind: VirtualMachine
metadata:
  annotations:
  name: fedora-vm
  namespace: example-namespace
# ...
spec:
  template:
    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.
Create the VM by running the following command:
```
$ oc create -f <file_name>.yaml
```

12.5.4. Additional resources

Monitoring application health by using health checks

12.6. 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.

12.6.1. CDIDataImportCronOutdated

View the runbook for the CDIDataImportCronOutdated alert.

12.6.2. CDIDataVolumeUnusualRestartCount

View the runbook for the CDIDataVolumeUnusualRestartCount alert.

12.6.3. CDIDefaultStorageClassDegraded

View the runbook for the CDIDefaultStorageClassDegraded alert.

12.6.4. CDIMultipleDefaultVirtStorageClasses

View the runbook for the CDIMultipleDefaultVirtStorageClasses alert.

12.6.5. CDINoDefaultStorageClass

View the runbook for the CDINoDefaultStorageClass alert.

12.6.6. CDINotReady

View the runbook for the CDINotReady alert.

12.6.7. CDIOperatorDown

View the runbook for the CDIOperatorDown alert.

12.6.8. CDIStorageProfilesIncomplete

View the runbook for the CDIStorageProfilesIncomplete alert.

12.6.9. CnaoDown

View the runbook for the CnaoDown alert.

12.6.10. CnaoNMstateMigration

View the runbook for the CnaoNMstateMigration alert.

12.6.11. HCOInstallationIncomplete

View the runbook for the HCOInstallationIncomplete alert.

12.6.12. HPPNotReady

View the runbook for the HPPNotReady alert.

12.6.13. HPPOperatorDown

View the runbook for the HPPOperatorDown alert.

12.6.14. HPPSharingPoolPathWithOS

View the runbook for the HPPSharingPoolPathWithOS alert.

12.6.15. KubemacpoolDown

View the runbook for the KubemacpoolDown alert.

12.6.16. KubeMacPoolDuplicateMacsFound

View the runbook for the KubeMacPoolDuplicateMacsFound alert.

12.6.17. KubeVirtComponentExceedsRequestedCPU

The KubeVirtComponentExceedsRequestedCPU alert is deprecated.

12.6.18. KubeVirtComponentExceedsRequestedMemory

The KubeVirtComponentExceedsRequestedMemory alert is deprecated.

12.6.19. KubeVirtCRModified

View the runbook for the KubeVirtCRModified alert.

12.6.20. KubeVirtDeprecatedAPIRequested

View the runbook for the KubeVirtDeprecatedAPIRequested alert.

12.6.21. KubeVirtNoAvailableNodesToRunVMs

View the runbook for the KubeVirtNoAvailableNodesToRunVMs alert.

12.6.22. KubevirtVmHighMemoryUsage

View the runbook for the KubevirtVmHighMemoryUsage alert.

12.6.23. KubeVirtVMIExcessiveMigrations

View the runbook for the KubeVirtVMIExcessiveMigrations alert.

12.6.24. LowKVMNodesCount

View the runbook for the LowKVMNodesCount alert.

12.6.25. LowReadyVirtControllersCount

View the runbook for the LowReadyVirtControllersCount alert.

12.6.26. LowReadyVirtOperatorsCount

View the runbook for the LowReadyVirtOperatorsCount alert.

12.6.27. LowVirtAPICount

View the runbook for the LowVirtAPICount alert.

12.6.28. LowVirtControllersCount

View the runbook for the LowVirtControllersCount alert.

12.6.29. LowVirtOperatorCount

View the runbook for the LowVirtOperatorCount alert.

12.6.30. NetworkAddonsConfigNotReady

View the runbook for the NetworkAddonsConfigNotReady alert.

12.6.31. NoLeadingVirtOperator

View the runbook for the NoLeadingVirtOperator alert.

12.6.32. NoReadyVirtController

View the runbook for the NoReadyVirtController alert.

12.6.33. NoReadyVirtOperator

View the runbook for the NoReadyVirtOperator alert.

12.6.34. OrphanedVirtualMachineInstances

View the runbook for the OrphanedVirtualMachineInstances alert.

12.6.35. OutdatedVirtualMachineInstanceWorkloads

View the runbook for the OutdatedVirtualMachineInstanceWorkloads alert.

12.6.36. SingleStackIPv6Unsupported

View the runbook for the SingleStackIPv6Unsupported alert.

12.6.37. SSPCommonTemplatesModificationReverted

View the runbook for the SSPCommonTemplatesModificationReverted alert.

12.6.38. SSPDown

View the runbook for the SSPDown alert.

12.6.39. SSPFailingToReconcile

View the runbook for the SSPFailingToReconcile alert.

12.6.40. SSPHighRateRejectedVms

View the runbook for the SSPHighRateRejectedVms alert.

12.6.41. SSPTemplateValidatorDown

View the runbook for the SSPTemplateValidatorDown alert.

12.6.42. UnsupportedHCOModification

View the runbook for the UnsupportedHCOModification alert.

12.6.43. VirtAPIDown

View the runbook for the VirtAPIDown alert.

12.6.44. VirtApiRESTErrorsBurst

View the runbook for the VirtApiRESTErrorsBurst alert.

12.6.45. VirtApiRESTErrorsHigh

View the runbook for the VirtApiRESTErrorsHigh alert.

12.6.46. VirtControllerDown

View the runbook for the VirtControllerDown alert.

12.6.47. VirtControllerRESTErrorsBurst

View the runbook for the VirtControllerRESTErrorsBurst alert.

12.6.48. VirtControllerRESTErrorsHigh

View the runbook for the VirtControllerRESTErrorsHigh alert.

12.6.49. VirtHandlerDaemonSetRolloutFailing

View the runbook for the VirtHandlerDaemonSetRolloutFailing alert.

12.6.50. VirtHandlerRESTErrorsBurst

View the runbook for the VirtHandlerRESTErrorsBurst alert.

12.6.51. VirtHandlerRESTErrorsHigh

View the runbook for the VirtHandlerRESTErrorsHigh alert.

12.6.52. VirtOperatorDown

View the runbook for the VirtOperatorDown alert.

12.6.53. VirtOperatorRESTErrorsBurst

View the runbook for the VirtOperatorRESTErrorsBurst alert.

12.6.54. VirtOperatorRESTErrorsHigh

View the runbook for the VirtOperatorRESTErrorsHigh alert.

12.6.55. VirtualMachineCRCErrors

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

12.6.56. VMCannotBeEvicted

View the runbook for the VMCannotBeEvicted alert.

12.6.57. VMStorageClassWarning

View the runbook for the VMStorageClassWarning alert.