검색

이 콘텐츠는 선택한 언어로 제공되지 않습니다.

Chapter 11. Monitoring

download PDF

11.1. Monitoring overview

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

Monitoring OpenShift Virtualization VM health status
View the overall health of your OpenShift Virtualization environment in the web console by navigating to the Home Overview page in the Red Hat OpenShift Service on AWS web console. The Status card displays the overall health of OpenShift Virtualization based on the alerts and conditions.
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 Red Hat OpenShift Service on AWS web console.

11.2. Prometheus queries for virtual resources

Use the Red Hat OpenShift Service on AWS monitoring dashboard to query virtualization metrics. OpenShift Virtualization provides metrics that you can use to monitor the consumption of cluster infrastructure resources, including network, storage, and guest memory swapping. You can also use metrics to query live migration status.

11.2.1. Prerequisites

  • For guest memory swapping queries to return data, memory swapping must be enabled on the virtual guests.

11.2.2. Querying metrics

The Red Hat OpenShift Service on AWS 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 dedicated-admin, you can query one or more namespaces at a time for metrics about 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.

11.2.2.1. Querying metrics for all projects as a cluster administrator

As a dedicated-admin or as a user with view permissions for all projects, you can access metrics for all default Red Hat OpenShift Service on AWS and user-defined projects in the Metrics UI.

Note

Only dedicated administrators have access to the third-party UIs provided with Red Hat OpenShift Service on AWS monitoring.

Prerequisites

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

Procedure

  1. From the Administrator perspective in the Red Hat OpenShift Service on AWS 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.

11.2.2.2. 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. Developers cannot access the third-party UIs provided with Red Hat OpenShift Service on AWS monitoring.

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 Red Hat OpenShift Service on AWS 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.

11.2.3. 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.

11.2.3.1. 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.

11.2.3.2. Storage metrics

11.2.3.2.1. 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.
11.2.3.2.2. Storage snapshot data
kubevirt_vmsnapshot_disks_restored_from_source
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{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.
11.2.3.2.3. 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.

11.2.3.3. 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
Returns the total amount (in bytes) of memory the virtual guest is swapping in. Type: Gauge.
kubevirt_vmi_memory_swap_out_traffic_bytes
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[6m])) + sum by (name, namespace) (rate(kubevirt_vmi_memory_swap_out_traffic_bytes[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.

11.2.3.4. Live migration metrics

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

kubevirt_vmi_migration_data_processed_bytes
The amount of guest operating system data that has migrated to the new virtual machine (VM). Type: Gauge.
kubevirt_vmi_migration_data_remaining_bytes
The amount of guest operating system data that remains to be migrated. Type: Gauge.
kubevirt_vmi_migration_memory_transfer_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_vmi_migrations_in_pending_phase
The number of pending migrations. Type: Gauge.
kubevirt_vmi_migrations_in_scheduling_phase
The number of scheduling migrations. Type: Gauge.
kubevirt_vmi_migrations_in_running_phase
The number of running migrations. Type: Gauge.
kubevirt_vmi_migration_succeeded
The number of successfully completed migrations. Type: Gauge.
kubevirt_vmi_migration_failed
The number of failed migrations. Type: Gauge.

11.2.4. Additional resources

11.3. Exposing custom metrics for virtual machines

Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS 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.

11.3.1. 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 Red Hat OpenShift Service on AWS 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

11.3.2. 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

11.3.3. 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 Red Hat OpenShift Service on AWS 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.

11.3.3.1. 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

11.3.4. 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

11.3.4.1. 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

11.3.5. Additional resources

11.4. Virtual machine health checks

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

11.4.1. 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.

11.4.1.1. 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

    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.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml

11.4.1.2. 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

    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.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml

11.4.1.3. 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

    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.
  2. Create the VM by running the following command:

    $ oc create -f <file_name>.yaml

11.4.2. 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.

11.4.2.1. 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

11.4.2.2. 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

11.5. OpenShift Virtualization runbooks

To diagnose and resolve issues that trigger OpenShift Virtualization alerts, follow the procedures in the runbooks for the OpenShift Virtualization Operator. Triggered OpenShift Virtualization alerts can be viewed in the main Observe Alerts tab in the web console, and also in the Virtualization Overview tab.

Runbooks for the OpenShift Virtualization Operator are maintained in the openshift/runbooks Git repository, and you can view them on GitHub.

11.5.1. CDIDataImportCronOutdated

11.5.2. CDIDataVolumeUnusualRestartCount

11.5.3. CDIDefaultStorageClassDegraded

11.5.4. CDIMultipleDefaultVirtStorageClasses

11.5.5. CDINoDefaultStorageClass

11.5.6. CDINotReady

11.5.7. CDIOperatorDown

11.5.8. CDIStorageProfilesIncomplete

11.5.9. CnaoDown

11.5.10. CnaoNMstateMigration

11.5.11. HCOInstallationIncomplete

11.5.12. HPPNotReady

11.5.13. HPPOperatorDown

11.5.14. HPPSharingPoolPathWithOS

11.5.15. KubemacpoolDown

11.5.16. KubeMacPoolDuplicateMacsFound

11.5.17. KubeVirtComponentExceedsRequestedCPU

  • The KubeVirtComponentExceedsRequestedCPU alert is deprecated.

11.5.18. KubeVirtComponentExceedsRequestedMemory

  • The KubeVirtComponentExceedsRequestedMemory alert is deprecated.

11.5.19. KubeVirtCRModified

11.5.20. KubeVirtDeprecatedAPIRequested

11.5.21. KubeVirtNoAvailableNodesToRunVMs

11.5.22. KubevirtVmHighMemoryUsage

11.5.23. KubeVirtVMIExcessiveMigrations

11.5.24. LowKVMNodesCount

11.5.25. LowReadyVirtControllersCount

11.5.26. LowReadyVirtOperatorsCount

11.5.27. LowVirtAPICount

11.5.28. LowVirtControllersCount

11.5.29. LowVirtOperatorCount

11.5.30. NetworkAddonsConfigNotReady

11.5.31. NoLeadingVirtOperator

11.5.32. NoReadyVirtController

11.5.33. NoReadyVirtOperator

11.5.34. OrphanedVirtualMachineInstances

11.5.35. OutdatedVirtualMachineInstanceWorkloads

11.5.36. SingleStackIPv6Unsupported

11.5.37. SSPCommonTemplatesModificationReverted

11.5.38. SSPDown

11.5.39. SSPFailingToReconcile

11.5.40. SSPHighRateRejectedVms

11.5.41. SSPTemplateValidatorDown

11.5.42. SSPOperatorDown

11.5.43. UnsupportedHCOModification

11.5.44. VirtAPIDown

11.5.45. VirtApiRESTErrorsBurst

11.5.46. VirtApiRESTErrorsHigh

11.5.47. VirtControllerDown

11.5.48. VirtControllerRESTErrorsBurst

11.5.49. VirtControllerRESTErrorsHigh

11.5.50. VirtHandlerDaemonSetRolloutFailing

11.5.51. VirtHandlerRESTErrorsBurst

11.5.52. VirtHandlerRESTErrorsHigh

11.5.53. VirtOperatorDown

11.5.54. VirtOperatorRESTErrorsBurst

11.5.55. VirtOperatorRESTErrorsHigh

11.5.56. VirtualMachineCRCErrors

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

11.5.57. VMCannotBeEvicted

11.5.58. VMStorageClassWarning

Red Hat logoGithubRedditYoutubeTwitter

자세한 정보

평가판, 구매 및 판매

커뮤니티

Red Hat 문서 정보

Red Hat을 사용하는 고객은 신뢰할 수 있는 콘텐츠가 포함된 제품과 서비스를 통해 혁신하고 목표를 달성할 수 있습니다.

보다 포괄적 수용을 위한 오픈 소스 용어 교체

Red Hat은 코드, 문서, 웹 속성에서 문제가 있는 언어를 교체하기 위해 최선을 다하고 있습니다. 자세한 내용은 다음을 참조하세요.Red Hat 블로그.

Red Hat 소개

Red Hat은 기업이 핵심 데이터 센터에서 네트워크 에지에 이르기까지 플랫폼과 환경 전반에서 더 쉽게 작업할 수 있도록 강화된 솔루션을 제공합니다.

© 2024 Red Hat, Inc.