Chapter 13. Monitoring 13.1. 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. 13.2. 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. 13.2.1. Prerequisites For guest memory swapping queries to return data, memory swapping must be enabled on the virtual guests. 13.2.2. Querying metrics for all projects with the Red Hat OpenShift Service on AWS web console You can use the Red Hat OpenShift Service on AWS metrics query browser 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 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. The Metrics UI includes predefined queries, for example, CPU, memory, bandwidth, or network packet for all projects. You can also run custom Prometheus Query Language (PromQL) queries. 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 In the Administrator perspective of the Red Hat OpenShift Service on AWS web console, click Observe and go to the Metrics tab. To add one or more queries, perform any of the following actions: Option Description Select an existing query. From the Select query drop-down list, select an existing query. 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. Use the keyboard arrows to select one of these suggested items and then press Enter to add the item to your expression. Move your mouse pointer over a suggested item to view a brief description of that item. Add multiple queries. Click Add query . Duplicate an existing query. Click the options menu to the query, then choose Duplicate query . Disable a query from being run. Click the options menu to the query and choose Disable query . To run queries that you created, click Run queries . The metrics from the queries are visualized on the plot. If a query is invalid, the UI shows an error message. Note When drawing time series graphs, queries that operate on large amounts of data might time out or overload the browser. To avoid this, click Hide graph and calibrate your query by using only the metrics table. Then, after finding a feasible query, enable the plot to draw the graphs. By default, the query table shows an expanded view that lists every metric and its current value. Click the ˅ down arrowhead to minimize the expanded view for a query. Optional: Save the page URL to use this set of queries again in the future. Explore the visualized metrics. Initially, all metrics from all enabled queries are shown on the plot. Select which metrics are shown by performing any of the following actions: 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. Perform one of the following actions: Visually select the time range by clicking and dragging on the plot horizontally. Use the menu to select the time range. Reset the time range. Click Reset zoom . Display outputs for all queries at a specific point in time. Hover over the plot at the point you are interested in. The query outputs appear in a pop-up box. Hide the plot. Click Hide graph . 13.2.3. Querying metrics for user-defined projects with the Red Hat OpenShift Service on AWS web console You can use the Red Hat OpenShift Service on AWS metrics query browser to run Prometheus Query Language (PromQL) queries to examine metrics visualized on a plot. This functionality provides information about any user-defined workloads that you are monitoring. 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. The Metrics UI includes predefined queries, for example, CPU, memory, bandwidth, or network packet. These queries are restricted to the selected project. You can also run custom Prometheus Query Language (PromQL) queries 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 In the Developer perspective of the Red Hat OpenShift Service on AWS web console, click Observe and go to the Metrics tab. Select the project that you want to view metrics for from the Project: list. To add one or more queries, perform any of the following actions: Option Description Select an existing query. From the Select query drop-down list, select an existing query. 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. Use the keyboard arrows to select one of these suggested items and then press Enter to add the item to your expression. Move your mouse pointer over a suggested item to view a brief description of that item. Add multiple queries. Click Add query . Duplicate an existing query. Click the options menu to the query, then choose Duplicate query . Disable a query from being run. Click the options menu to the query and choose Disable query . To run queries that you created, click Run queries . The metrics from the queries are visualized on the plot. If a query is invalid, the UI shows an error message. Note When drawing time series graphs, queries that operate on large amounts of data might time out or overload the browser. To avoid this, click Hide graph and calibrate your query by using only the metrics table. Then, after finding a feasible query, enable the plot to draw the graphs. By default, the query table shows an expanded view that lists every metric and its current value. Click the ˅ down arrowhead to minimize the expanded view for a query. Optional: Save the page URL to use this set of queries again in the future. Explore the visualized metrics. Initially, all metrics from all enabled queries are shown on the plot. Select which metrics are shown by performing any of the following actions: 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. Perform one of the following actions: Visually select the time range by clicking and dragging on the plot horizontally. Use the menu to select the time range. Reset the time range. Click Reset zoom . Display outputs for all queries at a specific point in time. Hover over the plot at the point you are interested in. The query outputs appear in a pop-up box. Hide the plot. Click Hide graph . 13.2.4. 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. 13.2.4.1. 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. 13.2.4.2. Storage metrics 13.2.4.2.1. 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. 13.2.4.2.2. 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. 13.2.4.2.3. 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. 13.2.4.3. 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. 13.2.4.4. 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. 13.2.5. Additional resources Monitoring overview Querying Prometheus Prometheus query examples 13.3. 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. 13.3.1. 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 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: USD oc create -f node-exporter-service.yaml 13.3.2. 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 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. USD 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. USD 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. USD sudo systemctl enable node_exporter.service USD sudo systemctl start node_exporter.service Verification Verify that the node-exporter agent is reporting metrics from the virtual machine. USD 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 13.3.3. 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 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. 13.3.3.1. 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 Obtain the HTTP service endpoint by specifying the namespace for the service: USD oc get service -n <namespace> <node-exporter-service> To list all available metrics for the node-exporter service, query the metrics resource. USD curl http://<172.30.226.162:9100>/metrics | grep -vE "^#|^USD" 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 13.3.4. 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 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. USD oc create -f node-exporter-metrics-monitor.yaml 13.3.4.1. 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 Expose the node-exporter service. USD oc expose service -n <namespace> <node_exporter_service_name> Obtain the FQDN (Fully Qualified Domain Name) for the route. USD 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. USD 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 13.3.5. Additional resources Creating and using config maps Controlling virtual machine states 13.4. Virtual machine health checks You can configure virtual machine (VM) health checks by defining readiness and liveness probes in the VirtualMachine resource. 13.4.1. 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. 13.4.1.1. Defining an HTTP readiness probe Define an HTTP readiness probe by setting the spec.readinessProbe.httpGet field of the virtual machine (VM) configuration. Procedure 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: USD oc create -f <file_name>.yaml 13.4.1.2. Defining a TCP readiness probe Define a TCP readiness probe by setting the spec.readinessProbe.tcpSocket field of the virtual machine (VM) configuration. Procedure 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: USD oc create -f <file_name>.yaml 13.4.1.3. 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 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: USD oc create -f <file_name>.yaml 13.4.2. 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.runStrategy is not set to manual , 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. 13.4.2.1. 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 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: runStrategy: Halted 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: USD 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: USD 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 13.4.2.2. 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 13.5. 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. 13.5.1. CDIDataImportCronOutdated View the runbook for the CDIDataImportCronOutdated alert. 13.5.2. CDIDataVolumeUnusualRestartCount View the runbook for the CDIDataVolumeUnusualRestartCount alert. 13.5.3. CDIDefaultStorageClassDegraded View the runbook for the CDIDefaultStorageClassDegraded alert. 13.5.4. CDIMultipleDefaultVirtStorageClasses View the runbook for the CDIMultipleDefaultVirtStorageClasses alert. 13.5.5. CDINoDefaultStorageClass View the runbook for the CDINoDefaultStorageClass alert. 13.5.6. CDINotReady View the runbook for the CDINotReady alert. 13.5.7. CDIOperatorDown View the runbook for the CDIOperatorDown alert. 13.5.8. CDIStorageProfilesIncomplete View the runbook for the CDIStorageProfilesIncomplete alert. 13.5.9. CnaoDown View the runbook for the CnaoDown alert. 13.5.10. CnaoNMstateMigration View the runbook for the CnaoNMstateMigration alert. 13.5.11. HCOInstallationIncomplete View the runbook for the HCOInstallationIncomplete alert. 13.5.12. HPPNotReady View the runbook for the HPPNotReady alert. 13.5.13. HPPOperatorDown View the runbook for the HPPOperatorDown alert. 13.5.14. HPPSharingPoolPathWithOS View the runbook for the HPPSharingPoolPathWithOS alert. 13.5.15. KubemacpoolDown View the runbook for the KubemacpoolDown alert. 13.5.16. KubeMacPoolDuplicateMacsFound View the runbook for the KubeMacPoolDuplicateMacsFound alert. 13.5.17. KubeVirtComponentExceedsRequestedCPU The KubeVirtComponentExceedsRequestedCPU alert is deprecated . 13.5.18. KubeVirtComponentExceedsRequestedMemory The KubeVirtComponentExceedsRequestedMemory alert is deprecated . 13.5.19. KubeVirtCRModified View the runbook for the KubeVirtCRModified alert. 13.5.20. KubeVirtDeprecatedAPIRequested View the runbook for the KubeVirtDeprecatedAPIRequested alert. 13.5.21. KubeVirtNoAvailableNodesToRunVMs View the runbook for the KubeVirtNoAvailableNodesToRunVMs alert. 13.5.22. KubevirtVmHighMemoryUsage View the runbook for the KubevirtVmHighMemoryUsage alert. 13.5.23. KubeVirtVMIExcessiveMigrations View the runbook for the KubeVirtVMIExcessiveMigrations alert. 13.5.24. LowKVMNodesCount View the runbook for the LowKVMNodesCount alert. 13.5.25. LowReadyVirtControllersCount View the runbook for the LowReadyVirtControllersCount alert. 13.5.26. LowReadyVirtOperatorsCount View the runbook for the LowReadyVirtOperatorsCount alert. 13.5.27. LowVirtAPICount View the runbook for the LowVirtAPICount alert. 13.5.28. LowVirtControllersCount View the runbook for the LowVirtControllersCount alert. 13.5.29. LowVirtOperatorCount View the runbook for the LowVirtOperatorCount alert. 13.5.30. NetworkAddonsConfigNotReady View the runbook for the NetworkAddonsConfigNotReady alert. 13.5.31. NoLeadingVirtOperator View the runbook for the NoLeadingVirtOperator alert. 13.5.32. NoReadyVirtController View the runbook for the NoReadyVirtController alert. 13.5.33. NoReadyVirtOperator View the runbook for the NoReadyVirtOperator alert. 13.5.34. OrphanedVirtualMachineInstances View the runbook for the OrphanedVirtualMachineInstances alert. 13.5.35. OutdatedVirtualMachineInstanceWorkloads View the runbook for the OutdatedVirtualMachineInstanceWorkloads alert. 13.5.36. SingleStackIPv6Unsupported View the runbook for the SingleStackIPv6Unsupported alert. 13.5.37. SSPCommonTemplatesModificationReverted View the runbook for the SSPCommonTemplatesModificationReverted alert. 13.5.38. SSPDown View the runbook for the SSPDown alert. 13.5.39. SSPFailingToReconcile View the runbook for the SSPFailingToReconcile alert. 13.5.40. SSPHighRateRejectedVms View the runbook for the SSPHighRateRejectedVms alert. 13.5.41. SSPTemplateValidatorDown View the runbook for the SSPTemplateValidatorDown alert. 13.5.42. SSPOperatorDown View the runbook for the SSPOperatorDown alert. 13.5.43. UnsupportedHCOModification View the runbook for the UnsupportedHCOModification alert. 13.5.44. VirtAPIDown View the runbook for the VirtAPIDown alert. 13.5.45. VirtApiRESTErrorsBurst View the runbook for the VirtApiRESTErrorsBurst alert. 13.5.46. VirtApiRESTErrorsHigh View the runbook for the VirtApiRESTErrorsHigh alert. 13.5.47. VirtControllerDown View the runbook for the VirtControllerDown alert. 13.5.48. VirtControllerRESTErrorsBurst View the runbook for the VirtControllerRESTErrorsBurst alert. 13.5.49. VirtControllerRESTErrorsHigh View the runbook for the VirtControllerRESTErrorsHigh alert. 13.5.50. VirtHandlerDaemonSetRolloutFailing View the runbook for the VirtHandlerDaemonSetRolloutFailing alert. 13.5.51. VirtHandlerRESTErrorsBurst View the runbook for the VirtHandlerRESTErrorsBurst alert. 13.5.52. VirtHandlerRESTErrorsHigh View the runbook for the VirtHandlerRESTErrorsHigh alert. 13.5.53. VirtOperatorDown View the runbook for the VirtOperatorDown alert. 13.5.54. VirtOperatorRESTErrorsBurst View the runbook for the VirtOperatorRESTErrorsBurst alert. 13.5.55. VirtOperatorRESTErrorsHigh View the runbook for the VirtOperatorRESTErrorsHigh alert. 13.5.56. VirtualMachineCRCErrors The runbook for the VirtualMachineCRCErrors alert is deprecated because the alert was renamed to VMStorageClassWarning . View the runbook for the VMStorageClassWarning alert. 13.5.57. VMCannotBeEvicted View the runbook for the VMCannotBeEvicted alert. 13.5.58. VMStorageClassWarning View the runbook for the VMStorageClassWarning alert.

Commands:
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
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
kubevirt_vmsnapshot_disks_restored_from_source{vm_name="simple-vm", vm_namespace="default"} 1
kubevirt_vmsnapshot_disks_restored_from_source_bytes{vm_name="simple-vm", vm_namespace="default"} 1
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
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
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
oc create -f node-exporter-service.yaml
wget https://github.com/prometheus/node_exporter/releases/download/v1.3.1/node_exporter-1.3.1.linux-amd64.tar.gz
sudo tar xvf node_exporter-1.3.1.linux-amd64.tar.gz --directory /usr/bin --strip 1 "*/node_exporter"
[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
sudo systemctl enable node_exporter.service sudo systemctl start node_exporter.service
curl http://localhost:9100/metrics
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
spec: template: metadata: labels: monitor: metrics
oc get service -n <namespace> <node-exporter-service>
curl http://<172.30.226.162:9100>/metrics | grep -vE "^#|^USD"
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
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
oc create -f node-exporter-metrics-monitor.yaml
oc expose service -n <namespace> <node_exporter_service_name>
oc get route -o=custom-columns=NAME:.metadata.name,DNS:.spec.host
NAME DNS node-exporter-service node-exporter-service-dynamation.apps.cluster.example.org
curl -s http://node-exporter-service-dynamation.apps.cluster.example.org/metrics
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
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
oc create -f <file_name>.yaml
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
oc create -f <file_name>.yaml
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
oc create -f <file_name>.yaml
apiVersion: kubevirt.io/v1 kind: VirtualMachine metadata: labels: kubevirt.io/vm: vm2-rhel84-watchdog name: <vm-name> spec: runStrategy: Halted template: metadata: labels: kubevirt.io/vm: vm2-rhel84-watchdog spec: domain: devices: watchdog: name: <watchdog> i6300esb: action: "poweroff" 1
oc apply -f <file_name>.yaml
lspci | grep watchdog -i
echo c > /proc/sysrq-trigger
pkill -9 watchdog
yum install watchdog
#watchdog-device = /dev/watchdog
systemctl enable --now watchdog.service