이 콘텐츠는 선택한 언어로 제공되지 않습니다.
Chapter 42. Analyzing Cluster Capacity
42.1. Overview
As a cluster administrator, you can use the cluster capacity tool to view the number of pods that can be scheduled to increase the current resources before they become exhausted, and to ensure any future pods can be scheduled. This capacity comes from an individual node host in a cluster, and includes CPU, memory, disk space, and others.
The cluster capacity tool simulates a sequence of scheduling decisions to determine how many instances of an input pod can be scheduled on the cluster before it is exhausted of resources to provide a more accurate estimation.
The remaining allocatable capacity is a rough estimation, because it does not count all of the resources being distributed among nodes. It analyzes only the remaining resources and estimates the available capacity that is still consumable in terms of a number of instances of a pod with given requirements that can be scheduled in a cluster.
Also, pods might only have scheduling support on particular sets of nodes based on its selection and affinity criteria. As a result, the estimation of which remaining pods a cluster can schedule can be difficult.
You can run the cluster capacity analysis tool as a stand-alone utility from the command line, or as a job in a pod inside an OpenShift Container Platform cluster. Running it as job inside of a pod enables you to run it multiple times without intervention.
42.2. Running Cluster Capacity Analysis on the Command Line
To run the tool on the command line:
$ cluster-capacity --kubeconfig <path-to-kubeconfig> \ --podspec <path-to-pod-spec>
The --kubeconfig
option indicates your Kubernetes configuration file, and the --podspec
option indicates a sample pod specification file, which the tool uses for estimating resource usage. The podspec
specifies its resource requirements as limits
or requests
. The cluster capacity tool takes the pod’s resource requirements into account for its estimation analysis.
An example of the pod specification input is:
apiVersion: v1 kind: Pod metadata: name: small-pod labels: app: guestbook tier: frontend spec: containers: - name: php-redis image: gcr.io/google-samples/gb-frontend:v4 imagePullPolicy: Always resources: limits: cpu: 150m memory: 100Mi requests: cpu: 150m memory: 100Mi
You can also add the --verbose
option to output a detailed description of how many pods can be scheduled on each node in the cluster:
$ cluster-capacity --kubeconfig <path-to-kubeconfig> \ --podspec <path-to-pod-spec> --verbose
The output will look similar to the following:
small-pod pod requirements: - CPU: 150m - Memory: 100Mi The cluster can schedule 52 instance(s) of the pod small-pod. Termination reason: Unschedulable: No nodes are available that match all of the following predicates:: Insufficient cpu (2). Pod distribution among nodes: small-pod - 192.168.124.214: 26 instance(s) - 192.168.124.120: 26 instance(s)
In the above example, the number of estimated pods that can be scheduled onto the cluster is 52.
42.3. Running Cluster Capacity as a Job Inside of a Pod
Running the cluster capacity tool as a job inside of a pod has the advantage of being able to be run multiple times without needing user intervention. Running the cluster capacity tool as a job involves using a ConfigMap
.
Create the cluster role:
$ cat << EOF| oc create -f - kind: ClusterRole apiVersion: v1 metadata: name: cluster-capacity-role rules: - apiGroups: [""] resources: ["pods", "nodes", "persistentvolumeclaims", "persistentvolumes", "services"] verbs: ["get", "watch", "list"] EOF
Create the service account:
$ oc create sa cluster-capacity-sa
Add the role to the service account:
$ oc adm policy add-cluster-role-to-user cluster-capacity-role \ system:serviceaccount:default:cluster-capacity-sa
Define and create the pod specification:
apiVersion: v1 kind: Pod metadata: name: small-pod labels: app: guestbook tier: frontend spec: containers: - name: php-redis image: gcr.io/google-samples/gb-frontend:v4 imagePullPolicy: Always resources: limits: cpu: 150m memory: 100Mi requests: cpu: 150m memory: 100Mi
The cluster capacity analysis is mounted in a volume using a
ConfigMap
namedcluster-capacity-configmap
to mount input pod spec filepod.yaml
into a volumetest-volume
at the path/test-pod
.If you haven’t created a
ConfigMap
, create one before creating the job:$ oc create configmap cluster-capacity-configmap \ --from-file=pod.yaml=pod.yaml
Create the job using the below example of a job specification file:
apiVersion: batch/v1 kind: Job metadata: name: cluster-capacity-job spec: parallelism: 1 completions: 1 template: metadata: name: cluster-capacity-pod spec: containers: - name: cluster-capacity image: openshift/origin-cluster-capacity imagePullPolicy: "Always" volumeMounts: - mountPath: /test-pod name: test-volume env: - name: CC_INCLUSTER 1 value: "true" command: - "/bin/sh" - "-ec" - | /bin/cluster-capacity --podspec=/test-pod/pod.yaml --verbose restartPolicy: "Never" serviceAccountName: cluster-capacity-sa volumes: - name: test-volume configMap: name: cluster-capacity-configmap
- 1
- A required environment variable letting the cluster capacity tool know that it is running inside a cluster as a pod.
Thepod.yaml
key of theConfigMap
is the same as the pod specification file name, though it is not required. By doing this, the input pod spec file can be accessed inside the pod as/test-pod/pod.yaml
.
Run the cluster capacity image as a job in a pod:
$ oc create -f cluster-capacity-job.yaml
Check the job logs to find the number of pods that can be scheduled in the cluster:
$ oc logs jobs/cluster-capacity-job small-pod pod requirements: - CPU: 150m - Memory: 100Mi The cluster can schedule 52 instance(s) of the pod small-pod. Termination reason: Unschedulable: No nodes are available that match all of the following predicates:: Insufficient cpu (2). Pod distribution among nodes: small-pod - 192.168.124.214: 26 instance(s) - 192.168.124.120: 26 instance(s)