Use load-aware pod scheduling

Procedure

You can specify whether to enable load-aware pod scheduling by adding an annotation.

      annotations:
        alibabacloud.com/loadAwareScheduleEnabled: "true" # Enable load-aware pod scheduling. 

Use load-aware pod scheduling

The cluster used in this example contains three nodes. Each node has 4 CPU cores and 8 GiB of memory.

1. Use the following YAML file to create a Job for video transcoding:

   apiVersion: batch/v1
   kind: Job
   metadata:
     generateName: ffmpeg
     namespace: default
   spec:
     backoffLimit: 1
     parallelism: 1
     template:
       metadata:
       spec:
         containers:
         - image: registry.cn-hangzhou.aliyuncs.com/kubernetes-image-hub/ffmpeg:latest
           name: ffmpeg
           resources:
             requests:
               cpu: 1
               memory: 2Gi
             limits:
               cpu: 2
               memory: 4Gi
           command: ["/bin/sh"]
           args: ["-c","while true;do ffmpeg -i /root/mp4/statefulset.mp4 -c:v libx264 -crf 100 -threads 200 destination.flv -y;sleep 1;done"]
         restartPolicy: OnFailure

2. Run the following command to query the status and deployment of the pod:

   # The image size is large and it may require about 2 minutes to start the pod.
   kubectl get pods -o wide

   Expected output:

   NAME                READY   STATUS    RESTARTS   AGE    IP             NODE                   NOMINATED NODE   READINESS GATES
   ffmpegjjpv2-xcl6t   1/1     Running   0          2m6s   172.20.177.8   cn-beijing.10.0.0.46   <none>           <none>

   The preceding output indicates that the pod is running on the node cn-beijing.10.0.0.46.

3. Run the following command to view the loads on all of the cluster nodes:

   kubectl top node

   Expected output:

   NAME                   CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%
   cn-beijing.10.0.0.44   251m         6%     1531Mi          22%
   cn-beijing.10.0.0.45   352m         8%     1881Mi          27%
   cn-beijing.10.0.0.46   1802m        45%    1689Mi          25%

   The preceding output indicates that the load on the node cn-beijing.10.0.0.46 is the highest.

4. Use the following YAML file to create pods for which load-aware pod scheduling is disabled:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: nginx
     namespace: default
     labels:
       app: nginx
   spec:
     replicas: 6
     selector:
       matchLabels:
         app: nginx
     template:
       metadata:
         name: nginx
         labels:
           app: nginx
       spec:
         containers:
         - name: nginx
           image: nginx
           resources:
             limits:
               cpu: 500m
             requests:
               cpu: 500m

5. Run the following command to view details of the pods:

   kubectl get pods | grep nginx

   Expected output:

   nginx-6976ccb95-96xmf   1/1     Running   0          108s    172.20.177.10    cn-beijing.10.0.0.46   <none>           <none>
   nginx-6976ccb95-b6xtp   1/1     Running   0          108s    172.20.177.70    cn-beijing.10.0.0.44   <none>           <none>
   nginx-6976ccb95-hmqcw   1/1     Running   0          108s    172.20.177.69    cn-beijing.10.0.0.44   <none>           <none>
   nginx-6976ccb95-l2gnn   1/1     Running   0          108s    172.20.177.9     cn-beijing.10.0.0.46   <none>           <none>
   nginx-6976ccb95-rzsfc   1/1     Running   0          108s    172.20.176.200   cn-beijing.10.0.0.45   <none>           <none>
   nginx-6976ccb95-shv52   1/1     Running   0          108s    172.20.177.71    cn-beijing.10.0.0.44   <none>           <none>

   The preceding output indicates that the pods are evenly scheduled to the nodes in the cluster. In this scenario, load-aware pod scheduling is disabled for the pods, and the cluster cannot monitor the loads on the nodes.

6. Run the following command to delete the pods for which load-aware pod scheduling is disabled:

   kubectl delete deployment nginx

7. Use the following YAML file to create pods that have load-aware pod scheduling enabled:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: nginx
     namespace: default
     labels:
       app: nginx
   spec:
     replicas: 6
     selector:
       matchLabels:
         app: nginx
     template:
       metadata:
         name: nginx
         labels:
           app: nginx
         annotations:
           alibabacloud.com/loadAwareScheduleEnabled: "true" # Enable load-aware pod scheduling. 
       spec:
         containers:
         - name: nginx
           image: nginx
           resources:
             limits:
               cpu: 500m
             requests:
               cpu: 500m

8. Run the following command to view details of the pods:

   kubectl get pods | grep nginx

   Expected output:

   nginx-7759585d4c-hfblh   1/1     Running   0          72s     172.20.177.74    cn-beijing.10.0.0.44   <none>           <none>
   nginx-7759585d4c-hzzt9   1/1     Running   0          72s     172.20.176.203   cn-beijing.10.0.0.45   <none>           <none>
   nginx-7759585d4c-m4gmm   1/1     Running   0          72s     172.20.177.72    cn-beijing.10.0.0.44   <none>           <none>
   nginx-7759585d4c-v7wfb   1/1     Running   0          72s     172.20.176.201   cn-beijing.10.0.0.45   <none>           <none>
   nginx-7759585d4c-wx59r   1/1     Running   0          72s     172.20.177.73    cn-beijing.10.0.0.44   <none>           <none>
   nginx-7759585d4c-xbwh6   1/1     Running   0          72s     172.20.176.202   cn-beijing.10.0.0.45   <none>           <none>

   The preceding output indicates that after load-aware pod scheduling is enabled for the pods, the cluster can monitor the loads on the nodes and use the scheduling policy to schedule the pods to nodes other than the node cn-beijing.10.0.0.46.