Install the elastic workload component

In a Kubernetes cluster, you must deal with pod scheduling and pod lifecycle management for workloads. Pod scheduling and pod lifecycle management are based on the following operations:

• Change the pod scheduling policy when the number of pod replicas reaches a threshold.
• Prioritize specific pods for lifecycle management.

Create an application by using a Deployment based on the following template:

apiVersion: apps/v1 # for versions before 1.8.0 use apps/v1beta1
kind: Deployment
metadata:
  name: nginx-deployment-basic
  labels:
    app: nginx
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
    #  nodeSelector:
    #    env: test-team
      containers:
      - name: nginx
        image: nginx:1.7.9 
        ports:
        - containerPort: 80

Create an elastic workload for the application.

# Define an elastic workload. 
apiVersion: autoscaling.alibabacloud.com/v1beta1
kind: ElasticWorkload
metadata:
  name: elasticworkload-sample
spec:
  sourceTarget:
    name: nginx-deployment-basic
    kind: Deployment
    apiVersion: apps/v1
    min: 2
    max: 4
  replicas: 6
  elasticUnit:
  - name: virtual-kubelet
    labels:
      alibabacloud.com/eci: "true"
    # min: 0 You can specify different values for different elastic units. 
    # max: 10

Typically, the configurations of an elastic workload include the following parts:

1. SourceTarget: defines the type of the source workload and the range of the number of pod replicas.
2. elasticUnit: an array of fields that define the scheduling policies of an elastic unit. If you want to define multiple elastic units, set the fields in the same order as shown in the template for each elastic unit.

In this example, the number of pod replicas provisioned for the source workload ranges from 2 to 4. This indicates that pod replicas are scheduled to the source workload if the elastic workload has 2 to 4 pod replicas. If the number of pod replicas is greater than 4, extra pod replicas are scheduled to the virtual-kubelet elastic unit. Pod replicas are scheduled based on the scheduling policies of virtual-kubelet, such as the label, annotation, nodeSelector, affinity, and toleration parameters.

Run the following command to query the state of the elastic workload:

kubectl describe ew elasticworkload-sample   # same as kubectl get elasticworkload

Expected output:

Name:         elasticworkload-sample
Namespace:    default
Labels:       <none>
Annotations:  <none>
API Version:  autoscaling.alibabacloud.com/v1beta1
Kind:         ElasticWorkload
Metadata:
  Creation Timestamp:  2021-04-30T06:58:03Z
  Generation:          2
  Resource Version:  1126368056
  Self Link:         /apis/autoscaling.alibabacloud.com/v1beta1/namespaces/default/elasticworkloads/elasticworkload-sample
  UID:               a465de9e-1253-4cbe-8cd5-98393393e990
Spec:
  Elastic Unit:
    Labels:
      alibabacloud.com/eci:  true
    Name:                    virtual-kubelet
  Replicas:                  6
  Source Target:
    API Version:  apps/v1
    Kind:         Deployment
    Max:          4
    Min:          2
    Name:         nginx-deployment-basic
Status:
  Elastic Units Status:
    Desired Replicas:  2
    Name:              nginx-deployment-basic-unit-virtual-kubelet
    Update Timestamp:  2021-04-30T06:58:03Z
  Replicas:            6
  Selector:            app=nginx
  Source Target:
    API Version:       apps/v1
    Desired Replicas:  4
    Kind:              Deployment
    Name:              nginx-deployment-basic
    Update Timestamp:  2021-04-30T06:58:03Z
Events:
  Type    Reason                 Age                  From             Message
  ----    ------                 ----                 ----             -------
  Normal  UnitCreation           110s                 ElasticWorkload  ElasticWorkloadUnit nginx-deployment-basic-unit-virtual-kubelet created
  Normal  ElasticWorkloadUpdate  110s (x2 over 110s)  ElasticWorkload  ElasticWorkload update
  Normal  UnitUpdate             110s                 ElasticWorkload  ElasticWorkloadUnit virtual-kubelet has been updated

Run the following command to query the states of the pods:

kubectl get pod -o wide

Expected output:

NAME                                                           READY   STATUS    RESTARTS   AGE   IP              NODE                            NOMINATED NODE   READINESS GATES
nginx-deployment-basic-769f84b5cf-hdmlw                        1/1     Running   0          46m   172.26.240.69   cn-hangzhou.10.1.84.111         <none>           <none>
nginx-deployment-basic-769f84b5cf-lmd99                        1/1     Running   0          46m   172.26.240.6    cn-hangzhou.10.1.84.112         <none>           <none>
nginx-deployment-basic-769f84b5cf-nbp5c                        1/1     Running   0          30m   172.26.240.75   cn-hangzhou.10.1.84.111         <none>           <none>
nginx-deployment-basic-769f84b5cf-scj68                        1/1     Running   0          30m   172.26.240.11   cn-hangzhou.10.1.84.112         <none>           <none>
nginx-deployment-basic-unit-virtual-kubelet-594f86b5c9-8z876   1/1     Running   0          10m   10.1.84.119     virtual-kubelet-cn-hangzhou-i   <none>           <none>
nginx-deployment-basic-unit-virtual-kubelet-594f86b5c9-drxvq   1/1     Running   0          10m   10.1.84.118     virtual-kubelet-cn-hangzhou-i   <none>           <none>

You can also use HPA to scale an elastic workload, as shown in the preceding figure. If an elastic workload is scaled by HPA, the elastic workload adjusts the number of pod replicas scheduled to each elastic unit. For example, if HPA reduces the number of pod replicas in the elastic workload from 6 to 4, elastic units will scale in and the pod replicas of elastic units are reduced in priority.

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: elastic-workload-demo
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: autoscaling.alibabacloud.com/v1beta1
    kind: ElasticWorkload
    name: elasticworkload-sample
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50