Deploy and use ack-kubernetes-elastic-workload in an ACK cluster

You must deal with pod scheduling and pod lifecycle management for workloads in a Kubernetes cluster. Pod scheduling and pod lifecycle management are mainly based on the following operations:

• Change the pod scheduling policy when the number of pod replicas reaches a certain threshold.
• Change the lifecycle to prioritize certain pods.

For example, 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:
      virtual-kubelet: "true"
    annotations:
      virtual-kubelet: "true"
    nodeSelector:
      type: "virtual-kubelet"
    tolerations:
    - key: "virtual-kubelet.io/provider"
      operator: "Exists"
    # min: 0 You can specify the maximum and minimum numbers of pod replicas in each elastic unit.
    # max: 10

Typically, an elastic workload is configured with two parts:

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

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

After you run the template for the elastic workload, you can run the following command to view the status of the elastic workload. The value of the Desired Replicas field in the Status section of the result indicates the number of pod replicas that are scheduled to the elastic unit.

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

Name:         elasticworkload-sample
Namespace:    default
Labels:       <none>
Annotations:  <none>
API Version:  autoscaling.alibabacloud.com/v1beta1
Kind:         ElasticWorkload
Metadata:
  Creation Timestamp:  2020-05-06T03:43:41Z
  Generation:          27
  Resource Version:    20635284
  Self Link:           /apis/autoscaling.alibabacloud.com/v1beta1/namespaces/default/elasticworkloads/elasticworkload-sample
  UID:                 0e9205ff-38b8-43b7-9076-ffa130f26ef4
Spec:
  Elastic Unit:
    Annotations:
      Virtual - Kubelet:  true
    Labels:
      Virtual - Kubelet:  true
    Name:                 demo
    Node Selector:
      Type:  virtual-kubelet
    Tolerations:
      Key:       virtual-kubelet.io/provider
      Operator:  Exists
  Replicas:      6
  Source Target:
    API Version:  apps/v1
    Kind:         Deployment
    Max:          2
    Min:          0
    Name:         nginx-deployment-basic
Status:
  Elastic Units Status:
    Desired Replicas:  4
    Name:              nginx-deployment-basic-unit-virtual-kubelet
    Update Timestamp:  2020-05-07T12:38:27Z
  Replicas:            6
  Selector:            app=nginx
  Source Target:
    API Version:       apps/v1
    Desired Replicas:  2
    Kind:              Deployment
    Name:              nginx-deployment-basic
    Update Timestamp:  2020-05-07T12:38:27Z
Events:                <none>

After you run the template for the elastic workload, you can run the following command to view the status of pods. The following result shows that new Deployments and pods are cloned based on the source workload. You can also find that the number of pod replicas in these Deployments complies with scheduling policy.

kubectl get pod -o wide

 NAME                                               READY   STATUS    RESTARTS   AGE    IP             NODE                     NOMINATED NODE   READINESS GATES
nginx-deployment-basic-7ff9955f89-djxwv            1/1     Running   0          138m   172.20. *.***   cn-hangzhou.10.0. *.***   <none>           <none>
nginx-deployment-basic-7ff9955f89-hrw2z            1/1     Running   0          138m   172.2*. *.**    cn-hangzhou.10.0. *.***   <none>           <none>
nginx-deployment-basic-unit-demo-8bb586568-4f8xt   1/1     Running   0          138m   10.1. **.**     virtual-node-eci-1       <none>           <none>
nginx-deployment-basic-unit-demo-8bb586568-bl5pd   1/1     Running   0          138m   10.1. **.**     virtual-node-eci-0       <none>           <none>
nginx-deployment-basic-unit-demo-8bb586568-ndbp8   1/1     Running   0          138m   10.1. **.**     virtual-node-eci-0       <none>           <none>
nginx-deployment-basic-unit-demo-8bb586568-vx9jx   1/1     Running   0          138m   10.1. **.**     virtual-node-eci-2       <none>           <none>

You can also use Horizontal Pod Autoscaler in an elastic workload as shown in the following example. If the number of pod replicas of an elastic workload changes, Horizontal Pod Autoscaler can dynamically adjust the number of pod replicas scheduled to each elastic unit. For example, if the number of the pod replicas changes 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