Container Service for Kubernetes:Install ack-kubernetes-elastic-workload (discontinued maintenance)

You must resolve the following issues when you use Kubernetes to schedule pods and manage the lifecycle of pods:

• How to modify the pod scheduling policy when the number of replicated pods reaches a threshold.

• How to prioritize specific pods when Kubernetes manages the lifecycle of pods.

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
        resources:
          limits:
            cpu: "500m"

Create an elastic workload for the Deployment.

# 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 the maximum and minimum numbers of replicated pods for each elastic unit.
  # max: 10

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

1. SourceTarget: defines the type of the source workload, and the maximum and minimum numbers of replicated pods.

2. elasticUnit: an array that defines the scheduling policy for an elastic unit. To define scheduling policies for multiple elastic units, specify related parameters in the order shown in the template.

In this example, the number of replicated pods provisioned for the source workload ranges from 2 to 4. If the elastic workload has 2 to 4 replicated pods, all the pods are scheduled to the source workload. If the elastic workload contains more than 4 pods, the remaining pods are scheduled to the virtual-kubelet elastic unit. Replicated pods are scheduled based on the scheduling policy of virtual-kubelet, such as the label, annotation, nodeSelector, affinity, and toleration parameters.

Run the following command to query the status 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 status 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 together with ack-kubernetes-elastic-workload to scale an elastic workload, as shown in the preceding figure. If a Kubernetes workload is scaled by HPA, ack-kubernetes-elastic-workload adjusts the number of replicated pods scheduled to each elastic unit. For example, if HPA reduces the number of replicated pods in an elastic workload from 6 to 4, ack-kubernetes-elastic-workload preferably reduces the replicated pods of elastic units instead of reducing the number of replicated pods in the source workload.

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