Container Service for Kubernetes:Deploying AHPA

Step 1: Install AHPA Controller

1. Log on to the ACK console. In the left navigation pane, click Clusters.

2. On the Clusters page, click the name of your cluster. In the left navigation pane, click Components and Add-ons.

3. On the Add-ons page, locate the AHPA Controller component. Click Install on the component card and follow the on-screen instructions to complete the installation.

Step 2: Configure Prometheus data source

1. Log on to the ARMS console.

2. In the left navigation pane, choose Managed Service for Prometheus > Instances.

3. At the top of the Instances page, select the region of your Prometheus instance, and then click the name of the instance, which is the same as your ACK cluster name.

4. On the Settings page, in the HTTP API Address (Grafana Read Address) section, record the values of the following fields:

   • (Optional) If token-based authentication is enabled, record the access token.

   • View and record the Internal Network endpoint (Prometheus URL).

5. Set the Prometheus query URL in your ACK cluster.

   1. Create a file named application-intelligence.yaml with the following content.

      • prometheusUrl: The endpoint for Managed Service for Prometheus.

      • token: The access token for Prometheus.

      apiVersion: v1
      kind: ConfigMap
      metadata:
        name: application-intelligence
        namespace: kube-system
      data:
        prometheusUrl: "http://cn-hangzhou-intranet.arms.aliyuncs.com:9443/api/v1/prometheus/da9d7dece901db4c9fc7f5b9c40****/158120454317****/cc6df477a982145d986e3f79c985a****/cn-hangzhou"
        token: "eyJhxxxxx"

      Note

      To view the AHPA dashboard in Managed Service for Prometheus, you must also configure the following fields in this ConfigMap:

      • prometheus_writer_url: The internal Remote Write endpoint.

      • prometheus_writer_ak: The AccessKey ID of your Alibaba Cloud account.

      • prometheus_writer_sk: The AccessKey secret of your Alibaba Cloud account.

      For more information, see Enable the Prometheus dashboard for AHPA.

   2. Run the following command to deploy application-intelligence.

      kubectl apply -f application-intelligence.yaml

Step 3: Deploy a test service

The test service includes a Deployment named fib-deployment, a Service named fib-svc, and a load generator named fib-loader to simulate traffic fluctuations. You will also deploy a HorizontalPodAutoscaler (HPA) resource to establish a baseline for comparison with AHPA.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: fib-deployment
  namespace: default
  annotations:
    k8s.aliyun.com/eci-use-specs: "1-2Gi"
spec:
  replicas: 1
  selector:
    matchLabels:
      app: fib-deployment
  strategy:
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 25%
    type: RollingUpdate
  template:
    metadata:
      creationTimestamp: null
      labels:
        app: fib-deployment
    spec:
      containers:
      - image: registry.cn-huhehaote.aliyuncs.com/kubeway/knative-sample-fib-server:20200820-171837
        imagePullPolicy: IfNotPresent
        name: user-container
        ports:
        - containerPort: 8080
          name: user-port
          protocol: TCP
        resources:
          limits:
            cpu: "1"
            memory: 2000Mi
          requests:
            cpu: "1"
            memory: 2000Mi
---
apiVersion: v1
kind: Service
metadata:
  name: fib-svc
  namespace: default
spec:
  ports:
  - name: http
    port: 80
    protocol: TCP
    targetPort: 8080
  selector:
    app: fib-deployment
  sessionAffinity: None
  type: ClusterIP
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: fib-loader
  namespace: default
spec:
  progressDeadlineSeconds: 600
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: fib-loader
  strategy:
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 25%
    type: RollingUpdate
  template:
    metadata:
      creationTimestamp: null
      labels:
        app: fib-loader
    spec:
      containers:
      - args:
        - -c
        - |
          /ko-app/fib-loader --service-url="http://fib-svc.${NAMESPACE}?size=35&interval=0" --save-path=/tmp/fib-loader-chart.html
        command:
        - sh
        env:
        - name: NAMESPACE
          valueFrom:
            fieldRef:
              apiVersion: v1
              fieldPath: metadata.namespace
        image: registry.cn-huhehaote.aliyuncs.com/kubeway/knative-sample-fib-loader:20201126-110434
        imagePullPolicy: IfNotPresent
        name: loader
        ports:
        - containerPort: 8090
          name: chart
          protocol: TCP
        resources:
          limits:
            cpu: "8"
            memory: 16000Mi
          requests:
            cpu: "2"
            memory: 4000Mi
---
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: fib-hpa
  namespace: default
spec:
  maxReplicas: 50
  minReplicas: 1
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: fib-deployment
  targetCPUUtilizationPercentage: 50
---

Step 4: Deploy AHPA

To configure a scaling policy, create an AdvancedHorizontalPodAutoscaler resource.

1. Create a file named ahpa-demo.yaml with the following content.

   apiVersion: autoscaling.alibabacloud.com/v1beta1
   kind: AdvancedHorizontalPodAutoscaler
   metadata:
     name: ahpa-demo
   spec:
     scaleStrategy: observer
     metrics:
     - type: Resource
       resource:
         name: cpu
         target:
           type: Utilization
           averageUtilization: 40
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: fib-deployment 
     maxReplicas: 100
     minReplicas: 2
     stabilizationWindowSeconds: 300
     prediction:
       quantile: 95
       scaleUpForward: 180
     instanceBounds:
     - startTime: "2021-12-16 00:00:00"
       endTime: "2031-12-16 00:00:00"
       bounds:
       - cron: "* 0-8 ? * MON-FRI"
         maxReplicas: 15
         minReplicas: 4
       - cron: "* 9-15 ? * MON-FRI"
         maxReplicas: 15
         minReplicas: 10
       - cron: "* 16-23 ? * MON-FRI"
         maxReplicas: 20
         minReplicas: 15

   The following table describes some of the parameters.

   Parameter	Required	Description
   scaleTargetRef	Yes	Specifies the target Deployment.
   metrics	Yes	Configures the metrics for scaling. Supported metrics include CPU, GPU, memory, QPS, and RT.
   target	Yes	The target threshold. For example, averageUtilization: 40 sets the target CPU utilization to 40%.
   scaleStrategy	No	Specifies the scaling mode. The default value is observer. auto: AHPA performs scaling operations. observer: AHPA observes but does not perform scaling operations. You can use this mode to check if AHPA works as expected. proactive: Only proactive prediction takes effect. reactive: Only reactive scaling takes effect.
   maxReplicas	Yes	The maximum number of replicas to scale out to.
   minReplicas	Yes	The minimum number of replicas to scale in to.
   stabilizationWindowSeconds	No	The stabilization window for scaling operations. This prevents rapid fluctuations in the number of replicas. The default value is 300 seconds.
   prediction.quantile	Yes	The prediction quantile. A higher value results in a more conservative prediction. The value must be between 0 and 100. The recommended range is 90 to 99, with a default of 99.
   prediction.scaleUpForward	Yes	The time it takes for a new pod to become Ready. This is also known as the cold start time.
   instanceBounds	No	The boundaries for the number of replicas within specific time periods. startTime: The start time. endTime: The end time.
   instanceBounds.bounds.cron	No	Configures a scheduled task. A cron expression specifies a schedule using several space-separated fields to specify a set of times. For example, - cron: "* 0-8 ? * MON-FRI" specifies that the task runs every minute from 00:00 to 08:59 on Monday to Friday.

   The fields of a cron expression are described as follows. For more information, see Cron scheduled tasks.

   Field	Required	Allowed values	Special characters
   Minutes	Yes	0–59	* / , -
   Hours	Yes	0–23	* / , -
   Day of month	Yes	1–31	* / , - ?
   Month	Yes	1–12 or JAN–DEC	* / , -
   Day of week	Yes	0–6 or SUN–SAT	* / , - ?

   Note

   • The Month and Day of week fields are not case-sensitive. For example, SUN, Sun, and sun have the same effect.

   • If the Day of Week field is not configured, it defaults to *.

   • Special characters:

     • *: Matches all possible values in the field.

     • /: Specifies an increment for a value.

     • ,: Lists enumerated values.

     • -: Specifies a range.

     • ?: Indicates that no specific value is set. Use this in either the Day of month or Day of week field when the other is specified.

2. Run the following command to create the AHPA scaling policy.

   kubectl apply -f ahpa-demo.yaml

Step 5: View prediction results

To view the predictive scaling results of AHPA, you can enable the Prometheus dashboard for AHPA.

This example uses the observer scaling mode (observer mode) to compare the AHPA prediction results with the results from a standard HPA policy. The HPA results serve as a baseline for the application's actual resource requirements. This comparison helps you observe whether the AHPA predictions meet your expectations.

Related documents

• To use GPU metrics from Managed Service for Prometheus for AHPA predictive scaling, see Configure AHPA predictive scaling based on GPU metrics.

• To view the monitoring dashboards provided by Managed Service for Prometheus, see Enable the Prometheus dashboard for AHPA.