Container Compute Service:Deploy and use AHPA to predict resource demand

Background information

You can enable Managed Service for Prometheus for the AHPA controller to collect the historical metric data of your application. The AHPA controller makes scaling decisions based on the collected data. The AHPA controller uses machine learning algorithms to predict the number of pods that may be required by your business within the next 24 hours. The AHPA controller is suitable for scenarios where the workloads dynamically change. The AHPA controller provides proactive and passive prediction policies to ensure timely pod scaling and resource prefetching so that your business can withstand traffic spikes. This enhances the response speed, performance, and stability of your business and also helps reduce resource costs. For more information, see AHPA overview.

Step 1: Install the AHPA controller

1. Log on to the ACS console. In the left-side navigation pane, click Clusters.

2. On the Clusters page, find the cluster that you want to manage and click its ID. In the left-side navigation pane of the cluster details page, choose Operations > Add-ons.

3. On the Add-ons page, click the Others tab. Find AHPA Controller and click Install. Then, follow the on-screen instructions to install the AHPA controller.

Step 2: Add Prometheus as a data source for AHPA

1. Log on to the ARMS console.

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

3. In the upper part of the Instances page, select the region where your Prometheus instance is deployed. Select the Prometheus instance that is named after your ACS cluster. General-purpose is displayed in the Instance Type column for the Prometheus instance. Click Settings in the Actions column. In the HTTP API URL (Grafana Read URL) section, record the following information:

   • If access tokens are enabled, you must configure an access token for your cluster.

   • View and record the internal endpoint.

4. Specify the endpoint of the Prometheus instance in the cluster configurations.

   1. Create a file named application-intelligence.yaml and copy the following content to the file:

      • prometheusUrl: the endpoint of the Prometheus instance.

      • token: the access token of the Prometheus instance.

      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

      If you want to view the Prometheus metrics displayed on the AHPA dashboard, set the following parameters in the Configmap:

      • prometheus_writer_url: Specify the internal remote write endpoint of the Prometheus instance.

      • prometheus_writer_ak: Specify the AccessKey ID of the Alibaba Cloud account.

      • prometheus_writer_sk: Specify the AccessKey secret of the Alibaba Cloud account.

   2. Run the following command to create the application-intelligence ConfigMap:

      kubectl apply -f application-intelligence.yaml

5. Configure Managed Service for Prometheus to monitor AHPA.

   1. Log on to the ARMS console.

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

   3. In the top navigation bar, click Integrate Other Components to go to the Integration Center page. Type AHPA in the search box and click the search icon. Click the AHPA card after it appears.

   4. Configure Managed Service for Prometheus to monitor AHPA.

      1. On the ACK AHPA page, choose Select a Kubernetes cluster > Select Cluster. Select your ACS cluster from the drop-down list.

      2. Configure the parameters in the Configuration Information section based on the following table. Click OK.

         Parameter	Description
         Exporter Name	The name must be unique among the exporters used to collect monitoring data from AHPA.
         metrics collection interval (seconds)	The interval at which you want the service to collect monitoring data.

   5. After the Integration Status Check step is complete, click Integration Management. On the page that appears, you can find that Managed Service for Prometheus is enabled for AHPA.

Step 3: Deploy a test service

Deploy a test service that consists of a Deployment named fib-deployment and a Service named fib-svc. Deploy an application named fib-loader that is used to send requests to the test service to simulate traffic fluctuation. Then, deploy Horizontal Pod Autoscaler (HPA) to scale the test service. This way, you can compare the HPA scaling results with the AHPA prediction results.

1. Create a file named demo.yaml and copy the following content to the file:

   Show the YAML file content

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: fib-deployment
     namespace: default
   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
   ---

2. Run the following command to deploy a test service:

   kubectl apply -f demo.yaml

Step 4: Create an AHPA policy in a declarative manner

To configure an AHPA policy, perform the following steps:

1. Create a file named ahpa-demo.yaml and copy the following content to the file:

   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 that are specified in the preceding code block.

   Parameter	Required	Description
   scaleTargetRef	Yes	The Deployment that you want to manage.
   metrics	Yes	The metrics based on which the AHPA policy is implemented. The following metrics are supported: CPU, GPU, memory, queries per second (QPS), and response time (RT).
   target	Yes	The scaling threshold. If you specify averageUtilization: 40, the scaling threshold of CPU utilization is 40%.
   scaleStrategy	No	The scaling mode of AHPA. Default value: observer. Valid values: auto: AHPA automatically performs scaling activities. observer: AHPA observes the resource usage and does not perform scaling activities. You can use the observer mode to check whether AHPA works as expected. scalingUpOnly: AHPA performs only scale-out operations and does not perform scale-in operations. proactive: Only active predictions take effect. reactive: Only passive predictions take effect.
   maxReplicas	Yes	The maximum number of replicated pods that can be provisioned for the application.
   minReplicas	Yes	The minimum number of replicated pods that must run for the application.
   stabilizationWindowSeconds	No	The cooldown period of scale-in activities. Default value: 300. Unit: seconds.
   prediction.quantile	Yes	A quantile that indicates the probability of the actual metric value not reaching the scaling threshold. A larger value indicates a higher probability. Valid values: 0 to 1. Default value: 0.99. The value is accurate to two decimal places. We recommend that you set the parameter to a value from 0.90 to 0.99.
   prediction. scaleUpForward	Yes	The duration of a cold start. The value represents the time period from the point in time when a pod is created to the point in time when the pod enters the Ready state.
   instanceBounds	No	The time period during which the number of pods is limited by the maximum number of pods and the minimum number of pods defined by AHPA. startTime: the start time of the scaling operation. endTime: the end time of the scaling operation.
   instanceBounds.bounds.cron	No	This parameter is used to create a scheduled scaling job. A cron expression consists of five fields that are separated by spaces. For example, the cron expression - cron: "* 0-8 ? * MON-FRI" specifies the time period from 00:00:00 to 08:00:00 on Monday to Friday each month.

   The following table describes the fields that are contained in a cron expression. For more information, see Cron expressions.

   Field	Required	Valid value	Valid special character
   Minutes	Yes	0~59	* / , -
   Hours	Yes	0~23	* / , -
   Day of Month	Yes	1~31	* / , – ?
   Month	Yes	1 to 12 or JAN to DEC	* / , -
   Day of Week	No	0 to 6 or SUN to SAT	* / , – ?

   Note

   • The Month and Day of Week fields are not case-sensitive. For example, you can specify SUN, Sun, or sun.

   • The default value of the Day of Week field is *.

   • The following list describes the special characters:

     • *: specifies an arbitrary value.

     • /: specifies an increment.

     • ,: separates a list of values.

     • -: specifies a range.

     • ?: specifies a placeholder.

2. Run the following command to apply the AHPA policy:

   kubectl apply -f ahpa-demo.yaml

Step 5: View the prediction results

On the Integration Management page, click the name of your cluster on the Container Service tab. Select ACK AHPA in the Addon Type section. Click the Dashboards tab. Then, click ahpa-dashboard. On the page that appears, you can view data in the AHPA dashboard.

In this example, the AHPA policy uses the observer scaling mode. The following figure compares the AHPA prediction results with the HPA scaling results. You can use the comparison results to check whether the AHPA prediction results meet the expectations.