AHPA for predictive scaling

Step 1: Install Application Intelligence Controller

1. Log on to the ACK console.
2. In the left-side navigation pane of the ACK console, click Clusters.
3. On the Clusters page, find the cluster that you want to manage and click the name of the cluster or click Details in the Actions column. The details page of the cluster appears.
4. In the left-side navigation pane of the details page, choose Operations > Add-ons.
5. On the Add-ons page, click the Others tab, find Application Intelligence Controller, and then click Install.
6. In the Install Application Intelligence Controller message, click OK.

Step 2: Add ARMS Prometheus as a data source

1. Log on to the ARMS console .
2. In the left-side navigation pane, choose Prometheus Monitoring > Prometheus Instances.
3. In the upper-left corner of the Prometheus Monitoring page, select the region in which your Prometheus instances are deployed. Then, click the name of a Prometheus instance whose Instance Type is Prometheus for Container Service. The details page of the Prometheus instance appears.
4. In the left-side navigation pane of the instance details page, click Settings and copy the public endpoint in the HTTP API Address section.
5. In the HTTP API Address section, click Generate Token to generate a token. The token is used to pass the authentication when you access the Prometheus instance.
6. Create a ConfigMap named application-intelligence.yaml based on the following content and specify the public endpoint of the Prometheus instance:

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

   Descriptions of parameters:

   • armsUrl: Specify the public endpoint of the Prometheus instance that you copied in Step 4.
   • token: Specify the token that was generated in Step 5.
7. Run the following command to deploy the ConfigMap:

   kubectl apply -f application-intelligence.yaml

Step 3: Configure AHPA

1. Create an AHPA policy 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: php-apache
     maxReplicas: 100
     minReplicas: 2
     prediction:
       quantile: 95
       scaleUpForward: 180
     instanceBounds:
     - startTime: "2021-12-16 00:00:00"
       endTime: "2022-12-16 24:00:00"
       bounds:
       - cron: "* 0-8 ? * MON-FRI"
         maxReplicas: 4
         minReplicas: 15
       - cron: "* 8-15 ? * MON-FRI"
         maxReplicas: 10
         minReplicas: 15
       - cron: "* 15-24 ? * MON-FRI"
         maxReplicas: 15
         minReplicas: 20

   The following table describes some parameters that are specified in the preceding code block.

   Parameter	Description
   scaleTargetRef	Required. The Deployment for which you want to configure predictive scaling.
   metrics	Required. The metrics based on which predictive scaling is performed. You can specify CPU and memory metrics.
   averageUtilization	Required. The threshold that is used to trigger scaling activities. For example, a value of 40 indicates that the CPU or memory usage threshold is set to 40%.
   scaleStrategy	Optional. The scaling mode. Valid values: auto and observer. Default value: observer. auto: AHPA automatically performs scaling activities. observer: AHPA observes the resource usage but does not perform scaling activities. You can use the observer mode to check whether AHPA works as expected.
   maxReplicas	Required. The maximum number of pods that can be provisioned for the application.
   minReplicas	Required. The minimum number of pods that must be provisioned for the application.
   instanceBounds	Optional. The duration of a scaling activity. startTime: the start time of a scaling activity. endTime: the end time of a scaling activity.
   cron	Optional. This parameter is used to create a scheduled scaling job. The cron expression contains five fields that are separated by space characters. These fields specify a schedule. For example, - 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 expression.

   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 *.
   • Descriptions of 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

Verify the AHPA policy

In this section, an AHPA policy that uses the observer scaling mode is used as an example to check whether AHPA works as expected.

1. Run the following command to obtain the observer.html file. The file contains the AHPA prediction results that are compared with the HPA scaling results.

   kubectl get --raw '/apis/metrics.alibabacloud.com/v1beta1/namespaces/default/predictionsobserver/ahpa-demo'jq -r '.content' base64 -d > observer.html

2. Open the observer.html file and check the details. The following figures show the AHPA prediction results that are compared with the HPA scaling results based on the CPU usage.
   • Predict CPU Observer: The actual CPU usage based on HPA is represented by a blue line. The CPU usage predicted by AHPA is represented by a green line. The predicted CPU usage is higher than the actual CPU usage.
   • Predict POD Observer: The actual number of pods that are provisioned by HPA is represented by a blue line. The number of pods that are predicted by AHPA is represented by a green line. The predicted number of pods is less than the actual number of pods. You can set the scaling mode to auto and configure other settings based on the predicted number of pods. This way, AHPA can save pod resources.

   The results show that AHPA can use predictive scaling to handle fluctuating workloads as expected. After you confirm the prediction results, you can set the scaling mode to auto, which allows AHPA to automatically scale pods.