Obtain recommendations on resource specifications

Limits

The following table describes the versions that are required for system components.

Procedure

1. Use the following YAML template to enable recommendations on resource specifications for your workloads.
   You can use the RecommendationProfile CustomResourceDefinition (CRD) to generate resource profiles for your workloads and provide recommendations on resource specifications for containers in your workloads. You can specify the namespaces and workload types to which a RecommendationProfile CRD is applied.

   apiVersion: autoscaling.alibabacloud.com/v1alpha1
   kind: RecommendationProfile
   metadata:
     # The name of the RecommendationProfile CRD. If you want to create a non-namespaced RecommendationProfile CRD, do not specify a namespace. 
     name: profile-demo
   spec:
     # The workload types for which you want to enable resource profiling. 
     controllerKind:
     - Deployment
     # The namespaces for which you want to enable resource profiling. 
     enabledNamespaces:
     - recommender-demo

   The following table describes the parameters in the YAML template.

   Parameter	Data type	Description
   metadata.name	String	The name of the resource object. If you want to create a non-namespaced RecommendationProfile CRD, do not specify a namespace.
   spec.controllerKind	String	The workload types for which you want to enable resource profiling. The following workload types are supported: Deployment , StatefulSet, DaemonSet, and ReplicaSet.
   spec.enabledNamespaces	String	The namespaces for which you want to enable resource profiling. An asterisk (*) indicates all namespaces.

   Notice To generate accurate recommendations on resource specifications, we recommend that you wait at least one day after you enable resource profiling for your workloads. This way, you can obtain a sufficient amount of historical data.
2. Run the kubectl get recommendations -o yaml command to obtain recommendations on resource specifications for your workloads.
   After you enable resource profiling for your workloads, ack-slo-manager provides recommendations on resource specifications for each container in your workloads. The recommendations are stored in the Recommendation CRD. The following code block shows the content of a Recommendation CRD that stores the recommendation on resource specifications for a workload named cpu-load-gen:

   apiVersion: autoscaling.alibabacloud.com/v1alpha1
   kind: Recommendation
   metadata:
     labels:
       alpha.alibabacloud.com/recommendation-workload-apiVersion: app-v1
       alpha.alibabacloud.com/recommendation-workload-kind: Deployment
       alpha.alibabacloud.com/recommendation-workload-name: cpu-load-gen
     name: f20ac0b3-dc7f-4f47-b3d9-bd91f906****
     namespace: recommender-demo
   spec:
     workloadRef:
       apiVersion: apps/v1
       kind: Deployment
       name: cpu-load-gen
   status:
     recommendResources:
       containerRecommendations:
       - containerName: cpu-load-gen
         target:
           cpu: 4742m
           memory: 262144k
         originalTarget: # The intermediate results provided by the algorithm that is used to generate the recommendation on resource specifications. We recommend that you do not use the intermediate results. 
          # ...

   To facilitate data retrieval, the Recommendation CRD is generated in the same namespace as the workload. In addition, the Recommendation CRD has specific labels that record the API version, type, and name of the workload. The following table describes the labels.

   Label Key	Description	Example
   alpha.alibabacloud.com/recommendation-workload-apiVersion	The API version of the workload. The value of the label conforms to the Kubernetes specifications. Forward slashes (/) are replaced by hyphens (-).	app-v1 (Original form: app/v1)
   alpha.alibabacloud.com/recommendation-workload-kind	The type of the workload, for example, Deployment or StatefulSet.	Deployment
   alpha.alibabacloud.com/recommendation-workload-name	The name of the workload. The value of the label conforms to the Kubernetes specifications and cannot exceed 63 characters in length.	cpu-load-gen

   The recommendations on resource specifications for each container are displayed in the status.recommendResources.containerRecommendations section. The following table describes the fields.

   Field	Description	Format	Example
   containerName	The name of the container.	string	cpu-load-gen
   target	The recommendations on CPU and memory resources.	map[ResourceName]resource.Quantity	cpu: 4742m memory: 262144k
   originalTarget	The intermediate results provided by the algorithm that is used to generate the recommendation on resource specifications. We recommend that you do not use the intermediate results. If you want to use the intermediate results, Submit a ticket.	-	-

   Note The recommended minimum CPU resources are 0.025 vCPUs. The recommended minimum memory resources are 250 MB.
3. Verify the results in Application Real-Time Monitoring Service (ARMS) Prometheus
   ack-slo-manager allows you to check resource profiles in ARMS Prometheus.
   1. To collect the resource profiles generated by ack-slo-manager to ARMS Prometheus, modify the ConfigMap of ack-slo-manager based on the following content:
      By default, the resource profiles generated by ack-slo-manager are not collected to ARMS Prometheus. If you want to collect the resource profiles generated by ack-slo-manager, you must modify the ConfigMap of ack-slo-manager.

      apiVersion: v1
      data:
        recommender-config: 
          {
            "enableRecommendationTargetMetric": true
          }
      kind: ConfigMap
      metadata:
        namespace: kube-system
        name: ack-slo-manager-config

   2. Run the following command to update the ack-slo-manager-config ConfigMap.
      To avoid changing other settings in the ack-slo-manager-config ConfigMap, we recommend that you run the kubectl patch command to update the ConfigMap.

      kubectl patch cm -n kube-system ack-slo-manager-config --patch "$(cat configmap.yaml)"

   3. View details about the collected resource profiles.
      • If this is the first time you use Prometheus dashboards, reset the dashboards and install the Resource Profile dashboard. For more information about how to reset Prometheus dashboards, see Reset dashboards

        To view details about the collected resource profiles in ARMS Prometheus, perform the following steps:

        1. Log on to the ACK console.
        2. In the left-side navigation pane, click Clusters.
        3. On the Clusters page, find the cluster that you want to manage and click its name or click Details in the Actions column.
        4. In the left-side navigation pane of the cluster details page, choose Operations > Prometheus Monitoring.
        5. On the Prometheus Monitoring page, choose Cost Analysis/Resource Optimization > Resource Profile.

           On the Resource Profile tab, you can view details about the collected resource profiles. The details include the resource requests, resource usage, and recommended resource specifications of containers. For more information, see Enable ARMS Prometheus.

           
      • If you use a self-managed Prometheus monitoring system, you can use the following metrics to configure dashboards:

        # Information about the containers for which you want to generate recommendations on CPU resources. 
        slo_manager_recommender_recommendation_workload_target{exported_namespace="$namespace", workload_name="$workload", container_name="$container", resource="cpu"}
        # Information about the containers for which you want to generate recommendations on memory resources. 
        slo_manager_recommender_recommendation_workload_target{exported_namespace="$namespace", workload_name="$workload", container_name="$container", resource="memory"}

Examples

1. Create a file named cpu-load-gen.yaml with the following YAML template:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: cpu-load-gen
     labels:
       app: cpu-load-gen
   spec:
     replicas: 2
     selector:
       matchLabels:
         app: cpu-load-gen-selector
     template:
       metadata:
         labels:
           app: cpu-load-gen-selector
       spec:
         containers:
         - name: cpu-load-gen
           image: registry.cn-zhangjiakou.aliyuncs.com/acs/slo-test-cpu-load-gen:v0.1
           command: ["cpu_load_gen.sh"]
           imagePullPolicy: Always
           resources:
             requests:
               cpu: 8 # Request eight vCPUs for the application. 
               memory: "1G"
             limits:
               cpu: 12
               memory: "2G"

2. Run the following command to deploy the cpu-load-gen application:

   kubectl apply -f cpu-load-gen.yaml

3. Create a file named recommender-profile.yaml with the following YAML template:

   apiVersion: autoscaling.alibabacloud.com/v1alpha1
   kind: RecommendationProfile
   metadata:
     name: profile-demo
   spec:
     controllerKind:
     - Deployment
     enabledNamespaces: # Enable recommendations on resource specifications for all Deployments in the default namespace. 
     - default

4. Run the following command to enable resource profiling for the application that you created:

   kubectl apply -f recommender-profile.yaml

5. Run the following command to obtain recommendations on resource specifications for the application that you created.
   To generate accurate recommendations on resource specifications, we recommend that you wait at least one day after you enable resource profiling for your workloads. This way, you can obtain a sufficient amount of historical data. For more information about the labels, see Labels.

   kubectl get recommendations -l \
     "alpha.alibabacloud.com/recommendation-workload-apiVersion=apps-v1, \
     alpha.alibabacloud.com/recommendation-workload-kind=Deployment, \
     alpha.alibabacloud.com/recommendation-workload-name=cpu-load-gen" -o yaml

   Expected output:

   apiVersion: autoscaling.alibabacloud.com/v1alpha1
   kind: Recommendation
   metadata:
     creationTimestamp: "2022-02-09T08:56:51Z"
     labels:
       alpha.alibabacloud.com/recommendation-workload-apiVersion: app-v1
       alpha.alibabacloud.com/recommendation-workload-kind: Deployment
       alpha.alibabacloud.com/recommendation-workload-name: cpu-load-gen
     name: f20ac0b3-dc7f-4f47-b3d9-bd91f906****
     namespace: recommender-demo
   spec:
     workloadRef:
       apiVersion: apps/v1
       kind: Deployment
       name: cpu-load-gen
   status:
     conditions:
     - lastTransitionTime: "2022-02-09T08:56:52Z"
       status: "True"
       type: RecommendationProvided
     recommendResources:
       containerRecommendations:
       - containerName: cpu-load-gen
         target:
           cpu: 4742m # The recommended CPU resources are 4.742 vCPUs. 
           memory: 262144k
         originalTarget: # The intermediate results provided by the algorithm that is used to generate the recommendation on resource specifications. We recommend that you do not use the intermediate results. 
           #...              

Analyze the results

Compare the resource specifications in Step 1 and Step 5. The requested amount of CPU resources is greater than the amount of CPU resources in the recommendation. You can reduce the CPU request of the application to save resources in the cluster.