Vertical application scaling in a cluster

Managed Service for Kubernetes supports several types of autoscaling. In this article you will learn how to configure the automatic management of pod resources with Vertical Pod Autoscaler:

• Create Vertical Pod Autoscaler and a test application.
• Test Vertical Pod Autoscaler.

If you no longer need the resources you created, delete them.

Getting startedGetting started

1. If you do not have the Yandex Cloud command line interface yet, install and initialize it.

   The folder specified in the CLI profile is used by default. You can specify a different folder using the --folder-name or --folder-id parameter.

2. Create security groups for the Managed Service for Kubernetes cluster and its node groups.

   Warning

   The configuration of security groups determines the performance and availability of the cluster and the services and applications running in it.

3. Create a Managed Service for Kubernetes cluster. Use these settings:

   • Use the previously created security groups.
   • If you intend to use your cluster within the Yandex Cloud network, there is no need to allocate a public IP address to it. To allow connections from outside the network, assign a public IP address to the cluster.

4. Create a node group. Use these settings:

   • Use the previously created security groups.
   • Allocate it a public IP address to grant internet access to the node group and allow pulling Docker images and components.

5. Install kubectl and configure it to work with the created cluster.

   If a cluster has no public IP address assigned and kubectl is configured via the cluster's private IP address, run kubectl commands on a Yandex Cloud VM that is in the same network as the cluster.

6. Install Vertical Pod Autoscaler from the following repository:

   cd /tmp && \
     git clone https://github.com/kubernetes/autoscaler.git && \
     cd autoscaler/vertical-pod-autoscaler/hack && \
     ./vpa-up.sh
   

Create Vertical Pod Autoscaler and a test applicationCreate Vertical Pod Autoscaler and a test application

1. Create a file named app.yaml with the nginx test application and load balancer settings:

   app.yaml

   ---
   ### Deployment
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: nginx
     labels:
       app: nginx
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: nginx
     template:
       metadata:
         name: nginx
         labels:
           app: nginx
       spec:
         containers:
           - name: nginx
             image: registry.k8s.io/hpa-example
             resources:
               requests:
                 memory: "256Mi"
                 cpu: "500m"
               limits:
                 memory: "500Mi"
                 cpu: "1"
   ---
   ### Service
   apiVersion: v1
   kind: Service
   metadata:
     name: nginx
   spec:
     selector:
       app: nginx
     ports:
       - protocol: TCP
         port: 80
         targetPort: 80
     type: LoadBalancer
   

2. Create a file named vpa.yaml with Vertical Pod Autoscaler configuration:

   vpa.yaml

   ---
   apiVersion: autoscaling.k8s.io/v1
   kind: VerticalPodAutoscaler
   metadata:
     name: nginx
   spec:
     targetRef:
       apiVersion: "apps/v1"
       kind:       Deployment
       name:       nginx
     updatePolicy:
       updateMode:  "Auto"
       minReplicas: 1
   

3. Create objects:

   kubectl apply -f app.yaml && \
   kubectl apply -f vpa.yaml
   

4. Make sure the Vertical Pod Autoscaler and nginx pods have entered the Running state:

   kubectl get pods -n kube-system | grep vpa && \
   kubectl get pods | grep nginx
   

   Result:

   vpa-admission-controller-58********-qmxtv  1/1  Running  0  44h
   vpa-recommender-67********-jqvgt           1/1  Running  0  44h
   vpa-updater-64********-xqsts               1/1  Running  0  44h
   nginx-6c********-62j7w                     1/1  Running  0  42h
   

Test Vertical Pod AutoscalerTest Vertical Pod Autoscaler

To test Vertical Pod Autoscaler, nginx application workload will be simulated.

1. Review the recommendations provided by Vertical Pod Autoscaler prior to creating the workload:

   kubectl describe vpa nginx
   

   Note the low Cpu values in the Status.Recommendation.Container Recommendations metrics:

   Name:         nginx
   Namespace:    default
   Labels:       <none>
   Annotations:  <none>
   API Version:  autoscaling.k8s.io/v1
   Kind:         VerticalPodAutoscaler
   ...
   Status:
     Conditions:
       Last Transition Time:  2022-03-18T08:02:04Z
       Status:                True
       Type:                  RecommendationProvided
     Recommendation:
       Container Recommendations:
         Container Name:  nginx
         Lower Bound:
           Cpu:     25m
           Memory:  262144k
         Target:
           Cpu:     25m
           Memory:  262144k
         Uncapped Target:
           Cpu:     25m
           Memory:  262144k
         Upper Bound:
           Cpu:     25m
           Memory:  262144k
   

2. Make sure Vertical Pod Autoscaler is managing the nginx application pod resources:

   kubectl get pod <nginx_pod_name> --output yaml
   

   Result:

   apiVersion: v1
   kind: Pod
   metadata:
     annotations:
       vpaObservedContainers: nginx
       vpaUpdates: 'Pod resources updated by nginx: container 0: cpu request, memory
         request, cpu limit, memory limit'
   ...
   spec:
     containers:
     ...
       name: nginx
       resources:
         limits:
           cpu: 50m
           memory: 500000Ki
         requests:
           cpu: 25m
           memory: 262144k
   

3. Run the workload simulation process in a separate window:

   URL=$(kubectl get service nginx -o json \
     | jq -r '.status.loadBalancer.ingress[0].ip') && \
     while true; do wget -q -O- http://$URL; done
   

   Tip

   To increase load and accelerate the execution of the scenario, run several processes in separate windows.

   Note

   If the resource is unavailable at the specified URL, make sure that the security groups for the Managed Service for Kubernetes cluster and its node groups are configured correctly. If any rule is missing, add it.

4. After several minutes, review the recommendation provided by Vertical Pod Autoscaler after the workload is created:

   kubectl describe vpa nginx
   

   Vertical Pod Autoscaler allocated additional resources to the pods as the workload increased. Note the increased Cpu values in the Status.Recommendation.Container Recommendations metrics:

   Name:         nginx
   Namespace:    default
   Labels:       <none>
   Annotations:  <none>
   API Version:  autoscaling.k8s.io/v1
   Kind:         VerticalPodAutoscaler
   ...
   Status:
    Conditions:
       Last Transition Time:  2022-03-18T08:02:04Z
       Status:                True
       Type:                  RecommendationProvided
     Recommendation:
       Container Recommendations:
         Container Name:  nginx
         Lower Bound:
           Cpu:     25m
           Memory:  262144k
         Target:
           Cpu:     410m
           Memory:  262144k
         Uncapped Target:
           Cpu:     410m
           Memory:  262144k
         Upper Bound:
           Cpu:     28897m
           Memory:  1431232100
   

5. Stop simulating the workload. Within a few minutes, the Status.Recommendation.Container Recommendations metrics will return to their original values.

Delete the resources you createdDelete the resources you created

Delete the resources you no longer need to avoid paying for them:

1. Delete the Kubernetes cluster.
2. If static public IP addresses were used for cluster and node access, release and delete them.