Vertical scaling of an application in a Yandex Managed Service for Kubernetes cluster

Written by

Updated at May 5, 2025

Required paid resources
Getting started
Create Vertical Pod Autoscaler and a test application
Test Vertical Pod Autoscaler
Delete the resources you created

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:

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

Required paid resources

The support cost includes:

Managed Service for Kubernetes cluster fee: using the master and outgoing traffic (see Managed Service for Kubernetes pricing).
Cluster nodes (VM) fee: using computing resources, operating system, and storage (see Compute Cloud pricing).
Fee for the public IP address assigned to cluster nodes (see Virtual Private Cloud pricing).

Getting started

If you do not have the Yandex Cloud CLI yet, install and initialize it.

The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can specify a different folder using the --folder-name or --folder-id parameter.
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.
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.
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.
Install kubect and configure it to work with the new 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.

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 application

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

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

Create objects:

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

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 Autoscaler

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

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

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

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.

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

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

Delete the resources you created

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

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

Vertical scaling of an application in a Yandex Managed Service for Kubernetes cluster

Required paid resourcesRequired paid resources

Getting startedGetting started

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

Test Vertical Pod AutoscalerTest Vertical Pod Autoscaler

Delete the resources you createdDelete the resources you created

Was the article helpful?

Required paid resources

Getting started

Create Vertical Pod Autoscaler and a test application

Test Vertical Pod Autoscaler

Delete the resources you created