Vertical Pod Autoscaling (VPA) is a feature in Kubernetes that automatically adjusts the CPU and memory requests and limits for your pods based on their actual usage. This ensures that your applications have the resources they need to run efficiently without over-provisioning.

1. Resource Requests and Limits:

   • Requests: The amount of CPU and memory guaranteed to a container.
   • Limits: The maximum amount of CPU and memory a container can use.

2. Vertical Pod Autoscaler (VPA):

   • A Kubernetes component that monitors the resource usage of pods and adjusts their resource requests and limits accordingly.

3. VPA Modes:

   • Off: VPA is not active.
   • Auto: VPA automatically updates the resource requests and limits.
   • Recreate: VPA updates the resource requests and limits by recreating the pods.
   • Initial: VPA sets the initial resource requests and limits when the pod is created.

• A running Kubernetes cluster.
• kubectl command-line tool configured to communicate with your cluster.

1. Clone the VPA repository:

   git clone https://github.com/kubernetes/autoscaler.git
   cd autoscaler/vertical-pod-autoscaler/
   

2. Deploy the VPA components:

   kubectl apply -f vertical-pod-autoscaler/deploy/recommender.yaml
   kubectl apply -f vertical-pod-autoscaler/deploy/updater.yaml
   kubectl apply -f vertical-pod-autoscaler/deploy/admission-controller.yaml
   

1. Create a VPA object:

   apiVersion: autoscaling.k8s.io/v1
   kind: VerticalPodAutoscaler
   metadata:
     name: my-vpa
   spec:
     targetRef:
       apiVersion: "apps/v1"
       kind:       Deployment
       name:       my-deployment
     updatePolicy:
       updateMode: "Auto"
   

2. Apply the VPA configuration:

   kubectl apply -f my-vpa.yaml
   

1. Create a sample deployment:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: my-deployment
   spec:
     replicas: 2
     selector:
       matchLabels:
         app: my-app
     template:
       metadata:
         labels:
           app: my-app
       spec:
         containers:
         - name: my-container
           image: nginx
           resources:
             requests:
               cpu: "100m"
               memory: "128Mi"
             limits:
               cpu: "200m"
               memory: "256Mi"
   

2. Apply the deployment:

   kubectl apply -f my-deployment.yaml
   

3. Apply the VPA configuration:

   kubectl apply -f my-vpa.yaml
   

• Check VPA recommendations:

  kubectl describe vpa my-vpa
  

• View updated resource requests and limits:

  kubectl get pods -o=jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.spec.containers[*].resources}{"\n"}{end}'
  

1. Create a deployment for a sample application.
2. Configure and apply a VPA object for the deployment.
3. Monitor the resource adjustments made by VPA.

1. Create a deployment:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: sample-deployment
   spec:
     replicas: 2
     selector:
       matchLabels:
         app: sample-app
     template:
       metadata:
         labels:
           app: sample-app
       spec:
         containers:
         - name: sample-container
           image: nginx
           resources:
             requests:
               cpu: "100m"
               memory: "128Mi"
             limits:
               cpu: "200m"
               memory: "256Mi"
   

2. Apply the deployment:

   kubectl apply -f sample-deployment.yaml
   

3. Create a VPA configuration:

   apiVersion: autoscaling.k8s.io/v1
   kind: VerticalPodAutoscaler
   metadata:
     name: sample-vpa
   spec:
     targetRef:
       apiVersion: "apps/v1"
       kind:       Deployment
       name:       sample-deployment
     updatePolicy:
       updateMode: "Auto"
   

4. Apply the VPA configuration:

   kubectl apply -f sample-vpa.yaml
   

5. Monitor the VPA recommendations:

   kubectl describe vpa sample-vpa
   

6. Check the updated resource requests and limits:

   kubectl get pods -o=jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.spec.containers[*].resources}{"\n"}{end}'
   

• Incorrect VPA mode: Ensure the updateMode is set correctly based on your needs (e.g., Auto for automatic updates).
• Resource limits too high/low: Monitor the resource usage and adjust the initial requests and limits accordingly.
• Not monitoring VPA recommendations: Regularly check VPA recommendations to ensure your pods are getting the right resources.

Vertical Pod Autoscaling is a powerful feature in Kubernetes that helps optimize resource usage by automatically adjusting the CPU and memory requests and limits for your pods. By setting up and configuring VPA, you can ensure that your applications run efficiently without over-provisioning resources. In the next section, we will explore Cluster Autoscaling, which focuses on scaling the entire cluster based on resource demands.