Autoscaling in Kubernetes

What is Autoscaling in Kubernetes?

Autoscaling in Kubernetes allows applications to adjust dynamically to workload changes. Instead of manually increasing or decreasing resources, Kubernetes can automatically scale based on demand.

Why is Autoscaling Important?

  • ⚡ **Improves Performance** - Ensures enough resources for high traffic.
  • 💰 **Cost Efficient** - Prevents over-provisioning, saving money on unused resources.
  • 🔄 **Automatic Scaling** - Dynamically adjusts to load changes without human intervention.

Types of Autoscaling

Kubernetes provides three main types of autoscaling:

  • 🔄 Horizontal Pod Autoscaler (HPA) – Scales the number of pods in a deployment.
  • 📈 Vertical Pod Autoscaler (VPA) – Adjusts the CPU and memory of existing pods.
  • ⚙️ Cluster Autoscaler – Adjusts the number of worker nodes in the cluster.

1. Horizontal Pod Autoscaler (HPA)

HPA automatically increases or decreases the number of running pods based on CPU or memory usage.

Step 1: Enable the Metrics Server

The Metrics Server is required for HPA to monitor CPU and memory usage. Install it using:

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

Step 2: Create a Deployment

Define an Nginx deployment that will be autoscaled:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx
        resources:
          requests:
            cpu: "250m"
          limits:
            cpu: "500m"

Step 3: Apply Horizontal Pod Autoscaler

Run the following command to create an HPA:

kubectl autoscale deployment nginx-deployment --cpu-percent=50 --min=1 --max=5

This scales the deployment between **1 and 5** pods when CPU usage exceeds **50%**.

Step 4: Check the Status of HPA

kubectl get hpa

2. Vertical Pod Autoscaler (VPA)

VPA adjusts the CPU and memory of pods instead of adding new ones. This is useful for applications with unpredictable resource usage.

Step 1: Install Vertical Pod Autoscaler

kubectl apply -f https://github.com/kubernetes/autoscaler/releases/latest/download/vertical-pod-autoscaler.yaml

Step 2: Create a VPA Configuration

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

Step 3: Apply VPA

kubectl apply -f nginx-vpa.yaml

This will automatically adjust CPU and memory requests for Nginx pods.

3. Cluster Autoscaler

The Cluster Autoscaler increases or decreases the number of worker nodes based on the cluster's overall resource demands.

How Does Cluster Autoscaler Work?

  • ✅ Adds new nodes when pods are pending due to insufficient resources.
  • ✅ Removes nodes when they are underutilized.
  • ✅ Works best with cloud providers like AWS, GCP, and Azure.

Step 1: Enable Cluster Autoscaler

To enable autoscaling for a managed Kubernetes cluster, use:

kubectl apply -f cluster-autoscaler.yaml

Step 2: Monitor Cluster Autoscaler

kubectl get nodes

When to Use Each Autoscaler?

  • 📌 Use HPA when you need to scale horizontally by adding more pods.
  • 📌 Use VPA when you need to adjust CPU and memory dynamically for pods.
  • 📌 Use Cluster Autoscaler when you need to scale worker nodes up/down.

Conclusion

Autoscaling helps Kubernetes applications handle traffic efficiently while optimizing costs. With **HPA, VPA, and Cluster Autoscaler**, Kubernetes ensures your workloads run smoothly under any load.