Kubernetes Cluster Autoscaling: Automatically Optimize Your Cluster Resources

Kubernetes cluster auto-scaling

Kubernetes cluster autoscaling is the ability to automatically adjust the number of nodes in a cluster based on workload demands. By dynamically scaling clusters, it ensures optimal resource utilization, reduces operational overhead and lowers costs.

This article explores how cluster autoscaling works, its components, settings, and best practices.

What is a cluster autoscaler?

this Cluster autoscaler Is a Kubernetes component that automatically scales the number of nodes in a cluster. It adds or removes nodes based on:

• Expand scale: When the Pod cannot be scheduled due to insufficient resources.
• downsize: When a node is underutilized and the workload can be accommodated on fewer nodes.

Cluster Autoscaler works with various cloud providers (such as AWS, Google Cloud, Azure, etc.) as well as custom settings.

How the cluster autoscaler works

1. Expand scale

When the scheduler cannot find a suitable node for a Pod due to resource constraints, Cluster Autoscaler:

• Analyze Pod’s resource requests (CPU, memory, GPU, etc.).
• Request the cloud provider to add nodes to the cluster.
• Once ready, reschedule pending Pods onto the new node.

2. Downsize

When a node is underutilized:

• Cluster Autoscaler checks whether the workload on a node can be rescheduled to other nodes.
• If this works, it drains the node (safely evicts the Pod) and removes it from the cluster.

Main features

1. Pod priority: Prioritize high-priority Pods during scaling decisions.
2. Node group management: Used with node pools or instance groups to add or delete nodes.
3. Resource optimization: Balance resource availability by removing underutilized nodes.
4. Supports multiple cloud providers: Compatible with AWS, Google Cloud, Azure, and more.

Cluster autoscaler settings

Prerequisites

• Kubernetes clusters executing on supported platforms (e.g. AWS, GCP, Azure).
• Cloud provider credentials configured for node extensions.

To enable cluster autoscaler

1. Install the cluster autoscaler
   Deploy the cluster autoscaler as a Kubernetes deployment in the cluster.

Example: YAML for GCP cluster autoscaler

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: cluster-autoscaler
     namespace: kube-system
     labels:
       app: cluster-autoscaler
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: cluster-autoscaler
     template:
       metadata:
         labels:
           app: cluster-autoscaler
       spec:
         containers:
         - image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.24.0
           name: cluster-autoscaler
           command:
           - ./cluster-autoscaler
           - --cloud-provider=gce
           - --nodes=1:10:
           - --scale-down-enabled=true
           - --skip-nodes-with-local-storage=false
           resources:
             limits:
               cpu: 100m
               memory: 300Mi

1. Configure node pool
   Defines the minimum and maximum number of nodes per node pool (or instance group) managed by the autoscaler.

Example: For GKE

   gcloud container clusters update  \
       --enable-autoscaling --min-nodes=1 --max-nodes=5 \
       --node-pool 

1. Label node (for AWS or custom settings)
   Use specific labels to identify groups of nodes managed by Cluster Autoscaler.

2. Monitor and verify

   • Check the Cluster Autoscaler’s logs for scaling operations:

    kubectl logs -n kube-system deployment/cluster-autoscaler
   

Cluster Autoscaler vs. Horizontal Pod Autoscaler

While both improve resource efficiency, they serve different purposes:

best practices

1. Use HPA and cluster autoscalers simultaneously

combine Horizontal Pod Autoscaler (HPA) Use Cluster Autoscaler for optimal workload scaling.

2. Define resource requests and limits

Make sure all workloads are specified resources.requests and resources.limits For CPU and memory. This helps Cluster Autoscaler accurately estimate resource requirements.

3. Optimize node pool configuration

• Use multiple node pools to meet different workload requirements (for example, compute-intensive vs. memory-intensive).
• Configure the appropriate minimum and maximum number of nodes.

4. Monitor extended operations

Use the following tools to track zoom events Prometheus and Grafanaor through the cloud provider dashboard.

5. Test scaling behavior

Simulate scenarios where pods are pending or nodes are underutilized to verify Cluster Autoscaler configuration.

6. Protect critical Pods

use Pod Disruption Budget (PDB) To prevent critical workloads from being evicted during scale-down.

Challenges and considerations

1. Start time: Adding new nodes may take some time, depending on the cloud provider.
2. Reduce latency:Cluster Autoscaler avoids actively deleting nodes to maintain stability.
3. local storage limit: Using locally stored Pods may prevent node scaling.

in conclusion

Cluster Autoscaler is a powerful tool for optimizing Kubernetes cluster resource utilization. By automatically scaling nodes based on demand, it ensures workload performance while controlling costs. Combining this with workload scaling strategies such as HPA can create a resilient and efficient infrastructure.