Abhay Singh Kathayat

Posted on Dec 23

Kubernetes Cluster Autoscaling: Automatically Optimize Your Cluster Resources

#k8s #devops #clusterautoscaler #cloudcomputing

Kubernetes Cluster Autoscaling

Kubernetes Cluster Autoscaling is a feature that automatically adjusts the number of nodes in a cluster based on workload demands. By dynamically scaling the cluster, it ensures optimal resource utilization, reduces operational overhead, and lowers costs.

This article explores how Cluster Autoscaling works, its components, setup, and best practices.

What Is Cluster Autoscaler?

The Cluster Autoscaler is a Kubernetes component that automatically scales the number of nodes in a cluster. It adds or removes nodes based on the following conditions:

Scale-Up: When Pods cannot be scheduled due to insufficient resources.
Scale-Down: When nodes are underutilized and workloads can be accommodated on fewer nodes.

Cluster Autoscaler works with various cloud providers, such as AWS, Google Cloud, Azure, and others, as well as custom setups.

How Cluster Autoscaler Works

1. Scale-Up

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

Analyzes the Pod's resource requests (CPU, memory, GPU, etc.).
Requests the cloud provider to add a new node to the cluster.
Reschedules the pending Pod onto the new node once it’s ready.

2. Scale-Down

When nodes are underutilized:

Cluster Autoscaler checks if the workloads on a node can be rescheduled onto other nodes.
If feasible, it drains the node (evicting Pods safely) and removes it from the cluster.

Key Features

Pod Prioritization: Gives preference to high-priority Pods during scale-up decisions.
Node Group Management: Works with node pools or instance groups to add or remove nodes.
Resource Optimization: Balances resource availability by removing underutilized nodes.
Support for Multiple Cloud Providers: Compatible with AWS, Google Cloud, Azure, and more.

Cluster Autoscaler Setup

Prerequisites

A Kubernetes cluster running on a supported platform (e.g., AWS, GCP, Azure).
Cloud provider credentials configured for node scaling.

Steps to Enable Cluster Autoscaler

Install the Cluster Autoscaler Deploy the Cluster Autoscaler as a Kubernetes Deployment in your 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:<node-group-name>
           - --scale-down-enabled=true
           - --skip-nodes-with-local-storage=false
           resources:
             limits:
               cpu: 100m
               memory: 300Mi

Configure Node Pools Define minimum and maximum node counts for each node pool (or instance group) the autoscaler manages.

Example: For GKE

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

Tag Nodes (for AWS or Custom Setups)
Use specific tags to identify node groups managed by Cluster Autoscaler.
Monitor and Validate
- Check the logs of the Cluster Autoscaler for scaling actions:
```
 kubectl logs -n kube-system deployment/cluster-autoscaler
```

Cluster Autoscaler vs. Horizontal Pod Autoscaler

While both improve resource efficiency, they serve different purposes:

Feature	Cluster Autoscaler	Horizontal Pod Autoscaler (HPA)
Scope	Scales nodes in the cluster	Scales Pods in a deployment
Trigger	Pending Pods due to resource shortage	CPU/memory usage exceeds thresholds
Focus	Infrastructure (nodes)	Workloads (Pods)
Implementation	Cloud provider-specific	Kubernetes-native

Best Practices

1. Use Both HPA and Cluster Autoscaler

Combine the Horizontal Pod Autoscaler (HPA) with Cluster Autoscaler for optimal workload scaling.

2. Define Resource Requests and Limits

Ensure all workloads specify resources.requests and resources.limits for CPU and memory. This helps Cluster Autoscaler accurately estimate resource needs.

3. Optimize Node Pool Configuration

Use multiple node pools for varying workload requirements (e.g., compute-intensive vs. memory-intensive).
Configure appropriate minimum and maximum node counts.

4. Monitor Scaling Actions

Track scaling events using tools like Prometheus and Grafana, or through cloud provider dashboards.

5. Test Scaling Behavior

Simulate scenarios where Pods are pending or nodes are underutilized to validate the Cluster Autoscaler configuration.

6. Protect Critical Pods

Use Pod Disruption Budgets (PDBs) to prevent critical workloads from being evicted during scale-down.

Challenges and Considerations

Startup Time: Adding new nodes may take time depending on the cloud provider.
Scale-Down Delays: Cluster Autoscaler avoids removing nodes aggressively to maintain stability.
Local Storage Constraints: Pods using local storage may block node scale-down.

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 keeping costs under control. Combining it with workload scaling strategies like HPA can create a resilient and efficient infrastructure.

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