EduScholarIT . 7th Aug, 2025 12:31 PM
Kubernetes Auto Scaling enables applications and workloads to automatically adjust resource usage in response to varying demand. This ensures optimal performance while controlling costs by scaling resources up or down as needed.
1. Horizontal Pod Autoscaler (HPA):
- Scales the number of pod replicas based on CPU/memory usage or custom
metrics.
- Example: Increasing replicas from 3 to 10 when CPU usage exceeds 70%.
2. Vertical Pod Autoscaler (VPA):
- Adjusts CPU and memory requests/limits for containers in a pod.
- Useful when a workload needs more resources without scaling out.
3. Cluster Autoscaler (CA):
- Scales the number of nodes in the cluster automatically.
- Works with cloud providers like AWS, GCP, and Azure to add/remove nodes.
1. Metrics Collection: Kubernetes gathers data from the
Metrics Server or custom metrics APIs.
2. Evaluation: The autoscaler checks metrics against defined thresholds.
3. Scaling Decision: Based on the evaluation, it decides whether to scale up,
scale down, or keep the current state.
4. Execution: Pods or nodes are added, removed, or resized.
- Optimized resource usage
- Enhanced application performance
- Reduced costs by avoiding over-provisioning
- Automatic adaptation to workload changes
kubectl autoscale deployment my-app --cpu-percent=70 --min=3 --max=10
