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How does auto-scaling work in a Kubernetes cluster?

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How does auto-scaling work in a Kubernetes cluster?

Asked on Nov 30, 2025

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Auto-scaling in a Kubernetes cluster dynamically adjusts the number of pods or nodes based on resource usage and demand, ensuring efficient utilization and cost management. Kubernetes provides Horizontal Pod Autoscaler (HPA) for scaling pods and Cluster Autoscaler for scaling nodes, both of which are essential for maintaining application performance and availability.

Example Concept: The Horizontal Pod Autoscaler (HPA) automatically scales the number of pod replicas in a deployment, replication controller, or replica set based on observed CPU utilization or other custom metrics. The Cluster Autoscaler, on the other hand, adjusts the size of the node pool by adding or removing nodes based on pending pods and resource requests. These mechanisms work together to ensure that the Kubernetes cluster can handle varying workloads efficiently.

Additional Comment:

HPA uses metrics from the Kubernetes Metrics Server or custom metrics via the Custom Metrics API.
Cluster Autoscaler is typically configured with cloud provider-specific settings to manage node pools.
Proper configuration of resource requests and limits is crucial for effective auto-scaling.
Monitoring and logging are essential to observe the impact of scaling actions and adjust configurations as needed.

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How does auto-scaling work in a Kubernetes cluster?

Asked on Nov 30, 2025

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