This question explores Cloud Computing and addresses practical considerations related to: How do I troubleshoot pod autoscaling issues in Kubernetes clusters?

To troubleshoot pod autoscaling issues in Kubernetes clusters, you need to focus on the Horizontal Pod Autoscaler (HPA) configuration, resource metrics, and cluster capacity. The Well-Architected Framework's reliability pillar emphasizes monitoring and adjusting your autoscaling policies to ensure application resilience and performance. Access the Kubernetes cluster using kubectl or a cloud provider's Kubernetes dashboard. Check the HPA configuration with `kubectl get hpa` to ensure the correct metrics and thresholds are set. Verify resource metrics using `kubectl top pods` and `kubectl top nodes` to ensure they reflect actual usage. Review the event logs with `kubectl describe hpa` to identify any scaling-related errors or warnings. Ensure sufficient cluster resources (CPU, memory) are available for scaling by checking node capacity and utilization. ADDITIONAL COMMENT: - Ensure that the metrics server is properly deployed and configured in your cluster. - Verify that the HPA is targeting the correct Deployment or StatefulSet. - Check for any resource limits or requests that might be affecting scaling behavior. - Consider using custom metrics if default CPU/memory metrics do not meet your scaling needs. - Review any recent changes to the application or cluster that might impact scaling. ✅

How do I troubleshoot pod autoscaling issues in Kubernetes clusters?

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How do I troubleshoot pod autoscaling issues in Kubernetes clusters?

Asked on Nov 12, 2025

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To troubleshoot pod autoscaling issues in Kubernetes clusters, you need to focus on the Horizontal Pod Autoscaler (HPA) configuration, resource metrics, and cluster capacity. The Well-Architected Framework's reliability pillar emphasizes monitoring and adjusting your autoscaling policies to ensure application resilience and performance.

Access the Kubernetes cluster using kubectl or a cloud provider's Kubernetes dashboard.
Check the HPA configuration with `kubectl get hpa` to ensure the correct metrics and thresholds are set.
Verify resource metrics using `kubectl top pods` and `kubectl top nodes` to ensure they reflect actual usage.
Review the event logs with `kubectl describe hpa` to identify any scaling-related errors or warnings.
Ensure sufficient cluster resources (CPU, memory) are available for scaling by checking node capacity and utilization.

Additional Comment:

Ensure that the metrics server is properly deployed and configured in your cluster.
Verify that the HPA is targeting the correct Deployment or StatefulSet.
Check for any resource limits or requests that might be affecting scaling behavior.
Consider using custom metrics if default CPU/memory metrics do not meet your scaling needs.
Review any recent changes to the application or cluster that might impact scaling.

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How do I troubleshoot pod autoscaling issues in Kubernetes clusters?

Asked on Nov 12, 2025

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