Gke Resource Allocation Autoscaling Optimization

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A financial services organization runs a mission-critical web application on a Google Kubernetes Engine (GKE) cluster. During periodic traffic spikes, several application Pods experience termination due to out-of-memory (OOM) events, while during normal hours, computing resources are underutilized. You are tasked with analyzing workload metrics to optimize cost efficiency and right-size resource requests and limits while ensuring maximum stability and responsiveness during autoscaling events.

Which combination of resource sizing and autoscaling configurations should you implement according to Google Cloud best practices?

Set container memory requests equal to memory limits, configure CPU requests to the minimum needed for SLOs with unbounded CPU limits, utilize Vertical Pod Autoscaler (VPA) in recommendation mode against real traffic, and fine-tune Horizontal Pod Autoscaler (HPA) targets with a safety buffer.

Remove container CPU and memory requests entirely to maximize node bin packing, configure the Horizontal Pod Autoscaler (HPA) target utilization to 100%, and enable Node Auto-Provisioning (NAP) without resource limits.

Set container memory limits significantly higher than requests to allow dynamic memory expansion, enforce hard CPU limits at 75% of node capacity, and disable horizontal autoscaling in favor of scheduled node pool resizing.

Configure container CPU requests equal to CPU limits to eliminate CPU contention, set memory requests to 50% of memory limits to allow burst headroom, and enable Vertical Pod Autoscaler (VPA) in auto-update mode alongside Horizontal Pod Autoscaler (HPA) targeting the same CPU metrics.

Professional Cloud DevOps Engineer

Gke Resource Allocation Autoscaling Optimization

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