Overprovisioned Node Pool in GKE Cluster

Service Category

Compute

Cloud Provider

GCP

Service Name

GCP GKE

Inefficiency Type

Overprovisioned Resource

Explanation

Node pools provisioned with large or specialized VMs (e.g., high-memory, GPU-enabled, or compute-optimized) can be significantly overprovisioned relative to the actual pod requirements. If workloads consistently leave a large portion of resources unused (e.g., low CPU/memory request-to-capacity ratio), the organization incurs unnecessary compute spend. This often happens in early cluster design phases, after application demand shifts, or when teams allocate for peak usage without autoscaling.

Relevant Billing Model

Billed based on: * Underlying Compute Engine VMs in the node pool (vCPU, memory, and attached storage) * GPU usage (if applicable) * Operating system licensing (for premium OS options) Control plane is free for Autopilot clusters or billed per cluster for Standard mode.

Detection

Compare requested vs. allocatable CPU and memory across node pools
Identify persistent gaps between allocated and requested resources
Review cluster autoscaler activity and pod eviction logs to assess actual demand patterns
Check for large nodes with single pods or with minimal utilization
Determine whether node pools have taints preventing broader scheduling

Remediation

Resize nodes to align with observed workload requirements
Enable or tune cluster autoscaler to manage node pool size dynamically
Split heterogeneous workloads into separate node pools for right-sized resources
Use Autopilot mode for smaller environments where Google manages node sizing
Consolidate workloads and reduce fragmentation by adjusting pod limits and requests

Relevant Documentation

https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler https://cloud.google.com/kubernetes-engine/docs/how-to/resizing-node-pools https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-architecture

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