Gke Computeclass Autoscaling Node Consolidation

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Your organization runs dynamically scaled container workloads on Google Kubernetes Engine (GKE) and is implementing FinOps practices to eliminate waste and optimize resource packing density. You deploy custom ComputeClass resources to manage auto-created node pools for variable workloads.

During off-peak periods, you observe that several nodes with low CPU and memory utilization remain running for prolonged periods before being reclaimed, resulting in unnecessary infrastructure compute spend. You need to fine-tune the node consolidation behavior to more aggressively consolidate workloads onto fewer nodes and reduce the duration underutilized nodes persist before removal.

Which configuration in the ComputeClass definition should you implement?

Define the autoscalingPolicy section in the ComputeClass manifest, specifying a higher consolidationThreshold percentage for CPU and memory, and setting a lower value for consolidationDelayMinutes.

Enable cluster-wide Node Auto-Provisioning (NAP) limits for CPU and memory and set the scaleDownFactor parameter to 1.0 in the Pod specification.

Set the cluster-autoscaler.kubernetes.io/safe-to-evict annotation to false in the Pod templates and configure consolidationDelayMinutes to zero.

Configure a lower consolidationThreshold percentage within the ComputeClass and set whenUnsatisfiable: ScaleUpAnyway to prevent idle node reclamation.

Professional Cloud DevOps Engineer

Gke Computeclass Autoscaling Node Consolidation

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