Professional Cloud DevOps Engineer
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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?
A custom ComputeClass in Google Kubernetes Engine allows platform administrators and DevOps engineers to declaratively specify compute requirements, priorities, and autoscaling policies for auto-created node pools. The autoscalingPolicy block within a ComputeClass custom resource provides granular control over node consolidation, overriding default cluster autoscaler profile behaviors.
StudyCerts
StudyCertsconsolidationThreshold parameter defines the resource utilization limit (as a percentage of allocatable node capacity) below which GKE flags a node as underutilized. Increasing this threshold percentage means nodes running at moderate-to-low utilization levels become eligible for consolidation sooner, allowing GKE to aggressively pack active Pods onto fewer nodes.consolidationDelayMinutes parameter dictates the time window GKE waits before evicting workloads, draining the node, and deleting the underlying Compute Engine instance. Reducing this value accelerates the removal of underutilized infrastructure during demand drops, directly minimizing compute spend.consolidationThreshold) and accelerator workloads (gpuConsolidationThreshold).Using native ComputeClass autoscaling parameters directly targets the node removal evaluation criteria. Rather than relying on global cluster autoscaler profiles that apply cluster-wide, configuring autoscalingPolicy at the ComputeClass level allows teams to tailor consolidation aggressiveness specifically for fluctuating workload profiles.
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