Cluster Resource Allocation

Where does cluster capacity go? Audit.

Buckets

Cluster resource allocation is the discipline of understanding where the cluster's compute and memory go. Without buckets, the cluster's resources are an opaque pool; with buckets, the team sees what is consumed by what and can optimize where it matters.

What the buckets are:

• System overhead.: The Kubernetes node's own consumption: kubelet, container runtime, OS daemons, monitoring agents. The system overhead is unavoidable; it is not waste, but understanding its size is the foundation.
• kube-system.: The cluster's own infrastructure: CoreDNS, ingress controllers, monitoring stack, log shippers. The kube-system pods are necessary; their size is bounded; understanding the size enables capacity planning.
• App workloads.: The actual application pods. The team's reason for running the cluster; the pods that produce value. The app workload share should be the largest bucket.
• Idle.: Resources allocated but not used. Pods with requests larger than their actual consumption; nodes with capacity that no pod needs. Idle is waste in the cost-optimization sense.
• Each measurable.: Each bucket can be measured. The cluster's metric data shows the per-bucket consumption; the team can quantify and report.

The buckets are the framework. The framework supports the conversation about where resources go and where to optimize.

Typical

Healthy clusters have predictable resource splits. Knowing the typical proportions helps the team identify outliers; clusters that deviate significantly indicate issues to investigate.

• System: 10 to 20%.: System overhead and kube-system together typically consume 10 to 20% of cluster resources. Above this, the overhead is unusually high; below this, the cluster might be under-provisioned in critical infrastructure.
• App: 50 to 70%.: Application workloads should consume the majority. The exact percentage varies by cluster and workload; 50 to 70% is the typical productive range.
• Idle: 15 to 30%.: Some idle is normal: capacity buffer for bursts, allocation overhead, requests larger than usage. The 15 to 30% range covers typical efficiency.
• Idle equals waste.: The idle bucket is the optimization target. Reducing idle without compromising reliability or burst capacity produces direct cost savings.
• Track the trend.: The bucket sizes trend over time. Healthy clusters maintain stable proportions; degrading proportions indicate growing waste or growing system overhead.

The typical proportions are the calibration. Deviations from typical produce specific investigation paths.

Act

The action focuses on reducing idle. Idle is waste; reducing it is the optimization. The tools are well-known; the discipline is sustained application.

• Reduce idle.: The team identifies sources of idle: oversized requests, over-provisioned capacity, inefficient packing. Each source has its own remediation.
• VPA.: Vertical Pod Autoscaler adjusts pod requests based on actual usage. The autoscaler reduces request sizes for pods using less than they request; idle from over-requesting drops.
• HPA tuning.: Horizontal Pod Autoscaler controls pod count based on load. Tuning produces better packing; idle from over-provisioned pod count drops.
• Right-size cluster.: The cluster's overall capacity should match actual demand. Over-provisioned clusters carry idle nodes; the right-sizing exercise catches them.
• Compounds.: Each optimization produces ongoing savings. Across many pods and many clusters, the cumulative savings are large. The discipline is sustained; the savings compound across years.

Cluster resource allocation is one of those Kubernetes operational disciplines that pays off proportionally to cluster size. Nova AI Ops integrates with cluster telemetry, surfaces per-bucket consumption, and produces the optimization queue that the platform team uses to drive the idle bucket down over time.