When Does Learning Beat Heuristics? A Case Study in Kubernetes Scheduler Score Plugins
Kubernetes scheduler plugins that score candidate nodes are, in production, hand-tuned heuristics. We ask whether a learned scoring function - trained on real placement decisions from a production cluster trace - can match or exceed these heuristics, and if not, why. We implement an external, HTTP-backed scoring plugin for a widely used scheduler simulator and evaluate two learned models (a Random Forest over engineered features, and a graph neural network encoder over per-job task-dependency graphs) trained on a large-scale production cluster trace. Under standard regression fit (R^2), both models improve modestly but monotonically across four feature-engineering iterations, reaching R^2...
| Authors | Wang Xuying, Zhibek Sarypbekova |
|---|---|
| Primary category | cs.DC |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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