Regime-Aware Resource Demand Forecasting for Cloud Scheduling: When History Beats ML and When ML Matters

Abstract

Efficient cluster scheduling requires reliable forecasts of resource demand, yet production workloads are heterogeneous, bursty, and strongly time-dependent. Using the Google Cluster-Usage Traces v3, we study leakage-safe prediction of three scheduling-relevant metrics: mean CPU demand, normalized memory pressure (average memory relative to assigned memory), and tail CPU demand (p95) as a burst-risk indicator. We develop a preprocessing and evaluation protocol that explicitly addresses two common threats to validity in trace-based learning: post-execution feature leakage (e.g., usage-derived fields) and identity leakage from random splits when recurring workloads appear in both training and test sets. Under time-ordered and gap-based splits, we compare gradient-boosted tree models (LightGBM) against strong history-only baselines (LastSeen and EMA) and perform a cold-start analysis by evaluating the first K occurrences of each workload entity. Results reveal a clear regime shift: for warm, recurring workloads, simple entity-history predictors achieve near-optimal accuracy and consistently outperform learned models; however, in cold-start settings where history is unavailable, LightGBM substantially improves CPU mean and tail forecasts (e.g., large gains in R2 for first-occurrence entities). These findings support a practical scheduling strategy: a hybrid, regime-aware policy that uses machine learning (ML) as a cold-start fallback and switches to lightweight history-based prediction as observations accumulate.