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