Conformal GPU Demand Envelopes and Power-Aware Scheduling for Heterogeneous AI Clusters under Cold Start and Cross-Cloud Shift - Sijia Chen; Marcus Reed; Hong Zhang (Journal of Information Systems and Business Technology, 2026)
Production LLM traffic is bursty, and capacity decisions couple GPU availability with power and carbon. We convert probabilistic demand forecasts into risk-controlled admission, heterogeneous allocation, calibrated overbooking, and cross-site dispatch. The evaluation uses all 1,429,737 requests in the 61-day BurstGPT trace and SustainDC carbon, weather, workload, configuration, and physical power model. Requests form a complete five-minute grid with a weighted-token target. A chronological split assigns 42 days to fitting, nine to conformal calibration, and ten to testing. Point models include persistence, seasonality, Ridge, histogram gradient boosting, and Extra Trees; interval models include direct quantiles, split, rolling, regime-conditioned, and conformalized quantile regression. Histogram gradient boosting attained 49.68% WAPE versus 115.81% for daily seasonality and reduced mean absolute error by 31,545.90 weighted tokens/bin (95% block-bootstrap interval, 23,152.07–37,746.37). Rolling conformal achieved 90.00% coverage at mean width 86,728.29. With a calibration-selected 0.80 overbooking factor, the proposed policy recorded 5.11% bin-level SLO violations and reduced emissions from 52.51 to 12.57 tCO2e through SustainDC-aware dispatch. The remaining 8.81% workload shortfall quantifies burst risk; GPU type, site, and price remain scenario inputs.