Two-Stage Stochastic Energy-Efficient Scheduling of Geo-Distributed Data Centers with Spatio-Temporal Workload Flexibility

Geo-distributed data centers (DCs) provide important opportunities to improve operational sustainability through coordinated spatial workload migration and temporal workload shifting. However, stochastic workload arrivals, coupled with server operation, cooling dynamics, and inter-DC network constraints, make cost-optimal scheduling highly challenging. To address this issue, a two-stage stochastic scheduling framework is proposed herein that jointly coordinates inflexible, temporal-shiftable, and spatially migratable workloads across multiple DCs and time slots. Workload uncertainty is efficiently handled through Latin hypercube sampling and fast forward scenario reduction. Numerical experiments on a three-DC system demonstrate that the proposed framework reshapes workload distribution across time and space, reducing total energy consumption by approximately 3.7% and total operating cost by about 6.7% compared with the baseline case without flexibility, while maintaining stable server utilization under increasing workload uncertainty. These results demonstrate that the framework provides an effective and practically implementable approach for economically efficient and energy-efficient scheduling of geo-distributed DCs, thereby supporting sustainable operation through coordinated workload flexibility and more efficient utilization of computing and energy resources.