Sustainability-Aware Workload Shifting Beyond Carbon Intensity

About

Time- and location-shifting of computational workloads is widely proposed to reduce data-centre emissions by exploiting variation in electricity carbon intensity. However, CO$_2$-only optimization can shift burdens to places where impacts are experienced locally, such as water withdrawals in stressed basins, worsened air-pollution exposure, and increased stress on constrained grids. We present Orca, a sustainability-aware workload shifting framework that jointly considers global climate impacts and heterogeneous local criteria. Orca integrates region- and time-dependent signals for carbon, water-stress--weighted water use, air-pollution exposure proxies, and grid-stress indicators, and formulates scheduling as a multi-objective optimization problem. Using Pareto analysis, preference weighting, and optional impact caps, Orca exposes and mitigates trade-offs between emissions reduction and local burdens. A three-region case study shows that CO$_2$-optimal shifting can worsen local outcomes, while Orca produces context-sensitive schedules that better balance global and local sustainability objectives.

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