In this paper, we investigate the potential of spatial and temporal cloud workload shifting to reduce carbon, water, and land use footprints. Specifically, we perform a simulation study leveraging publicly available data on the cloud infrastructure of major providers (AWS and Azure) as well as real-world workload traces (big data analytics and FaaS) and grid mix data to consider two different scenarios. Our simulation results indicate that spatial shifting can substantially lower carbon, water, and land use footprints. In the FaaS applications, shifting the spatiotemporal workload achieves carbon savings of up to 85%, water savings of around 50%, and reductions in land use of up to 45%, all while optimizing for the respective factors. Mixed optimization yields results comparable to those of land use alone. For big data workloads, spatiotemporal shifting delivers reductions of up to 45% in carbon emissions, 40% in water consumption, and nearly 40% in land use when optimized for the respective factors. Temporal shifting also decreases the footprint, though to a lesser extent. When applied together, the two strategies yield the greatest overall reduction, driven mainly by spatial shifting with temporal adjustments providing an additional, incremental benefit. Sensitivity analysis demonstrates that such shifting is robust to prediction errors in grid mix data and to variations across different seasons.

翻译：本文研究了通过时空迁移云工作负载以降低碳、水及土地利用足迹的潜力。具体而言，我们基于主要云服务提供商（AWS与Azure）的公开基础设施数据、真实工作负载追踪（大数据分析与FaaS）及电网结构数据，通过模拟研究探讨了两种不同场景。模拟结果表明，空间迁移能显著降低碳、水及土地利用足迹。在FaaS应用中，针对各因子优化后的时空工作负载迁移可实现高达85%的碳减排、约50%的节水效益以及最高45%的土地利用减少。混合优化所得结果与单独优化土地利用时相近。对于大数据工作负载，针对各因子优化后的时空迁移可实现高达45%的碳排放降低、40%的用水减少及近40%的土地利用缩减。时间迁移同样能降低足迹，但效果相对有限。当两种策略结合使用时，空间迁移主导的整体减排效果最为显著，时间调整则提供额外的渐进效益。敏感性分析表明，此类迁移策略对电网结构数据的预测误差及不同季节的波动具有稳健性。