To mitigate climate change, there has been a recent focus on reducing computing's carbon emissions by shifting its time and location to when and where lower-carbon energy is available. However, despite the prominence of carbon-aware spatiotemporal workload shifting, prior work has only quantified its benefits in narrow settings, i.e., for specific workloads in select regions. As a result, the potential benefits of spatiotemporal workload scheduling, which are a function of both workload and energy characteristics, are unclear. To address the problem, this paper quantifies the upper bound on the benefits of carbon-aware spatiotemporal workload shifting for a wide range of workloads with different characteristics, e.g., job duration, deadlines, SLOs, memory footprint, etc., based on hourly variations in energy's carbon-intensity across 123 distinct regions, including cloud regions, over a year. Notably, while we find that some workloads can benefit from carbon-aware spatiotemporal workload shifting in some regions, the approach yields limited benefits for many workloads and cloud regions. In addition, we also show that simple scheduling policies often yield most of the benefits. Thus, contrary to conventional wisdom, i) carbon-aware spatiotemporal workload shifting is likely not a panacea for significantly reducing cloud platforms' carbon emissions, and ii) pursuing further research on sophisticated policies is likely to yield little marginal benefits.

翻译：为缓解气候变化，近期研究致力于通过将计算任务的时间与地点迁移至低碳能源可用的时空范围，以减少计算的碳排放。然而，尽管碳感知时空工作负载迁移备受关注，先前研究仅限定了其在特定场景（如特定区域中的特定工作负载）下的效益。因此，作为工作负载与能源特性共同作用的结果，时空工作负载调度的潜在效益尚不明确。针对这一问题，本文基于123个不同区域（包括云区域）全年能源碳排放强度的逐时变化，量化了具有不同特性（如作业持续时间、截止时间、SLO、内存占用等）的广泛工作负载在碳感知时空迁移中的效益上限。值得注意的是，我们发现部分工作负载虽能在某些区域受益于碳感知时空迁移，但该方法对多数工作负载和云区域的效益有限。此外，我们还表明，简单的调度策略往往能够获得大部分效益。因此，与普遍认知相反：（1）碳感知时空工作负载迁移很可能并非显著降低云平台碳排放的万能方案；（2）针对复杂调度策略的进一步研究所能带来的边际效益极为有限。