As society explores new computing applications, data centers have seen a sharp rise in capacity and energy use, driving up data centers’ carbon footprint and raising concerns about their sustainability. In response, efforts to reduce emissions now complement the traditional goals of cutting energy costs and boosting performance. Given the spatiotemporal variability of the grid carbon intensity, researchers are increasingly adopting workload shifting strategies to minimize the operational carbon footprint of cloud workloads. However, shifting from cost- and performance-centric operations to carbon-aware operations introduces performance penalties and cost overheads. Moreover, large-scale spatial and temporal shifts can cause stampede effects, with synchronized migration to the same low-carbon regions or times, thereby straining resources and creating imbalances. In this paper, we quantify the carbon-cost-performance trade-offs of carbon-aware scheduling and evaluate the risk of such stampede effects. We provide real-world examples illustrating these tradeoffs for application and cloud providers. Lastly, we offer practical insights to help mitigate these challenges and guide sustainable scheduling decisions.

Cloud platforms have been focusing on reducing their carbon emissions by shifting workloads across time and locations to when and where low-carbon energy is available. Despite the prominence of this idea, prior work has only quantified the potential of spatiotemporal workload shifting in narrow settings, i.e., for specific workloads in select regions. In particular, there has been limited work on quantifying an upper bound on the ideal and practical benefits of carbon-aware spatiotemporal workload shifting for a wide range of cloud workloads. To address the problem, we conduct a detailed data-driven analysis to understand the benefits and limitations of carbon-aware spatiotemporal scheduling for cloud workloads. We utilize carbon intensity data from 123 regions, encompassing most major cloud sites, to analyze two broad classes of workloads---batch and interactive---and their various characteristics, e.g., job duration, deadlines, and SLOs. Our findings show that while spatiotemporal workload shifting can reduce workloads' carbon emissions, the practical upper bounds of these carbon reductions are currently limited and far from ideal. We also show that simple scheduling policies often yield most of these reductions, with more sophisticated techniques yielding little additional benefit. Notably, we also find that the benefit of carbon-aware workload scheduling relative to carbon-agnostic scheduling will decrease as the energy supply becomes "greener."