The content delves into the potential of spatial and temporal workload shifting to reduce carbon emissions in cloud computing. It highlights the challenges and benefits of such strategies, emphasizing the importance of understanding regional variations in carbon intensity for effective optimization.
Cloud platforms aim to reduce their carbon footprint by shifting workloads across time and locations to leverage low-carbon energy sources. The study analyzes data from 123 regions to quantify the upper bounds of carbon reduction through spatiotemporal scheduling for different types of cloud workloads. While there is potential for reducing emissions, practical constraints and diminishing returns from sophisticated policies pose challenges.
The analysis reveals that simple scheduling policies can yield significant reductions in carbon emissions, with more complex techniques offering marginal additional benefits. The study emphasizes the need for informed decision-making based on regional energy profiles to optimize carbon efficiency effectively in cloud computing environments.
Key findings include insights on global variations in carbon intensity, trends in grid energy's carbon-intensity changes over time, and periodicity patterns influencing workload flexibility. The content underscores the importance of considering capacity constraints, latency requirements, and geographical groupings when implementing spatial and temporal workload shifting strategies.
Overall, the study provides valuable insights into optimizing cloud workloads for sustainability by leveraging carbon-aware scheduling techniques effectively.
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arxiv.org
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