Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud (2110.13234v1)

Abstract: Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emissions caused by data centers. However, regional differences in the availability of low-carbon energy sources make it hard to provide general best practices for when to consume electricity. Moreover, existing research in this domain focuses mostly on carbon-aware workload migration across geo-distributed data centers, or addresses demand response purely from the perspective of power grid stability and costs. In this paper, we examine the potential impact of shifting computational workloads towards times where the energy supply is expected to be less carbon-intensive. To this end, we identify characteristics of delay-tolerant workloads and analyze the potential for temporal workload shifting in Germany, Great Britain, France, and California over the year 2020. Furthermore, we experimentally evaluate two workload shifting scenarios in a simulation to investigate the influence of time constraints, scheduling strategies, and the accuracy of carbon intensity forecasts. To accelerate research in the domain of carbon-aware computing and to support the evaluation of novel scheduling algorithms, our simulation framework and datasets are publicly available.

Leveraging Temporal Workload Shifting for Carbon Emission Reduction in Cloud Computing

The paper "Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud" by Wiesner et al. investigates the potential benefits of temporal workload shifting within data centers, aiming to align computational demand with periods of low-carbon energy availability. This approach roots in the fluctuating carbon intensity of electricity, which changes based on the energy source mix (renewables versus fossil fuels) and temporal factors such as weather and demand. The research is distinct from carbon-aware geo-scheduling, focusing instead on exploiting temporal variabilities within the same region.

Key Insights and Methodology

The authors explored carbon-saving opportunities by shifting workloads to times with lower expected carbon footprints across four regions: Germany, Great Britain, France, and California, using real energy data from 2020. The simulation and analysis are supported by a public dataset and a simulation framework, which facilitate transparency and reproducibility. The methodology includes analyzing factors like workload characteristics (e.g., duration, execution time, and interruptibility), energy sources' carbon intensity, and temporal forecasts of energy supply variability.

Numerical Results and Claims

Quantitatively, it was shown that:

• Germany, characterized by a high percentage of variable renewables (wind and solar), exhibited significant potential for temporal workload shifting, with carbon intensity fluctuating markedly during the day.
• France, relying heavily on nuclear energy, showed minimal temporal variability in carbon intensity, offering less shifting potential.
• California presents high shifting potential due to reliance on solar power leading to significant day-night carbon intensity changes.
• By experimentally evaluating workload scenarios, the paper demonstrated carbon savings up to 18.9% when leveraging interruptible workloads and semi-weekly scheduling constraints, underlining the misalignments between computational loads and eco-friendly energy availability.

Implications and Future Directions

The findings emphasize that temporal flexibility and interruptibility of workloads can substantially reduce emissions associated with cloud computing, particularly in regions with high renewable penetration. For practical implementation, the authors suggest extending SLAs to allow variable execution timelines and exploiting workload profiling for inherent flexibility characteristics.

With respect to future developments, the research indicates paths for advanced scheduling algorithms that integrate both temporal and spatial considerations for optimized carbon-aware computation. Furthermore, as carbon pricing becomes a global norm, the financial incentives for industries to adapt such scheduling practices may increase, leading to broader adoption.

In summary, this research contributes significantly to sustainability measures within cloud computing by showcasing the tangible benefits of temporal workload alignment with low-carbon energy periods. These findings are critical for the design of sustainable data center operations when coupled with accurate carbon intensity forecasting and intelligent workload management systems.