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Depending on energy sources and demand, the carbon intensity of the publicpower grid fluctuates over time. Exploiting this variability is an importantfactor in reducing the emissions caused by data centers. However, regionaldifferences in the availability of low-carbon energy sources make it hard toprovide general best practices for when to consume electricity. Moreover,existing research in this domain focuses mostly on carbon-aware workloadmigration across geo-distributed data centers, or addresses demand responsepurely from the perspective of power grid stability and costs.  In this paper, we examine the potential impact of shifting computationalworkloads towards times where the energy supply is expected to be lesscarbon-intensive. To this end, we identify characteristics of delay-tolerantworkloads and analyze the potential for temporal workload shifting in Germany,Great Britain, France, and California over the year 2020. Furthermore, weexperimentally evaluate two workload shifting scenarios in a simulation toinvestigate the influence of time constraints, scheduling strategies, and theaccuracy of carbon intensity forecasts. To accelerate research in the domain ofcarbon-aware computing and to support the evaluation of novel schedulingalgorithms, our simulation framework and datasets are publicly available.

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