  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.
