  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".
