  To improve the environmental implications of the growing demand of computing,
future applications need to improve the carbon-efficiency of computing
infrastructures. State-of-the-art approaches, however, do not consider the
intermittent nature of renewable energy. The time and location-based carbon
intensity of energy fueling computing has been ignored when determining how
computation is carried out. This poses a new challenge -- deciding when and
where to run applications across consumer devices at the edge and servers in
the cloud. Such scheduling decisions become more complicated with the
stochastic runtime variance and the amortization of the rising embodied
emissions. This work proposes GreenScale, a framework to understand the design
and optimization space of carbon-aware scheduling for green applications across
the edge-cloud infrastructure. Based on the quantified carbon output of the
infrastructure components, we demonstrate that optimizing for carbon, compared
to performance and energy efficiency, yields unique scheduling solutions. Our
evaluation with three representative categories of applications (i.e., AI,
Game, and AR/VR) demonstrate that the carbon emissions of the applications can
be reduced by up to 29.1% with the GreenScale. The analysis in this work
further provides a detailed road map for edge-cloud application developers to
build green applications.
