GreenScale: Carbon-Aware Systems for Edge Computing
Young Geun Kim, Udit Gupta, Andrew McCrabb, Yonglak Son and, Valeria Bertacco, David Brooks, Carole-Jean Wu

TL;DR
GreenScale is a framework that optimizes application scheduling across edge and cloud systems to reduce carbon emissions by considering renewable energy variability and infrastructure carbon output.
Contribution
It introduces a novel carbon-aware scheduling framework for edge-cloud systems, addressing renewable energy intermittency and providing a roadmap for green application development.
Findings
Carbon emissions reduced by up to 29.1% with GreenScale.
Optimizing for carbon yields different scheduling solutions than performance or energy efficiency.
Framework applicable to AI, gaming, and AR/VR applications.
Abstract
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…
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Taxonomy
TopicsGreen IT and Sustainability · IoT and Edge/Fog Computing · Cloud Computing and Resource Management
