Green Scheduling on the Edge

Overview & Synopsis

This work aims at designing and evaluating scheduling algorithms that minimize carbon cost on edge platforms. When a job is released to some edge server, difficult scheduling questions arise: should the job be executed on that server? If yes, when? If no, which other edge server should the job be transferred to? Typically, jobs are submitted online, and have a deadline to enforce. On-line scheduling problems are already difficult without accounting for different energy sources, so one should not expect any optimal solution. Still, an important research goal is to revisit standard algorithms such as Earliest Completion Time (ECT) and Earliest Deadline First (EDF) in order to design and evaluate carbon-aware variants. This paper introduces several new algorithms that use sophisticated scheduling policies to efficiently decrease carbon cost; these algorithms maximize the use of green intervals both on local and remote edge servers, by reevaluating previous decisions whenever needed to accommodate newly released jobs. We provide a comprehensive simulation campaign based on actual platform/job data and carbon traces and report an average gain of 42% over the standard approaches.