Learning to Be Green: Carbon-Aware Online Control for Edge Intelligence with Colocated Learning and Inference

Abstract

Edge intelligence is an emerging paradigm that leverages edge computing to pave the last mile delivery of artificial intelligence. While pilot efforts on edge intelligence have mostly focused on the performance and power issues, the sustainability dilemma along with the upcoming carbon peaking and neutrality era has largely been overlooked. To green edge intelligence, we propose a carbon-aware online control framework (CARE) in this paper. CARE colocates learning and inference tasks within an edge node and dynamically adapts their configurations based on the temporal variation of carbon intensity and renewable energy availability. With such a colocation setup, CARE aims to minimize the long-term inference accuracy loss under the long-term carbon emission cap. The underlying long-term optimization problem is nontrivial since it involves uncertain information (e.g., renewable energy availability) and is NP-hard. To address these dual challenges, CARE first designs an online learning module to make fractional decisions by learning from previous system dynamics and configuration adaptation results. Then, CARE further designs a randomized rounding module, which converts the fractional decision into integer without violating the long-term carbon emission cap. The effectiveness of CARE is verified by rigorous theoretical analysis and extensive trace-driven simulations.

Topics & Concepts