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Evergreen: Comprehensive Carbon Modeling for Performance-Emission Tradeoffs
DescriptionThe pervasive proliferation of computing infrastructure in recent decades has led to an increased fraction of worldwide energy consumption and greenhouse gas (GHG) emissions associated with computing. Such contributions are projected to increase quickly. Traditionally, computing research has been primarily focused on performance, power, and area optimization, with a much lower emphasis on the carbon footprint (CF) associated with computations. Hence, more holistic techniques are needed to mitigate Information Communication Technology's GHG emissions. To address this need, we propose Evergreen, a three-part approach comprised of (1) a holistic model of operational and embodied emissions of compute hardware, transmission infrastructure, and battery energy storage systems, (2) a CF predictor based on this model, and (3) a user-driven, carbon-aware scheduler to minimize GHG emissions of workloads on cloud environments. To the best of our knowledge, this work proposes the most holistic model and corresponding scheduler so far.

Using a case study, we demonstrate that Evergreen can reduce emissions by 19.6x with carbon-optimal scheduling compared to latency-optimal scheduling in data centers with only a 2.1% latency overhead.
Event Type
Work-in-Progress Poster
TimeWednesday, June 266:00pm - 7:00pm PDT
LocationLevel 2 Lobby
Topics
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security