Fair-CO2: Fair Attribution for Cloud Carbon Emissions
Leo Han, Jash Kakadia, Benjamin C. Lee, Udit Gupta
摘要
Fair-CO 2 is a system for fairly attributing operational and embodied carbon in cloud data centers to user workloads. It leverages the Shapley value, a game theory solution for fair shared cost attribution with theoretical fairness guarantees. We propose the standard Shapley value solution as a ground truth for attribution in cloud data centers, addressing two key gaps in existing carbon attribution methods that lead to unfair attributions: the effect of dynamic demand on embodied carbon, and interference effects on carbon attribution in colocated scenarios. However, the computational cost of the Shapley value solution scales exponentially with the number of workloads and becomes intractable for large systems. Using Monte Carlo simulations of different workload schedules and colocation scenarios, we show that Fair-CO 2 can approximate the ground truth Shapley attribution solution at scale. Fair-CO 2 comprises two core components: Temporal Shapley attribution that applies the Shapley value to demand-aware embodied carbon attribution with low computation complexity and an interference-aware resource cost attribution method. We also show how users, once provided a fair way of estimating their workload carbon footprint, can dynamically optimize workload deployment for carbon savings.
• Hardware → Impact on the environment; Enterprise level and data centers power issues; • Computer systems organization → Cloud computing.
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