Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

TL;DR

This paper introduces a hierarchical game-theoretic method for optimizing load allocation across global data centers to reduce carbon emissions while maintaining operational priorities and reliability.

Contribution

It presents a novel bilevel game-theoretic framework that models hierarchical control and operational objectives for sustainable data center management.

Findings

Reduces carbon emissions effectively in simulations.

Ensures operational reliability and priority scheduling.

Demonstrates the approach's effectiveness with real data.

Abstract

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.