Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

TL;DR

This paper introduces a multi-agent reinforcement learning framework for real-time optimization of data center operations, significantly reducing carbon footprint, energy consumption, and costs amid dynamic environmental and grid conditions.

Contribution

The paper presents a novel MARL-based control system that effectively manages cooling, load shifting, and energy storage for sustainable data centers in real-time.

Findings

Achieved 14.5% reduction in carbon emissions.

Reduced energy usage by 14.4%.

Lowered energy costs by 13.7%.

Abstract

As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.