Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
This addresses the critical need for sustainable data centers to mitigate high energy consumption from machine learning workloads, benefiting governments and corporations, and represents a novel approach rather than an incremental improvement.
The paper tackled the problem of reducing carbon emissions, energy consumption, and costs in data centers by developing a multi-agent reinforcement learning framework that optimizes cooling, load shifting, and energy storage in real-time, achieving reductions of 14.5% in carbon emissions, 14.4% in energy usage, and 13.7% in energy cost compared to an industry standard.
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.