Real-time Carbon Footprint Minimization in Sustainable Data Centers wth Reinforcement Learning (Papers Track) Best ML Innovation
Soumyendu Sarkar (Hewlett Packard Enterprise); Avisek Naug (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Cullen Bash (HPE)
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. There is a pressing need to optimize energy usage in these centers, especially considering factors like cooling, balancing flexible load based on renewable energy availability, and battery storage utilization. The challenge arises due to the interdependencies of these strategies with fluctuating external factors such as weather and grid carbon intensity. Although there's currently no real-time solution that addresses all these aspects, our proposed Data Center Carbon Footprint Reduction (DC-CFR) framework, based on multi-agent Reinforcement Learning (MARL), targets carbon footprint reduction, energy optimization, and cost. Our findings reveal that DC-CFR's MARL agents efficiently navigate these complexities, optimizing the key metrics in real-time. DC-CFR reduced carbon emissions, energy consumption, and energy costs by over 13% with EnergyPlus simulation compared to the industry standard ASHRAE controller controlling HVAC for a year in various regions.