Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions
  and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
Ninad Hogade Sudeep Pasricha
Data centers are increasingly using more energy due to the rise in Artificial
Intelligence (AI) workloads, which negatively impacts the environment and
raises operational costs. Reducing operating expenses and carbon emissions
while maintaining performance in data centers is a challenging problem. This
work introduces a unique approach combining Game Theory (GT) and Deep
Reinforcement Learning (DRL) for optimizing the distribution of AI inference
workloads in geo-distributed data centers to reduce carbon emissions and cloud
operating (energy + data transfer) costs. The proposed technique integrates the
principles of non-cooperative Game Theory into a DRL framework, enabling data
centers to make intelligent decisions regarding workload allocation while
considering the heterogeneity of hardware resources, the dynamic nature of
electricity prices, inter-data center data transfer costs, and carbon
footprints. We conducted extensive experiments comparing our game-theoretic DRL
(GT-DRL) approach with current DRL-based and other optimization techniques. The
results demonstrate that our strategy outperforms the state-of-the-art in
reducing carbon emissions and minimizing cloud operating costs without
compromising computational performance. This work has significant implications
for achieving sustainability and cost-efficiency in data centers handling AI
inference workloads across diverse geographic locations.