Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers

-- 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. The use of data centers to support Internet applications There are several reasons to favor geographically distributed is growing rapidly, with experts predicting increased data centers as they provide advantages in load balancing, reliability reliance on data centers fueled by Artificial Intelligence and redundancy, latency reduction, and compliance (AI)-driven workloads, new cloud services, demand for with data sovereignty rules [11]. Another compelling reason edge applications, expansion of the Internet of Things (IoT) for geographically spreading data centers is to reduce electricity devices, and the proliferation of technologies such as 5G/6G. AI workloads require immense [12], electricity prices fluctuate depending on the time of day. They also necessitate vast amounts of data for costs rise; whereas when the demand is low, they decline [13]. As a result, AI-driven computing cost on non-residential or commercial customers depending is fueling demand for more power-hungry data centers with on the maximum (peak) power utilized at any given moment expanded storage capabilities [2].