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Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
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.
A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management
We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues.
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