Can agents optimize data center cooling?
Multi-Agent Architecture in Distributed Environment Control Systems: vision, challenges, and opportunities
This paper proposes a multi-agent system (MAS) for controlling air-cooled chiller systems in data centers to optimize energy efficiency. Local RL agents manage individual chillers, coordinating through a central layer. A cloud platform analyzes data for long-term trends and retraining. The system aims for decentralized control, enhancing security and scalability. Relevant to LLM-based multi-agent systems are the concepts of local agent autonomy, central coordination, and adaptive retraining based on global analysis. The proposed architecture's on-premises focus aligns with data privacy concerns in LLM applications. Key benefits include reduced energy consumption, improved fault tolerance, and dynamic adaptation to changing conditions. The paper highlights challenges like inter-agent coordination and computational constraints, suggesting solutions like hierarchical control and efficient computational strategies using generative AI.