Multi-Agent Architecture in Distributed Environment Control Systems: vision, challenges, and opportunities
Natasha Astudillo, Fernando Koch
TL;DR
This paper tackles energy-efficient environmental control in data centers by leveraging a distributed multi-agent architecture for air-cooled chiller fleets. It presents an MAS-based framework with Sensors/IoT, edge-deployed Local Deep RL Agents, a Coordination Layer, a Central Aggregator, and a Cloud Analytics/Re-Training platform to enable on-premises, scalable, and secure operation. Empirical results indicate $5-20\%$ energy savings, $30-40\%$ faster anomaly detection, and up to $30\%$ improvements in predictive maintenance scheduling, validating scalability and robustness across multi-site deployments. The work highlights security, privacy, and interoperability as critical enablers and outlines future directions toward Generative AI, Hybrid AI, IoT standards, decentralized trust, and smart-grid integration.
Abstract
The increasing demand for energy-efficient solutions in large-scale infrastructure, particularly data centers, requires advanced control strategies to optimize environmental management systems. We propose a multi-agent architecture for distributed control of air-cooled chiller systems in data centers. Our vision employs autonomous agents to monitor and regulate local operational parameters and optimize system-wide efficiency. We demonstrate how this approach improves the responsiveness, operational robustness, and energy efficiency of the system, contributing to the broader goal of sustainable infrastructure management.