A Framework for Performance Control of Distributed Autonomous Agents

We propose an autonomous and scalable queueing theory-based methodology to control the performance of a hierarchical network of distributed agents. Multi-agent systems (MAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. Hence, the objective of the control framework is to identify the trade-offs between quality and performance and adaptively choose the operational settings to posture the MAS for better utility. By formulating the MAS as an open queueing network with multiple classes of traffic we evaluate the performance and subsequently the utility, from which we identify the control alternative for a localized, multi-tier zone.