Distributed Multi-Agent Systems (DMAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. The utility from maintaining performance is an important component of their survivability. This utility is often met by identifying trade-offs between quality of service and performance. To adaptively choose the operational settings for better utility, we propose an autonomous and scalable queueing theory based methodology to control the performance of a hierarchical network of distributed agents.
['Nathan Gnanasambandam', 'Seokcheon Lee', 'Soundar R. T. Kumara']
An autonomous performance control framework for Distributed Multi-Agent Systems: a queueing theory based approach