Network resource brokerage by means of distributed agent-based systems encompassing reinforcement learning schemes
The success of a network provider in a competitive communications market depends on the cost-effective provision of the appropriate QoS levels, and the ability to promote the network infrastructure, using dynamic attracting service providers. Advanced management tools encompassing the appropriate intelligence are enabling concepts in this direction. The aim of this paper is to present a part of a Network and Service Management System that acts as a distributed Bandwidth Brokerage System (BBS) over an Internet segment. Aspects addressed are the overall role of the BBS, its functional decoupling, the component deployment pattern in complex (hierarchical) network structures, and the computational intelligence encompassed in the components. The BBS components' logic, which is based on the Stochastic Estimator Learning Algorithm (SELA) concept, solves a version of the hierarchical routing and bandwidth management problem. Mathematical descriptions of the SELA concept and the corresponding routing and bandwidth management schemes are provided. Finally, the paper provides results on the efficiency of the BBS in managing network segments of commercial size and connectivity degree. (C) 2002 Elsevier Science B.V. All rights reserved.
service provider learning algorithms routing foundation for intelligent physical agents