A Computational Economy for IN Load Control Using a Multi-Agent System

Intelligent Networks (IN) are used in telecommunication networks to provide services that require a decision-making network element. The Service Control Point (SCP) can be overloaded when the number of service requests exceeds the SCPs designed capacity. Traditional IN load control algorithms assume a single service network model or use a centralized controller to find a solution. In this paper we propose and investigate a market-based model, in the form of a computational economy, for solving the distributed IN load control problem for a multi-service network. We investigate two algorithms, one price-oriented and the other resource-oriented, for finding the competitive equilibrium for this economy. We conclude that the price-oriented approach generally performs better and allows a greater level of distributed-decision making but suffers from an infeasible solution in real-time systems. Furthermore, we study a realization of this model as a multi-agent system (MAS) and investigate the communication overhead associated with running auctions for services.