A Learning Model for Routing in Telephone Networks

Abstract

The aim of this paper is to develop a theory of adaptive routing in telephone networks using learning methods. A mathematical model of the network with slow-learning algorithms distributed at various nodes is presented. The algorithms update the routing probabilities on the basis of network feedback information (like call blocking or completion) only. Convergence of the routing strategies is established. Two linear updating algorithms, under certain conditions, are shown to have desirable equilibrium behavior like load equalization and minimum blocking probability for the entire network.