Decentralized Adaptive Routing for Virtual Circuit Networks Using Stochastic Learning Automata*

The problem of routing virtual circuits according t o dynamic probabilities in virtual circuit packe t switched networks is considered. Queueing network models a r e introduced and performance measures a re defined. A decentralized asynchronous adaptive routing methodology, based on learning automata theory is presented. Every node in the network has a stochastic learning automaton as a router for every destination node. The routing probabilities tha t a re assigned t o the network paths a re updated asynchronously o n the basis of current network conditions. A new learning algorithm suitable for routing is used. Some initial simulation experiments, for a simple network, show convergence t o optimal routing.