Decentralized adaptive routing for virtual circuit networks using stochastic learning automata

The problem of routing virtual circuits according to dynamical probabilities in virtual-circuit packet-switched networks is considered. Queueing network models are introduced and performance measures are 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 that are assigned to the network paths are updated asynchronously on the basis of current network conditions. A learning algorithm suitable for routing is used. Some initial simulation experiments, for a simple network, show convergence to optimal routing.<>