DECENTRALIZED ADAPTIVE ROUTING FOR VIRTUAL CIRCUIT NETWORKS USING STOCHASTIC LEARNING AUTOMATA.
Date
1988ISBN
0-8186-0833-1Publisher
IEEESource
Proceedings - IEEE INFOCOMProceedings - IEEE INFOCOM '88: Networks. Evolution or Revolution? Seventh Annual Joint Conference of the IEEE Computer and Communications Societies.
Pages
613-622Google Scholar check
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The problem of routing virtual circuits according to dynamic probabilities in virtual-circuit packet-switched networks is considered. Queuing 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.