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Decentralized adaptive routing for virtual circuit networks using stochastic learning automata

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3 Author(s)
Economides, A.A. ; Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA, USA ; Ioannou, P.A. ; Silvester, J.A.

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.<>

Published in:

INFOCOM '88. Networks: Evolution or Revolution, Proceedings. Seventh Annual Joint Conference of the IEEE Computer and Communcations Societies, IEEE

Date of Conference:

27-31 March 1988