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DECENTRALIZED ADAPTIVE ROUTING FOR VIRTUAL CIRCUIT NETWORKS USING STOCHASTIC LEARNING AUTOMATA.

dc.contributor.authorEconomides, Anastasios A.en
dc.contributor.authorIoannou, Petros A.en
dc.contributor.authorSilvester, John A.en
dc.creatorEconomides, Anastasios A.en
dc.creatorIoannou, Petros A.en
dc.creatorSilvester, John A.en
dc.date.accessioned2019-12-02T10:34:57Z
dc.date.available2019-12-02T10:34:57Z
dc.date.issued1988
dc.identifier.isbn0-8186-0833-1
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/56773
dc.description.abstractThe 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.en
dc.publisherIEEEen
dc.sourceProceedings - IEEE INFOCOMen
dc.sourceProceedings - IEEE INFOCOM '88: Networks. Evolution or Revolution? Seventh Annual Joint Conference of the IEEE Computer and Communications Societies.en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0023866696&partnerID=40&md5=a5907c4addcec48267c2c051ac868aed
dc.subjectAUTOMATA THEORYen
dc.subjectDECENTRALIZED ADAPTIVE ROUTINGen
dc.subjectDIGITAL COMMUNICATION SYSTEMSen
dc.subjectMATHEMATICAL TECHNIQUES - Graph Theoryen
dc.subjectPROBABILITY - Queueing Theoryen
dc.subjectQUEUEING NETWORK MODELSen
dc.subjectSTOCHASTIC LEARNING AUTOMATAen
dc.subjectSYSTEMS SCIENCE AND CYBERNETICS - Learning Systemsen
dc.subjectVIRTUAL CIRCUIT NETWORKSen
dc.titleDECENTRALIZED ADAPTIVE ROUTING FOR VIRTUAL CIRCUIT NETWORKS USING STOCHASTIC LEARNING AUTOMATA.en
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.startingpage613
dc.description.endingpage622
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeConference Objecten
dc.description.notesIEEE, Communications Soc, New York, NY, USAen
dc.description.notesConference code: 11541en
dc.description.notesCited By :19</p>en
dc.description.notes<p>Sponsors: IEEE, Computer Soc, Los Alamitos, CA, USAes
dc.contributor.orcidEconomides, Anastasios A. [0000-0001-8056-1024]
dc.contributor.orcidIoannou, Petros A. [0000-0001-6981-0704]
dc.gnosis.orcid0000-0001-8056-1024
dc.gnosis.orcid0000-0001-6981-0704


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