<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Pomportsis, Andreas S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A learning-automata-based controller for client/server systems</style></title><secondary-title><style face="normal" font="default" size="100%">Neurocomputing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">client/server systems</style></keyword><keyword><style  face="normal" font="default" size="100%">learning automata</style></keyword><keyword><style  face="normal" font="default" size="100%">polling policies</style></keyword><keyword><style  face="normal" font="default" size="100%">throughput improvement</style></keyword><keyword><style  face="normal" font="default" size="100%">time-delay</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">381-394</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Polling policies have been introduced to simplifythe accessing process in client/server systems by acentralized control access scheme. This paper considers aclient/server model which employs a polling policy as itsaccess strategy. We propose a learning-automata-based approachfor polling in order to improve the throughput-delayperformance of the system. Each client has an associatedqueue and the server performs selective polling such thatthe next client to be served is identified by a learning automaton.The learning automaton updates each clientâ€™schoice probability according to the feedback information.Under the considered approach, a clientâ€™s choice probabilityasymptotically tends to be proportional to the probabilitythat this client is ready. Simulation results have shown thatthe proposed polling policy is beneficial in comparison tothe conventional round-robin polling when operating underbursty traffic conditions. The benefits are significant for thedelay reduction in the considered client/server system.&lt;/p&gt;
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