A client/server model which employs a polling policy as its access strategy is considered. We propose a learning-automata-based approach for polling in order to improve the throughput-delay performance of the system. Each client has an associated queue and the server performs selective polling such that the next client to be served is identified by a learning automaton. The learning automaton updates each client's choice probability according to the feedback information. Simulation results have shown that the proposed polling policy is beneficial in comparison to the conventional round-robin polling when operating under bursty traffic conditions.
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