Designing a learning-automata-based controller for client/server systems: a methodology

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