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

Polling policies have been introduced to simplify the accessing process in client/server systems by a centralized control access scheme. This paper considers a client/server model which employs a polling policy as its access strategy. 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. Under the considered approach, a client’s choice probability asymptotically tends to be proportional to the probability that this client is ready. 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. The benefits are significant for the delay reduction in the considered client/server system. Keywords—client/server systems, polling policies, learning automata, time-delay, throughput improvement.