Performance optimization and regulation for multitier servers

In this paper we present a novel approach to optimize and regulate performance in a multitier server. By using a queueing network model, we optimize the values of the mean service rates at each tier in the server by applying randomized algorithms based on statistical learning theory. After the optimization process is carried out, an IPA algorithm is implemented to regulate the throughput of the system to the calculated optimal reference value in a closed loop. The case study of a server with three tiers is simulated to validate our approach.