Graph Learning based Performance Analysis for Queueing Networks

Abstract

Queueing networks serve as a popular performance model in the analysis of business processes and computer systems [4]. Solving queueing network models helps the decision making of system designers. System response time and throughput are two key performance measures in queueing networks. The most widely used algorithms for solving these measures are mean value analysis (MVA) and its approximate extensions [3, §8-9]. However, conventional analytic methods are inaccurate at solving non-product form queueing networks that are frequently encountered in modeling most real systems. Approximation formulas typically rely on assumptions that may lead, on particular regions of the parameters, to inaccurate and misleading results. Simulation modeling is an accurate way, but it requires to be designed for each specific problem, and usually takes longer time to converge.