Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Weikun Wang, Giuliano Casale

Abstract: We propose maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent stations. Our ML estimators are expressed in implicit form and require only to compute mean queue lengths and marginal queue length probabilities from an empirical dataset. Further, in the load-independent case, we provide an explicit approximate formula for the ML estimator together with confidence intervals.