Our paper Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data;, co-authored by W. Wang, G. Casale, A. Kattepur and M. Nambiar has received the best paper award at ICPE2016, the IEEE International Conference on Performance Engineering!
The paper proposes maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent stations. The obtained ML estimators are expressed in implicit form and require only to compute mean queue lengths and marginal queue length probabilities from an empirical dataset.
An open dataset for this paper is available on Zenodo.
QORE
Quality of Service Research Lab
