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

Content Provider	ACM Digital Library
Author	Wang, Weikun Casale, Giuliano
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.
Starting Page	45
Ending Page	47
Page Count	3
File Format	PDF
ISSN	01635999
DOI	10.1145/2825236.2825255
Journal	ACM SIGMETRICS Performance Evaluation Review (PERV)
Volume Number	43
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-10
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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