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

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Title: Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
Authors: Wang, W
Casale, G
Ajay Kattepur
Manoj Nambiar
Item Type: Conference Paper
Abstract: Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application.
Issue Date: 12-Mar-2016
Date of Acceptance: 17-Nov-2015
ISBN: 978-1-4503-4080-9
Publisher: ACM
Start Page: 3
End Page: 14
Journal / Book Title: Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering
Copyright Statement: © 2016 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is available at
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 644869
Conference Name: 7th ACM/SPEC International Conference on Performance Engineering
Publication Status: Published
Start Date: 2016-03-12
Finish Date: 2016-03-18
Conference Place: Delft
Appears in Collections:Faculty of Engineering

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