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QMLE: a methodology for statistical inference of service demands from queueing data

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Title: QMLE: a methodology for statistical inference of service demands from queueing data
Authors: Wang, W
Casale, G
Kattepur, A
Nambiar, M
Item Type: Journal Article
Abstract: Estimating the demands placed by services on physical resources is an essential step for the definition of performance models. For example, scalability analysis relies on these parameters to predict queueing delays under increasing loads. In this paper, we investigate maximum likelihood (ML) estimators for demands at load-independent and load-dependent resources in systems with parallelism constraints. We define a likelihood function based on state measurements and derive necessary conditions for its maximization. We then obtain novel estimators that accurately and inexpensively obtain service demands using only aggregate state data. With our approach, and also thanks to approximation methods for computing marginal and joint distributions for the load-dependent case, confidence intervals can be rigorously derived, explicitly taking into account both topology and concurrency levels of the services. Our estimators and their confidence intervals are validated against simulations and real system measurements for two multi-tier applications, showing high accuracy also in the presence of load-dependent resources.
Issue Date: 1-Sep-2018
Date of Acceptance: 15-Jun-2018
URI: http://hdl.handle.net/10044/1/61497
DOI: https://dx.doi.org/10.1145/3233180
ISSN: 2376-3639
Publisher: ACM
Journal / Book Title: ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Volume: 3
Issue: 4
Copyright Statement: © 2018 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, ACM Transactions on Modeling and Performance Evaluation of Computing Systems, Volume 3 Issue 4, September 2018.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: EP/M009211/1
Keywords: Science & Technology
Computer Science, Information Systems
Computer Science
service demand
maximum likelihood
queueing networks
Publication Status: Published
Article Number: ARTN 17
Appears in Collections:Computing
Faculty of Engineering