A Bayesian Approach to Parameter Inference in Queueing Networks

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Title: A Bayesian Approach to Parameter Inference in Queueing Networks
Author(s): Wang, W
Casale, G
Sutton, C
Item Type: Journal Article
Abstract: The application of queueing network models to real-world applications often involves the task of estimating the service demand placed by requests at queueing nodes. In this article, we propose a methodology to estimate service demands in closed multiclass queueing networks based on Gibbs sampling. Our methodology requires measurements of the number of jobs at resources and can accept prior probabilities on the demands. Gibbs sampling is challenging to apply to estimation problems for queueing networks since it requires one to efficiently evaluate a likelihood function on the measured data. This likelihood function depends on the equilibrium solution of the network, which is difficult to compute in closed models due to the presence of the normalizing constant of the equilibrium state probabilities. To tackle this obstacle, we define a novel iterative approximation of the normalizing constant and show the improved accuracy of this approach, compared to existing methods, for use in conjunction with Gibbs sampling. We also demonstrate that, as a demand estimation tool, Gibbs sampling outperforms other popular Markov Chain Monte Carlo approximations. Experimental validation based on traces from a cloud application demonstrates the effectiveness of Gibbs sampling for service demand estimation in real-world studies.
Publication Date: 1-Aug-2016
Date of Acceptance: 14-Feb-2016
URI: http://hdl.handle.net/10044/1/30377
DOI: http://dx.doi.org/10.1145/2893480
ISSN: 1558-1195
Publisher: Association for Computing Machinery
Journal / Book Title: ACM Transactions on Modeling and Computer Simulation
Volume: 27
Issue: 1
Copyright Statement: © 2016 ACM
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 644869
Keywords: Operations Research
Computation Theory And Mathematics
Information Systems
Publication Status: Published
Article Number: 2
Appears in Collections:Faculty of Engineering
Computing



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