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March 2011 Bayesian inference for queueing networks and modeling of internet services
Charles Sutton, Michael I. Jordan
Ann. Appl. Stat. 5(1): 254-282 (March 2011). DOI: 10.1214/10-AOAS392


Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where each queue models one of the computers in the system. A key challenge is that the data are incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo. We evaluate our framework on data from a benchmark Web application. We also present a simple technique for selection among nested queueing models. We are unaware of any previous work that considers inference in networks of queues in the presence of missing data.


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Charles Sutton. Michael I. Jordan. "Bayesian inference for queueing networks and modeling of internet services." Ann. Appl. Stat. 5 (1) 254 - 282, March 2011.


Published: March 2011
First available in Project Euclid: 21 March 2011

zbMATH: 1220.62024
MathSciNet: MR2810397
Digital Object Identifier: 10.1214/10-AOAS392

Keywords: latent-variable models , Markov chain Monte Carlo , performance modeling , Queueing networks , Web applications

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.5 • No. 1 • March 2011
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