NDLI: Accelerating Performance Inference over Closed Systems by Asymptotic Methods

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Accelerating Performance Inference over Closed Systems by Asymptotic Methods

Content Provider	ACM Digital Library
Author	Casale, Giuliano
Abstract	Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enterprise applications for automated management and performance analysis. In spite of this trend, even simple performance inference problems involving queueing theoretic formulas often incur computational bottlenecks, for example upon computing likelihoods in models of batch systems. Motivated by this issue, we revisit the solution of multiclass closed queueing networks, which are popular models used to describe batch and distributed applications with parallelism constraints. We first prove that the normalizing constant of the equilibrium state probabilities of a closed model can be reformulated exactly as a multidimensional integral over the unit simplex. This gives as a by-product novel explicit expressions for the multiclass normalizing constant. We then derive a method based on cubature rules to efficiently evaluate the proposed integral form in small and medium-sized models. For large models, we propose novel asymptotic expansions and Monte Carlo sampling methods to efficiently and accurately approximate normalizing constants and likelihoods. We illustrate the resulting accuracy gains in problems involving optimization-based inference.
Starting Page	64
Ending Page	64
Page Count	1
File Format	PDF
ISBN	9781450350327
DOI	10.1145/3078505.3078514
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical inference Queueing theory Closed system
Content Type	Text
Resource Type	Article

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