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June 2021 Robust bounds and optimization at the large deviations scale for queueing models via Rényi divergence
Rami Atar, Amarjit Budhiraja, Paul Dupuis, Ruoyu Wu
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Ann. Appl. Probab. 31(3): 1061-1099 (June 2021). DOI: 10.1214/20-AAP1613

Abstract

This paper develops tools to obtain robust probabilistic estimates for queueing models at the large deviations (LD) scale. These tools are based on the recently introduced robust Rényi bounds, which provide LD estimates (and more generally risk-sensitive (RS) cost estimates) that hold uniformly over an uncertainty class of models, provided that the class is defined in terms of Rényi divergence with respect to a reference model and that estimates are available for the reference model. One very attractive quality of the approach is that the class to which the estimates apply may consist of hard models, such as highly non-Markovian models and ones for which the LD principle is not available. Our treatment provides exact expressions as well as bounds on the Rényi divergence rate on families of marked point processes, including as a special case renewal processes. Another contribution is a general result that translates robust RS control problems, where robustness is formulated via Rényi divergence, to finite dimensional convex optimization problems, when the control set is a finite dimensional convex set. The implications to queueing are vast, as they apply in great generality. This is demonstrated on two non-Markovian queueing models. One is the multiclass single-server queue considered as a RS control problem, with scheduling as the control process and exponential weighted queue length as cost. The second is the many-server queue with reneging, with the probability of atypically large reneging count as performance criterion. As far as LD analysis is concerned, no robust estimates or non-Markovian treatment were previously available for either of these models.

Citation

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Rami Atar. Amarjit Budhiraja. Paul Dupuis. Ruoyu Wu. "Robust bounds and optimization at the large deviations scale for queueing models via Rényi divergence." Ann. Appl. Probab. 31 (3) 1061 - 1099, June 2021. https://doi.org/10.1214/20-AAP1613

Information

Received: 1 January 2020; Revised: 1 July 2020; Published: June 2021
First available in Project Euclid: 23 June 2021

Digital Object Identifier: 10.1214/20-AAP1613

Subjects:
Primary: 60F10, 60K25, 94A17

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.31 • No. 3 • June 2021
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