Robust bounds and optimization at the large deviations scale for queueing models via Rényi divergence

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Annals of Applied Probability

سال: 2021

ISSN: ['1050-5164', '2168-8737']

DOI: https://doi.org/10.1214/20-aap1613