Explicit Convergence Rates for the M/G/1 Queue under Perturbation

Chunyang Guo and Yuanyuan Liu

Applied Mathematics and Computation, 2023, vol. 458, issue C

Abstract: This paper establishes convergence rates for discrete-time Markov chains on a countable state space that are stochastically ordered starting from a stationary distribution under perturbation. We investigate the explicit criteria to obtain the ordinary ergodicity, geometric ergodicity and polynomial ergodicity for the embedded M/G/1 queue under perturbation. The explicit geometric convergence rates for the original system and the system under perturbation are caculated. Our bounds in the geometric case and polynomial case are closely connected to the first hitting times. Two examples are provided to illustrate our result.

Keywords: Queues; Markov chains; Coupling method; Ergodicity; Convergence rate (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:458:y:2023:i:c:s0096300323003727

DOI: 10.1016/j.amc.2023.128203

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