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

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  • Guo, Chunyang
  • Liu, Yuanyuan

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.

Suggested Citation

  • Guo, Chunyang & Liu, Yuanyuan, 2023. "Explicit Convergence Rates for the M/G/1 Queue under Perturbation," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    • Handle: RePEc:eee:apmaco:v:458:y:2023:i:c:s0096300323003727
    DOI: 10.1016/j.amc.2023.128203
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    References listed on IDEAS

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    1. Mao, Yong-Hua, 2006. "Convergence rates in strong ergodicity for Markov processes," Stochastic Processes and their Applications, Elsevier, vol. 116(12), pages 1964-1976, December.
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