Facilitating Numerical Solutions of Inhomogeneous Continuous Time Markov Chains Using Ergodicity Bounds Obtained with Logarithmic Norm Method

Alexander Zeifman, Yacov Satin, Ivan Kovalev, Rostislav Razumchik and Victor Korolev
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Alexander Zeifman: Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova 44-2, 119333 Moscow, Russia
Yacov Satin: Department of Applied Mathematics, Vologda State University, Lenina 15, 160000 Vologda, Russia
Ivan Kovalev: Department of Applied Mathematics, Vologda State University, Lenina 15, 160000 Vologda, Russia
Rostislav Razumchik: Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova 44-2, 119333 Moscow, Russia
Victor Korolev: Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova 44-2, 119333 Moscow, Russia

Mathematics, 2020, vol. 9, issue 1, 1-20

Abstract: The problem considered is the computation of the (limiting) time-dependent performance characteristics of one-dimensional continuous-time Markov chains with discrete state space and time varying intensities. Numerical solution techniques can benefit from methods providing ergodicity bounds because the latter can indicate how to choose the position and the length of the “distant time interval” (in the periodic case) on which the solution has to be computed. They can also be helpful whenever the state space truncation is required. In this paper one such analytic method—the logarithmic norm method—is being reviewed. Its applicability is shown within the queueing theory context with three examples: the classical time-varying M / M / 2 queue; the time-varying single-server Markovian system with bulk arrivals, queue skipping policy and catastrophes; and the time-varying Markovian bulk-arrival and bulk-service system with state-dependent control. In each case it is shown whether and how the bounds on the rate of convergence can be obtained. Numerical examples are provided.

Keywords: continuous-time Markov chains; ergodicity bounds; discrete state space; rate of convergence; logarithmic norm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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