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On geometric and algebraic transience for block-structured Markov chains

Published online by Cambridge University Press:  23 November 2020

Xiuqin Li*
Affiliation:
Central South University
*
*Postal address: School of Mathematics and Statistics, Central South University, Changsha, Hunan, 410075, China.

Abstract

Block-structured Markov chains model a large variety of queueing problems and have many important applications in various areas. Stability properties have been well investigated for these Markov chains. In this paper we will present transient properties for two specific types of block-structured Markov chains, including M/G/1 type and GI/M/1 type. Necessary and sufficient conditions in terms of system parameters are obtained for geometric transience and algebraic transience. Possible extensions of the results to continuous-time Markov chains are also included.

Type
Research Papers
Copyright
© Applied Probability Trust 2020

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References

Anderson, W. J. (1991). Continuous-Time Markov Chain: An Applications-Oriented Approach. Springer, New York.10.1007/978-1-4612-3038-0CrossRefGoogle Scholar
Asmussen, S. (2003). Applied Probability and Queues, 2nd edn. Springer.Google Scholar
Bean, N. G., Bright, L., Latouche, G., Pearce, C. E. M., Pollett, P. K. and Taylor, P. G. (1997). The quasi-stationary behavior of quasi-birth-and-death processes. Ann. Appl. Prob. 7, 134155.Google Scholar
Bini, D. A., Latouche, G. and Meini, B. (2005). Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation). Oxford Science Publications, Oxford University Press, New York.Google Scholar
Chen, M. F. (2004). From Markov Chains to Non-Equilibrium Particle Systems, 2nd edn. World Scientific, Singapore.10.1142/5513CrossRefGoogle Scholar
Choi, B. D. and Kim, B. (2004). Non-ergodicity criteria for denumerable continuous time Markov processes. Operat. Res. Lett. 32, 575580,10.1016/j.orl.2004.03.001CrossRefGoogle Scholar
Debreu, G. and Herstein, I. N. (1953). Nonnegative square matrices. Econometrica 4, 597607.10.2307/1907925CrossRefGoogle Scholar
Gail, H. R., Hantler, S. L. and Taylor, B. A. (1996). Spectral analysis of M/G/1 and G/M/1 type Markov chains. Adv. Appl. Prob. 28, 114165.10.2307/1427915CrossRefGoogle Scholar
Goodman, J. B. and Massey, W. A. (1984). The non-ergodic Jackson network. J. Appl. Prob. 21, 860869.10.2307/3213702CrossRefGoogle Scholar
Hou, Z. and Liu, Y. (2004). Explicit criteria for several types of ergodicity of the embedded M/G/1 and GI/M/n queues. J. Appl. Prob. 41, 778790.10.1239/jap/1091543425CrossRefGoogle Scholar
Hou, Z., Liu, Y. and Zhang, H. (2005). Subgeometric rates of convergence for a class of continuous-time Markov process. J. Appl. Prob. 42, 698712.10.1239/jap/1127322021CrossRefGoogle Scholar
Kijima, M. (1993). Quasi-stationary distributions of single-server phase-type queues. Math. Operat. Res. 18, 423437.10.1287/moor.18.2.423CrossRefGoogle Scholar
Latouche, G. and Taylor, P. G. (2003). Drift conditions for matrix-analytic models. Math. Operat. Res. 28, 346360.10.1287/moor.28.2.346.14475CrossRefGoogle Scholar
Li, Q. L. and Zhao, Y. Q. (2002). A constructive method for finding $\beta$-invariant measures for transition matrices of M/G/1 type. In Matrix-Analytic Methods: Theory and Applications, eds G. Latouche and P. Taylor, pp. 237263. World Scientific.10.1142/9789812777164_0014CrossRefGoogle Scholar
Li, Q. L. and Zhao, Y. Q. (2003). $\beta$-invariant measures for transition matrices of GI/M/1 type. Stoch. Models. 19, 201233.10.1081/STM-120020387CrossRefGoogle ScholarPubMed
Li, W., Liu, Y. and Zhao, Y. Q. (2019). Exact tail asymptotics for fluid models driven by an M/M/c queue. Queueing Syst. 91, 319346.10.1007/s11134-019-09601-6CrossRefGoogle Scholar
Liu, Y. (2012). Perturbation bounds for the stationary distribution of Markov chains. SIAM J. Matrix Anal. Appl. 33, 10571074.10.1137/110838753CrossRefGoogle Scholar
Liu, Y. and Hou, Z. (2006). Several types of ergodicity for M/G/1-type Markov chains and Markov processes. J. Appl. Prob. 43, 141158.10.1239/jap/1143936249CrossRefGoogle Scholar
Liu, Y. and Li, W. (2018). Error bounds for augmented truncation approximations of Markov chains via the perturbation method. Adv. Appl. Prob. 50, 645669.10.1017/apr.2018.28CrossRefGoogle Scholar
Liu, Y. and Li, Y. (2019). V-uniform ergodicity for fluid queues. Appl. Math. Ser. B 34, 8291.10.1007/s11766-019-3543-2CrossRefGoogle Scholar
Liu, Y., Li, W. and Masuyama, H. (2018). Error bounds for augmented truncation approximations of continuous-time Markov chains. Operat. Res. Lett. 46, 409413.10.1016/j.orl.2018.05.001CrossRefGoogle Scholar
Liu, Y., Zhang, H. and Zhao, Y. Q. (2010). Subgeometric ergodicity for continuous-time Markov chains. J. Math. Anal. Appl. 368, 178189.10.1016/j.jmaa.2010.03.019CrossRefGoogle Scholar
Mao, Y. H. and Song, Y. H. (2014). On geometric and algebraic transience for discrete-time Markov chains. Stoch. Proc. Appl. 124, 16481678.10.1016/j.spa.2013.12.012CrossRefGoogle Scholar
Mao, Y. H., Tai, Y. M., Zhao, Y. Q. and Zou, J. Z. (2014). Ergodicity for the GI/G/1-type Markov chain. J. Appl. Prob. Statist. 9, 3144.Google Scholar
Masuyama, H. (2019). A sequential update algorithm for computing the stationary distribution vector in upper block-Hessenberg Markov chains. Queueing Syst. 92, 173200.10.1007/s11134-019-09599-xCrossRefGoogle Scholar
Meyn, S. P. and Tweedie, R. L. (2009). Markov Chains and Stochastic Stability, 2nd edn. Springer.10.1017/CBO9780511626630CrossRefGoogle Scholar
Morozov, E. (2002). Instability of nonhomogeneous queueing networks. J. Math. Sci. 112, 41554167.10.1023/A:1020272405385CrossRefGoogle Scholar
Neuts, M. F. (1981). Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach. Johns Hopkins University Press, Baltimore, MD.Google Scholar
Neuts, M. F. (1989). Structured Stochastic Matrices of M/G/1 Type and Their Applications. Marcel Dekker, New York.Google Scholar
Ramaswami, V. (1990). A duality theorem for the matrix paradigms in queueing theory. Stoch. Models 6, 151161.10.1080/15326349908807141CrossRefGoogle Scholar
Seneta, E. (2006). Non-Negative Matrices and Markov Chains, 2nd edn. Springer, New York.Google Scholar
Taylor, P. G. and van Houdt, B. (2010). On the dual relationship between Markov chains of GI/M/1 and M/G/1 type. Adv. Appl. Prob. 40, 210225.10.1239/aap/1269611150CrossRefGoogle Scholar
Zhao, Y. Q., Li, W. and Alfa, A. S. (1999). Duality results for block-structured transition matrices. J. Appl. Prob. 36, 10451057.10.1239/jap/1032374754CrossRefGoogle Scholar