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Duality results for Markov-modulated fluid flow models

Published online by Cambridge University Press:  14 July 2016

Soohan Ahn
Affiliation:
University of Seoul, Department of Statistics, The University of Seoul, Seoul 130-743, Korea
Vaidyanathan Ramaswami
Affiliation:
AT&T Labs-Research, AT&T Labs-Research, 180 Park Avenue, Florham Park, NJ 07932, USA. Email address: [email protected]
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Abstract

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We establish some interesting duality results for Markov-modulated fluid flow models. Though fluid flow models are continuous-state analogues of quasi-birth-and-death processes, some duality results do differ by the inclusion of a scaling factor.

Type
Part 7. Queueing Theory and Markov Processes
Copyright
Copyright © Applied Probability Trust 2011 

References

[1] Ahn, S. and Ramaswami, V., (2003). Fluid flow models and queues—a connection by stochastic coupling. Stoch. Models 19, 325348.Google Scholar
[2] Ahn, S. and Ramaswami, V., (2004). Transient analysis of fluid flow models via stochastic coupling to a queue. Stoch. Models 20, 71101.Google Scholar
[3] Ahn, S. and Ramaswami, V., (2005). Efficient algorithms for transient analysis of stochastic fluid flow models. J. Appl. Prob. 42, 531549.Google Scholar
[4] Ahn, S. and Ramaswami, V., (2006). Transient analysis of fluid flow via elementary level-crossing arguments. Stoch. Models 22, 129147.Google Scholar
[5] Ahn, S., Badescu, A. L. and Ramaswami, V., (2007). Time dependent analysis of finite buffer fluid flows and risk models with a dividend barrier. Queueing Systems 55, 207222.Google Scholar
[6] Ahn, S., Jeon, J. and Ramaswami, V., (2005). Steady state analysis of finite fluid flow models using finite QBDs. Queueing Systems 49, 223259.Google Scholar
[7] Asmussen, S., (1995). Stationary distributions for fluid flow models with or without Brownian noise. Commun. Statist. Stoch. Models 11, 2149.CrossRefGoogle Scholar
[8] Asmussen, S., (2003). Applied Probability and Queues, 2nd edn. Springer, New York.Google Scholar
[9] Asmussen, S. and Ramaswami, V., (1990). Probabilistic interpretations of some duality results for the matrix paradigms in queueing theory. Commun. Statist. Stoch. Models 6, 715733.Google Scholar
[10] Bean, N. G., O'Reilly, M. M. and Taylor, P. G., (2005). Hitting probabilities and hitting times for stochastic fluid flows. Stoch. Process. Appl. 115, 15301556.Google Scholar
[11] Etter, T., (2001). Dynamical Markov states and the quantum core. Presentation at the Society of Scientific Exploration Conference, San Diego, CA. Available at http://www.boundaryinstitute.org/allowbreak bi/allowbreak articles/allowbreak DynamicalMarkov.pdf.Google Scholar
[12] Latouche, G. and Ramaswami, V., (1999). Introduction to Matrix Analytic Methods in Stochastic Modeling. Society for Industrial and Applied Mathematics, Philadelphia, PA.CrossRefGoogle Scholar
[13] Neuts, M. F., (1981). Matrix-Geometric Solutions in Stochastic Models. The Johns Hopkins University Press, Baltimore, MD.Google Scholar
[14] Ramaswami, V., (1990). A duality theorem for the matrix paradigms in queueing theory. Commun. Statist. Stoch. Models 6, 151161.CrossRefGoogle Scholar
[15] Ramaswami, V., (1999). Matrix analytic methods for stochastic fluid flows. In Teletraffic Engineering in a Competitive World, eds Smith, D. and Key, P., Elsevier, Amsterdam, pp. 10191030.Google Scholar
[16] Ramaswami, V., (2006). Passage times in fluid models with application to risk processes. Methodology Comput. Appl. Prob. 8, 497515.Google Scholar
[17] 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. 42, 210225.CrossRefGoogle Scholar