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HITTING PROBABILITIES AND HITTING TIMES FOR STOCHASTIC FLUID FLOWS: THE BOUNDED MODEL

Published online by Cambridge University Press:  13 November 2008

Nigel G. Bean
Affiliation:
Applied Mathematics, University of Adelaide, SA 5005, Australia
Małgorzata M. O'Reilly
Affiliation:
School of Mathematics, University of Tasmania, Tas 7001, Australia E-mail: malgorzata.oreilly@utas.edu.au
Peter G. Taylor
Affiliation:
Department of Mathematics and Statistics, University of Melbourne, Vic 3010, Australia

Abstract

We consider a Markovian stochastic fluid flow model in which the fluid level has a lower bound zero and a positive upper bound. The behavior of the process at the boundaries is modeled by parameters that are different than those in the interior and allow for modeling a range of desired behaviors at the boundaries. We illustrate this with examples. We establish formulas for several time-dependent performance measures of significance to a number of applied probability models. These results are achieved with techniques applied within the fluid flow model directly. This leads to useful physical interpretations, which are presented.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2009

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References

1.Abate, J. & Whitt, W. (1995). Numerical inversion of Laplace transforms of probability distributions. ORSA Journal on Computing 7(1): 3643.CrossRefGoogle Scholar
2.Ahn, S., Badescu, A.L. & Ramaswami, V. (2007). Time dependent analysis of finite buffer fluid flows and risk models with a divident barrier. Queueing Systems 55: 207222.CrossRefGoogle Scholar
3.Ahn, S., Jeon, J. & Ramaswami, V. (2005). Steady state analysis of finite fluid flow models using finite QBDs. Queueing Systems 49(3–4): 223259.CrossRefGoogle Scholar
4.Ahn, S. & Ramaswami, V. (2004). Transient analysis of fluid flow models via stochastic coupling to a queue. Stochastic Models 20(1): 71104.CrossRefGoogle Scholar
5.Ahn, S. & Ramaswami, V. (2005). Efficient algorithms for transient analysis of stochastic fluid flow models. Journal of Applied Probability 42(2): 531549.CrossRefGoogle Scholar
6.Anick, D., Mitra, D. & Sondhi, M.M. (1982). Stochastic theory of data handling system with multiple sources. Bell System Technical Journal 61: 18711894.CrossRefGoogle Scholar
7.Asmussen, S. (1995). Stationary distributions for fluid flow models with or without Brownian noise. Stochastic Models 11: 120.Google Scholar
8.Bean, N.G. & O'Reilly, M.M. (2008). Performance measures of a multi-layer Markovian fluid model. Annals of Operations Research 160: 99120.CrossRefGoogle Scholar
9.Bean, N.G., O'Reilly, M.M. & Sargison, J. Stochastic fluid flows in the operation and maintenance of hydro-power generators. In preparation.Google Scholar
10.Bean, N.G., O'Reilly, M.M. & Taylor, P.G. (2005). Hitting probabilities and hitting times for stochastic fluid flows. Stochastic Processes and Their Applications 115: 15301556.CrossRefGoogle Scholar
11.Bean, N.G., O'Reilly, M.M. & Taylor, P.G. (2005). Algorithms for return probabilities for stochastic fluid flows. Stochastic Models 21(1): 149184.CrossRefGoogle Scholar
12.Bean, N.G., O'Reilly, M.M. & Taylor, P.G. (2008). Algorithms for the Laplace–Stieltjes transforms of the first return probabilities for stochastic fluid flows. Methodology & Computing in Applied Probability 10: 381408.CrossRefGoogle Scholar
13.Elwalid, A.I. & Mitra, D. (1991). Analysis and design of rate-based congestion control of high-speed networks, I: Stochastic fluid models, access regulation. Queueing Systems: Theory and Applications 9: 1964.CrossRefGoogle Scholar
14.Fulks, W. (1978). Advanced calculus. Introduction to analysis. 3rd ed.New York: Wiley.Google Scholar
15.Helfgott, A.E.R., Bean, N.G., Connolly, S., Baird, A. & O'Reilly, M.M. Modelling the resilience of coral reefs to global climate change: A stochastic fluid model of the adaptive bleaching hypothesis on the Great Barrier Reef. In preparation.Google Scholar
16.Mitra, D. (1988). Stochastic theory of a fluid model of procedures and consumers coupled by a buffer. Advances in Applied Probability 20: 646676.CrossRefGoogle Scholar
17.Ramaswami, V. (1999). Matrix analytic methods for stochastic fluid flows. In Proceedings of the 16th International Teletraffic Congress, pp. 10191030.Google Scholar
18.Ramaswami, V. (2006). Passage times in fluid models with application to risk processes. Methodology and Computing in Applied Probability 8: 497515.CrossRefGoogle Scholar
19.Remiche, M.-A. (2005). Compliance of the token bucket model for Markovian type traffic. Stochastic Models 21(2–3): 615630.CrossRefGoogle Scholar
20.Rogers, L.C. (1994). Fluid models in queueing theory and Wiener–Hopf factorization of Markov chains. The Annals of Applied Probability 4(2): 390413.CrossRefGoogle Scholar
21.Silva Soares, A. da & Latouche, G. (2002). Further results on the similarity between fluid queues and QBDs. In Latouche, G. & Taylor, P. (eds.) Matrix-analytic methods: Theory and applications. Singapore: World Scientific, pp. 89106.CrossRefGoogle Scholar
22.Silva Soares, A. da & Latouche, G. (2006). Matrix-analytic methods for fluid queues with finite buffers. Performance Evaluation 63: 295314.CrossRefGoogle Scholar
23.Simonian, A. & Virtamo, J. (1991). Transient and stationary distributions for fluid queues and input processes with a density. SIAM Journal on Applied Mathematics 51: 17321739.CrossRefGoogle Scholar
24.Stern, T.E. & Elwalid, A.I. (1991). Analysis of separable Markov-modulated rate models for information-handling systems. Advances in Applied Probability 23: 105139.CrossRefGoogle Scholar