Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-23T05:10:13.285Z Has data issue: false hasContentIssue false

Efficient algorithms for transient analysis of stochastic fluid flow models

Published online by Cambridge University Press:  14 July 2016

Soohan Ahn*
Affiliation:
The University of Seoul
V. Ramaswami*
Affiliation:
AT&T Labs
*
Postal address: Department of Statistics, The University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, Seoul, 130-743, South Korea.
∗∗Postal address: AT&T Labs, 180 Park Avenue, E-233, Florham Park, NJ 07932, USA. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We derive several algorithms for the busy period distribution of the canonical Markovian fluid flow model. One of them is similar to the Latouche-Ramaswami algorithm for quasi-birth-death models and is shown to be quadratically convergent. These algorithms significantly increase the efficiency of the matrix-geometric procedures developed earlier by the authors for the transient and steady-state analyses of fluid flow models.

Type
Research Papers
Copyright
© Applied Probability Trust 2005 

References

Ahn, S. and Ramaswami, V. (2003). Fluid flow models and queues—a connection by stochastic coupling. Stoch. Models 19, 325348.CrossRefGoogle Scholar
Ahn, S. and Ramaswami, V. (2004). Transient analysis of fluid flow models via stochastic coupling to a queue. Stoch. Models 20, 71101.CrossRefGoogle Scholar
Anick, D., Mitra, D. and Sondhi, M. M. (1982). Stochastic theory of a data-handling system with multiple sources. Bell System Tech. J. 61, 18711894.Google Scholar
Asmussen, S. (1994). Busy period analysis, rare events and transient behavior in fluid flow models. J. Appl. Math. Stoch. Anal. 7, 269299.Google Scholar
Asmussen, S. (1995). Stationary distributions for fluid flow models with or without Brownian noise. Stoch. Models 11, 120.Google Scholar
Çinlar, E. (1975). Introduction to Stochastic Processes. Prentice Hall, Englewood Cliffs, NJ.Google Scholar
Gaver, D. P. and Lehoczky, J. P. (1982). Channels that cooperatively service a data stream and voice messages. IEEE Trans. Commun. 30, 11531161.Google Scholar
Graham, A. (1981). Kronecker Products and Matrix Calculus: with Applications. John Wiley, New York.Google Scholar
Kobayashi, H. and Ren, Q. (1992). A mathematical theory for transient analysis of communication networks. IEICE Trans. Commun. 12, 12661276.Google Scholar
Latouche, G. (1993). Algorithms for infinite Markov chains with repeating columns. In Linear Algebra, Markov Chains and Queueing Models (IMA Vol. Math. Appl. 48), eds Meyer, C. D. and Plemmons, R. J., Springer, New York, pp. 231265.Google Scholar
Latouche, G. and Ramaswami, V. (1993). A logarithmic reduction algorithm for quasi-birth-and-death processes. J. Appl. Prob. 30, 650674.Google Scholar
Latouche, G. and Ramaswami, V. (1999). Introduction to Matrix Analytic Methods in Stochastic Modeling. Society for Industrial and Applied Mathematics, Philadelphia, PA.Google Scholar
Neuts, M. F. (1981). Matrix-Geometric Solutions in Stochastic Models. An Algorithmic Approach. John Hopkins University Press, Baltimore, MD.Google Scholar
Ortega, J. M. and Rheinboldt, W. C. (1970). Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York.Google Scholar
Ramaswami, V. (1999). Matrix analytic methods for stochastic fluid flows. In Teletraffic Engineering in a Competitive World. (Proc. 16th Internat. Teletraffic Congress), eds Smith, D. and Key, P., Elsevier, New York, pp. 10191030.Google Scholar
Rogers, L. C. G. (1994). Fluid models in queueing theory and Wiener–Hopf factorization of Markov chains. Ann. Appl. Prob. 4, 390413.Google Scholar
Sericola, B. (1998). Transient analysis of stochastic fluid models. Performance Evaluation 32, 245263.Google Scholar