Importance sampling for a Markov modulated queuing network

Date

2009-02-01

Author

Sezer, Ali Devin

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Importance sampling (IS) is a variance reduction method for simulating rare events. A recent paper by Dupuis, Wang and Sezer [Paul Dupuis, Ali Devin Sezer, Hui Wang, Dynamic importance sampling for queueing networks, Annals of Applied Probability 17 (4) (2007) 1306-1346] exploits connections between IS and stochastic games and optimal control problems to show how to design and analyze simple and efficient IS algorithms for various overflow events of tandem Jackson Networks. The present paper carries out a program parallel to the paper by Dupuis et al. for a two node tandem network whose arrival and service rates are modulated by in exogenous finite state Markov process. The overflow event we study is the following: the number of customers in the system reaches n without the system ever becoming empty, given that initially the system is empty.

Subject Keywords

Dynamic importance sampling, Rare event simulation, Tandem queues, Queuing networks, Markov modulated, Regime switch, Overflow probability, Large deviations, Isaacs equation, Optimal control

URI

https://hdl.handle.net/11511/30526

Journal

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

DOI

https://doi.org/10.1016/j.spa.2008.02.009

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

A. D. Sezer, “Importance sampling for a Markov modulated queuing network,” STOCHASTIC PROCESSES AND THEIR APPLICATIONS, pp. 491–517, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30526.