Simple and efficient importance sampling scheme for a tandem queue with server slow-down

D.I. Miretskiy, Willem R.W. Scheinhardt, M.R.H. Mandjes

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Abstract

This paper considers importance sampling as a tool for rare-event simulation. The system at hand is a so-called tandem queue with slow-down, which essentially means that the server of the first queue (or: upstreanm queue) switches to a lower speed when the second queue (downstream queue) exceeds some threshold. The goal is to assess to what extent such a policy succeeds in protecting the first queue, and therefore we focus on estimating the probability of overflow in the downstream queue. It is known that in this setting importance sampling with traditional state-independent distributions performs poorly. More sophisticated state-dependent schemes can be shown to be asymptotically efficient, but their implementation may be problematic, as for each state the new measure has to be computed. This paper presents an algorithm that is considerably simpler than the fully state-dependent scheme; it requires low computational effort, but still has high efficiency.
Original languageUndefined
Title of host publicationProceedings of the 7th Internationals Workshop on Rare Event Simulation, RESIM 2008
Place of PublicationRennes, France
PublisherINRIA
Pages38-49
Number of pages12
ISBN (Print)not assigned
Publication statusPublished - 2008
Event7th International Workshop on Rare Event Simulation, RESIM 2008 - Rennes, France
Duration: 24 Sep 200826 Sep 2008
Conference number: 7

Publication series

Name
PublisherINRIA

Workshop

Workshop7th International Workshop on Rare Event Simulation, RESIM 2008
Abbreviated titleRESIM
CountryFrance
CityRennes
Period24/09/0826/09/08

Keywords

  • IR-69815
  • EWI-17342
  • METIS-264511

Cite this

Miretskiy, D. I., Scheinhardt, W. R. W., & Mandjes, M. R. H. (2008). Simple and efficient importance sampling scheme for a tandem queue with server slow-down. In Proceedings of the 7th Internationals Workshop on Rare Event Simulation, RESIM 2008 (pp. 38-49). Rennes, France: INRIA.