skip to main content
research-article

Efficient importance sampling schemes for a feed-forward network

Published:16 December 2013Publication History
Skip Abstract Section

Abstract

The aim of this article is to construct efficient importance sampling schemes for a rare event, namely, the buffer overflow associated with a feed-forward network with discontinuous dynamics. This is done through a piecewise constant change of measure, which is based on a suitably constructed subsolution to an HJB equation. The main task is to change the measure such that the logarithmic asymptotic optimality is achieved. To that end, we find an upper bound on the second moment of the importance sampling estimator that yields optimality. Numerical simulations illustrate the validity of theoretical results.

References

  1. Asmussen, S. 2000. Ruin Probabilities. World Scientific, Singapore.Google ScholarGoogle Scholar
  2. Blanchet, J. H. and Glynn, P. W. 2008. Efficient rare-event simulation for the maximum of heavy-tailed random walks. Ann. Appl. Prob. 18, 1351--1378.Google ScholarGoogle ScholarCross RefCross Ref
  3. Blanchet, J. H., Glynn, P. W., and Liu, J. 2007. Fluid heuristics, Lyapunov bounds and efficient importance sampling for a heavy-tailed G/G/1 queue. QUESTA 57, 99--113. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chung, K. L. 1974. A Course in Probability Theory (2nd ed.). Academic Press, New York.Google ScholarGoogle Scholar
  5. De Boer, P. T. 2006. Analysis of state-independent importance sampling measures for the two-node tandem queue. ACM Trans. Modeling Comp. Simulation 16, 225--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dupuis, P. and Ellis, R. 1997. A Weak Convergence Approach to the Theory of Large Deviations. John Wiley & Sons, New York.Google ScholarGoogle Scholar
  7. Dupuis, P., Ellis, R., and Weiss, A. 1991. Large deviations for Markov processes with discontinuous statistics, I: General upper bounds. Ann. Probab. 19, 1280--1297.Google ScholarGoogle ScholarCross RefCross Ref
  8. Dupuis, P., Leder, K., and Wang, H. 2007. Large deviations and importance sampling for a tandem network with slow-down. QUESTA 57, 71--83. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dupuis, P., Leder, K., and Wang, H. 2009. Importance sampling for weighted serve-the-longest-queue. Math. Oper. Res. 34, 642--660. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Dupuis, P. and Wang, H. 2004. Importance sampling, large deviations, and differential games. Stoch. and Stoch. Reports 76, 481--508.Google ScholarGoogle ScholarCross RefCross Ref
  11. Dupuis, P. and Wang, H. 2007. Subsolutions of an Isaacs equation and efficient schemes for importance sampling. Math. Oper. Res. 32, 1--35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Dupuis, P. and Wang, H. 2009. Importance sampling for Jackson networks. QUESTA 62, 113--157. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Gilbarg, D. and Trudinger, N. S. 1983. Elliptic Partial Differential Equations of Second Order (2nd ed.). Springer Verlag, Berlin.Google ScholarGoogle Scholar
  14. Glasserman, P. and Wang, Y. 1997. Counter examples in importance sampling for large deviations probabilities. Ann. Appl. Prob. 7, 731--746.Google ScholarGoogle ScholarCross RefCross Ref
  15. Glynn, P. W. and Iglehart, D. G. 1988. Simulation methods for queues: an overview. QUESTA 3, 221--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Heidelberger, P. 1995. Fast simulation of rare events in queueing and reliability models. ACM Trans. Modeling Comp. Simulation 4, 43--85. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Juneja, S. and Nicola, V. 2005. Efficient simulation of buffer overflow probabilities in Jackson networks with feedback. ACM Trans. Modeling Comp. Simulation 15, 281--315. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Miretskiy, D. I., Scheinhardt, W. R. W., and Mandjes, M. R. H. 2007. Efficient simulation of a tandem queue with server slowdown. Simulation 83, 751--767. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Parekh, S. and Walrand, J. 1989. A quick simulation method for excessive backlogs in networks of queues. IEEE Trans. Autom. Control 34, 54--66.Google ScholarGoogle ScholarCross RefCross Ref
  20. Protter, P. 1995. Stochastic Integration and Differential Equations. Springer, New York.Google ScholarGoogle Scholar
  21. Sadowsky, J. S. 1996. On Monte Carlo estimation of large deviations probabilities. Ann. Appl. Prob. 6, 399--422.Google ScholarGoogle ScholarCross RefCross Ref
  22. Setayeshgar, L. and Wang, H. 2011. Large deviations for a feed-forward network. Adv. Appl. Prob. 43, 545--571.Google ScholarGoogle ScholarCross RefCross Ref
  23. Sezer, A. D. 2009. Importance sampling for a Markov modulated queuing network. Stoch. Proc. Appl. 119, 491--517.Google ScholarGoogle ScholarCross RefCross Ref
  24. Shahsbuddin, P. 1994. Importance sampling for the simulation of highly reliable Markovian systems. Manage. Sci. 40, 333--352. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Siegmund, D. 1976. Importance sampling in the Monte Carlo study of sequential tests. Ann. Statist. 4, 673--684.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Efficient importance sampling schemes for a feed-forward network

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on Modeling and Computer Simulation
        ACM Transactions on Modeling and Computer Simulation  Volume 23, Issue 4
        October 2013
        113 pages
        ISSN:1049-3301
        EISSN:1558-1195
        DOI:10.1145/2556945
        Issue’s Table of Contents

        Copyright © 2013 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 December 2013
        • Accepted: 1 June 2013
        • Revised: 1 January 2013
        • Received: 1 May 2012
        Published in tomacs Volume 23, Issue 4

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader