Efficient Importance Sampling Heuristics for the Simulation of Population Overflow in Feed-Forward Queueing Networks

V.F. Nicola, T.S. Zaburnenko

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

    30 Downloads (Pure)


    In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in feed-forward networks. This heuristic attempts to approximate the “optimal��? state-dependent change of measure without the need for difficult analysis or costly optimization involved in other recently proposed adaptive importance sampling algorithms. Preliminary simulation experiments with a 4-node feed-forward network yield asymptotically efficient estimates, with relative error increasing at most linearly in the overflow level, where state-independent importance sampling is demonstrably ineffective.
    Original languageUndefined
    Title of host publicationProceedings of the Sixth Rare-Event Simulation Workshop, RESIM2006
    Place of PublicationBamberg
    PublisherOtto-Friedrich University
    Number of pages9
    ISBN (Print)not assigned
    Publication statusPublished - 8 Oct 2006
    Event6th International Workshop on Rare Event Simulation, RESIM 2006 - Bamberg, Germany
    Duration: 8 Oct 200610 Oct 2006
    Conference number: 6

    Publication series

    PublisherOtto-Friedrich University


    Workshop6th International Workshop on Rare Event Simulation, RESIM 2006
    Abbreviated titleRESIM


    • EWI-9039
    • METIS-248476
    • IR-63907

    Cite this