Efficient Heuristics for Simulating Population Overflow in Parallel Networks

T.S. Zaburnenko, V.F. Nicola

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

    Abstract

    In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in networks of parallel queues. This heuristic approximates the “optimal��? state-dependent change of measure without the need for costly optimization involved in other recently proposed adaptive algorithms. Preliminary results from simulations of networks with up to 4 parallel queues and different traffic intensities yield asymptotically efficient estimates (with relative error increasing sublinearly in the overflow level) where state-independent importance sampling is ineffective.
    Original languageUndefined
    Title of host publicationThe 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability
    Pages86-93
    Number of pages3
    Publication statusPublished - 2006
    EventThe 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability, Petrozavodsk, Russia: Proceedings of the 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability -
    Duration: 26 Aug 200631 Aug 2006

    Conference

    ConferenceThe 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability, Petrozavodsk, Russia
    Period26/08/0631/08/06

    Keywords

    • EWI-9121
    • IR-63922
    • METIS-248481

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