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

    Keywords

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

    Cite this

    Zaburnenko, T. S., & Nicola, V. F. (2006). Efficient Heuristics for Simulating Population Overflow in Parallel Networks. In The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability (pp. 86-93)
    Zaburnenko, T.S. ; Nicola, V.F. / Efficient Heuristics for Simulating Population Overflow in Parallel Networks. The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability. 2006. pp. 86-93
    @inproceedings{bcd90e63d7544dca8a5d8cb949148753,
    title = "Efficient Heuristics for Simulating Population Overflow in Parallel Networks",
    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.",
    keywords = "EWI-9121, IR-63922, METIS-248481",
    author = "T.S. Zaburnenko and V.F. Nicola",
    year = "2006",
    language = "Undefined",
    isbn = "not assigned",
    pages = "86--93",
    booktitle = "The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability",

    }

    Zaburnenko, TS & Nicola, VF 2006, Efficient Heuristics for Simulating Population Overflow in Parallel Networks. in The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability. pp. 86-93.

    Efficient Heuristics for Simulating Population Overflow in Parallel Networks. / Zaburnenko, T.S.; Nicola, V.F.

    The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability. 2006. p. 86-93.

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

    TY - GEN

    T1 - Efficient Heuristics for Simulating Population Overflow in Parallel Networks

    AU - Zaburnenko, T.S.

    AU - Nicola, V.F.

    PY - 2006

    Y1 - 2006

    N2 - 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.

    AB - 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.

    KW - EWI-9121

    KW - IR-63922

    KW - METIS-248481

    M3 - Conference contribution

    SN - not assigned

    SP - 86

    EP - 93

    BT - The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability

    ER -

    Zaburnenko TS, Nicola VF. Efficient Heuristics for Simulating Population Overflow in Parallel Networks. In The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability. 2006. p. 86-93