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)