Asymptotically Optimal Importance Sampling for Product-Form Queuing Networks

Keith W. Ross, Jie Wang

    Research output: Contribution to journalArticle

    Abstract

    Monte Carlo integration M apphed to the integral representation of the normalization constant for a famdy of product-form multiclass queuing networks These networks are closed with single-server, fixed-rate stations and at least one infinite-server station. Letting the population for each class go to infinity, the asymptotically optimal importance-samphng distributions are derived for estimates of the normalization constant and of the utilizations. In the normal-usage regime, in which the asymptotic utilizations at the single-server stations are strictly less than one, independent exponential samphng is asymptotically optimal for estimating the normalization constant. In the same regime, the asymptotically optimal sampling dmtrlbution for estimatmg utilization is complex and dependent across single-server stations. In the critical-usage regime, m which the asymptotic utlhzatlons at the single-server stations are equal to one, truncated multivarlate normal sampling is asymptotically optimal for estimating the normalization constant.

    Original languageEnglish (US)
    Pages (from-to)244-268
    Number of pages25
    JournalACM Transactions on Modeling and Computer Simulation (TOMACS)
    Volume3
    Issue number3
    DOIs
    StatePublished - Jan 7 1993

      Fingerprint

    Keywords

    • Monte Carlo integration
    • importance sampling
    • queuing networks

    ASJC Scopus subject areas

    • Modeling and Simulation
    • Computer Science Applications

    Cite this