An engineering approach to dynamic prediction of network performance from application logs

Zalal Uddin Mohammad Abusina*, Salahuddln Muhammad Salim Zabir, Ahmed Ashir, Debasish Chakraborty, Takuo Suganuma, Norio Shiratori


    研究成果: Article査読

    13 被引用数 (Scopus)


    Network measurement traces contain information regarding network behavior over the period of observation. Research carried out from different contexts shows predictions of network behavior can be made depending on network past history. Existing works on network performance prediction use a complicated stochastic modeling approach that extrapolates past data to yield a rough estimate of long-term future network performance. However, prediction of network performance in the immediate future is still an unresolved problem. In this paper, we address network performance prediction as an engineering problem. The main contribution of this paper is to predict network performance dynamically for the immediate future. Our proposal also considers the practical implication of prediction. Therefore, instead of following the conventional approach to predict one single value, we predict a range within which network performance may lie. This range is bounded by our two newly proposed indices, namely, Optimistic Network Performance Index (ONPI) and Robust Network Performance Index (RNPI). Experiments carried out using one-year-long traffic traces between several pairs of real-life networks validate the usefulness of our model.

    ジャーナルInternational Journal of Network Management
    出版ステータスPublished - 2005 5月

    ASJC Scopus subject areas

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