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Communication performance analysis of message passing parallel programs relies on accurate modeling of cluster network traffic patterns. To this end the authors have developed a tool for collecting traffic samples by simulating parallel processing environment with statistically tractable network traffic. their results from experiments show network packet traffic generated by message-passing parallel programs in a Beowulf cluster setting exhibits strong self-similarity, which was tested using Hurst parameters. As most known Hurst parameter estimators are heuristics, the authors propose a novel approach for statistical inference via parametric bootstrapping, and show confidence intervals of the estimated parameters.
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