Self-Similarity in Message Passing Parallel Processing Communication

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.