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Percorrer por autor "Veitch, Darryl"

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  • Skampling for the flow duration distribution
    Publication . Antunes, Nelson; Pipiras, Vladas; Veitch, Darryl; Bolla, R.; Ciucu, F.
    This paper concerns the problem of estimating the Internet flow duration distribution from indirect measurements due to network constraints. The aim is to estimate the distribution from observing: the possible superpositions (collisions) of sampled flow durations, the flow arrivals-to-departures times without identification of sampled flows and the number of sampled flows in progress. For each type of data available, we present estimators of the flow duration distribution, formulating the problem in queueing system terms. We also propose data streaming algorithms using sampling and sketching (through counters) to obtain the considered partial information from flows. At the core of this skampling (i.e. sampling and sketching) approach is the ability to tune the flow sampling probability for "optimal" flow load onto sketch entries (queues). Finally, we present numerical results comparing the different estimators of the flow duration distribution using two real Internet traces.
    2017Documento de conferência Acesso restrito Ver mais
  • Small and large scale behavior of moments of poisson cluster processes
    Publication . Antunes, Nelson; Pipiras, Vladas; Abry, Patrice; Veitch, Darryl
    Poisson cluster processes are special point processes that find use in modeling Internet traffic, neural spike trains, computer failure times and other real-life phenomena. The focus of this work is on the various moments and cumulants of Poisson cluster processes, and specifically on their behavior at small and large scales. Under suitable assumptions motivated by the multiscale behavior of Internet traffic, it is shown that all these various quantities satisfy scale free (scaling) relations at both small and large scales. Only some of these relations turn out to carry information about salient model parameters of interest, and consequently can be used in the inference of the scaling behavior of Poisson cluster processes. At large scales, the derived results complement those available in the literature on the distributional convergence of normalized Poisson cluster processes, and also bring forward a more practical interpretation of the so-called slow and fast growth regimes. Finally, the results are applied to a real data trace from Internet traffic.
    2017-12Artigo científico Acesso aberto Ver mais