Mr. Paulo Jorge Salvador Serra Ferreira Profile

Paulo Jorge Salvador Serra Ferreira

at Univ de Aveiro

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Author

Publications (3)

Proceedings Article | 8 August 2003 Paper

Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior

Paulo Salvador, Antonio Nogueira, Rui Valadas

Proceedings Volume 5244, (2003) https://doi.org/10.1117/12.509357

KEYWORDS: Stochastic processes, Process modeling, Data modeling, Performance modeling, Organisms, Internet, Statistical modeling, Computer simulations, Matrices, Wavelets

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Proceedings Article | 8 August 2003 Paper

Markovian approach for modeling IP traffic behavior on several time scales

Antonio Nogueira, Paulo Salvador, Rui Valadas, Antonio Pacheco

Proceedings Volume 5244, (2003) https://doi.org/10.1117/12.509358

KEYWORDS: Superposition, Data modeling, Modulation, Performance modeling, Matrices, Mathematical modeling, Process modeling, Internet, Deconvolution, Telecommunications

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Proceedings Article | 24 July 2001 Paper

Framework based on Markov modulated Poisson processes for modeling traffic with long-range dependence

Paulo Ferreira Salvador, Rui Valadas

Proceedings Volume 4523, (2001) https://doi.org/10.1117/12.434317

KEYWORDS: Superposition, Internet, Wavelets, Modulation, Process modeling, Networks, Data modeling, Telecommunications, Statistical analysis, Performance modeling

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This paper proposes a novel fitting procedure for Markov Modulated Poisson Processes (MMPPs), consisting of the superposition of N 2-MMPPs, that is capable of capturing the long-range characteristics of the traffic. The procedure matches both the autocovariance and marginal distribution functions of the rate process. We start by matching each 2-MMPP to a different component of the autocovariance function. We then map the parameters of the model with N individual 2-MMPPs (termed superposed MMPP) to the parameters of the equivalent MMPP with 2^N states that results from the superposition of the N individual 2-MMPPs (termed generic MMPP). Finally, the parameters of the generic MMPP are fitted to the marginal distribution, subject to the constraints imposed by the autocovariance matching. Specifically, the matching of the distribution will be restricted by the fact that it may not be possible to decompose a generic MMPP back into individual 2-MMPPs. Overall, our procedure is motivated by the fact that direct relationships can be established between the autocovariance and the parameters of the superposed MMPP and between the marginal distribution and the parameters of the generic MMPP. We apply the fitting procedure to traffic traces exhibiting LRD including (i) IP traffic measured at our institution and (ii) IP traffic traces available in the Internet such as the well known, publicly available, Bellcore traces. The selected traces are representative of a wide range of services/protocols used in the Internet. We assess the fitting procedure by comparing the measured and fitted traces (traces generated from the fitted models) in terms of (i) Hurst parameter; (ii) degree of approximation between the autocovariance and marginal distribution curves; (iii) range of time scales where LRD is observed using a wavelet based estimator and (iv) packet loss ratio suffered in a single buffer for different values of the buffer capacity. Results are very clear in showing that MMPPs, when used in conjunction with the proposed fitting procedure, can be used to model efficiently Internet traffic in the relevant time scales, even when exhibiting LRD behavior.

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