An as-level approach to network traffic analysis and modelling

Grandemange, Quentin; Bhujwalla, Yusuf; Gilson, Marion; Ferveur, Olivier; Gnaedinger, Eric

doi:10.1109/icc.2017.7996344

Cited by 2 publications

(1 citation statement)

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“…A heterogeneidade dessas redes proporciona um comportamento de tráfego variável, com presença de rajadas difíceis de serem previstas. A melhoria no desempenho das redes de comunicações tem sido alvo de pesquisas constantes, e muitos são os trabalhos propondo modelos para descrição do tráfego de redes [Riedi et al 1999, Krishna et al 2003, Millán and Lefranc 2015, Tuberquia-David et al 2016, Grandemange et al 2017.…”

Section: Introductionunclassified

Método de Estimação de Parâmetros para Modelagem no Domínio Wavelet do Tráfego de Redes de Computadores Usando o Algoritmo de Levenberg-Marquardt

Silva¹,

Rocha²

2020

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance 2020)

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Research has shown that analysis and modeling techniques that provide a better understanding of the behavior of network traffic flows are very important in the design and optimization of communication networks. For this reason, this work proposes a multifractal model based on a multiplicative cascade in the wavelet domain, to synthesize network traffic samples. For this purpose, in the proposed model, a parametric modeling based on an exponential function is used for the variance of the multipliers along the stages of the cascade. The exponential function parameters are obtained through the solution of a non-linear system, for this purpose, the Levenberg-Marquardt method is used. The main contribution of the proposed algorithm is to use a fixed and reduced number of parameters to generate network traffic samples that have characteristics such as self-similarity and wide Multifractal Spectrum Width (MSW) similar to the real network traffic traces and without the need for prior adjustment of these parameters.

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