Método de Estimação de Parâmetros para Modelagem no Domínio Wavelet do Tráfego de Redes de Computadores Usando o Algoritmo de Levenberg-Marquardt
Resumo
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.
Referências
Barman, C., Chaudhuri, H., Deb, A., and Sinha, B. (2015). The essence of multifractal detrended fluctuation technique to explore the dynamics of soil radon precursor for earthquakes. Natural Hazards,78(2):855–877.
CODERS, A. (2020). Fractals. https://wall.alphacoders.com. Último acesso em 15-04-2020.
Coninck, Q. D., Baerts, M., Hesmans, B., and Bonaventurea, O. (2016).
Crawdad dataset. https://crawdad.org/uclouvain/mptcpsmartphone/20160304. Último acesso em 12-04-2020.
Cunha, M. C. C. (2000). Métodos Numéricos. Editora Unicamp, São Paulo, BRA.
Danzig, P., Mogul, J., Paxson, V., and Schwartz, M. (2000). The internet traffic archive.http://ita.ee.lbl.gov/. Último acesso em 12-04-2020.
Feldmann, A., Gilbert, A. C., and Willinger, W. (1998). Data networks as cascades: Investigating the multifractal nature of internet wan traffic. ACM SIGCOMM Computer Communication Review,28(4):42–55.
Grandemange, Q., Bhujwalla, Y., Gilson, M., Ferveur, O., and Gnaedinger, E. (2017).An as-level approach to network traffic analysis and modelling. IEEE International Conference on Communications (ICC), pages 1–6.
Krishna, M. P., Gadre, V., and Desai, U. B. (2003). Multifractal based network traffic modeling. Kluwer Academic Publishers.
Mallat, S. (2008). A wavelet tour of signal processing. Academic Press, 3a edição edition.
Mandelbrot, B. B. (1990). Limit log normal multifractal measures. In Frontiers of Physics: Landau Memorial Conference, (163):309–340.
Millán, G. and Lefranc, G. (2015). Simple technique of multifractal traffic modeling. In 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pages 329–332.
Park, K. and Willinger, W. (2000). Self-Similar Network Traffic and Performance Evaluation. John Wiley Sons, Inc. New York, NY, USA, 1st edition.
Riedi, R., Crouse, M., Ribeiro, V., and Baraniuk, R. (1999). A multifractal waveletmodel with application to network traffic. IEEE Transaction on Information Theory,45(3):992–1018.
Rocha, F. G. C. (2011). Modelagem multifractal aplicada à estimaçã de probabilidade de perda de dados e ao controle de fluxos de tráfego em redes. UFG, Goiânia, GO.
Tuberquia-David, M., Vela-Vargas, F., Lópes-Chávez, H., and Hernández, C. (2016). A multifractal wavelet model for the generation of long-range dependency traffic traces with adjustable parameters. Expert Systems With Applications, 62(2016):373–384.
Vieira, F. H. T. and Lee, L. L. (2010). An admission control approach for multifractal network traffic flows using effective envelopes. International Journal of Electronics and Communications, 64(7):629–639.
Vieira, F. H. T., Rocha, F. G. C., and dos Santos Junior, J. A. (2012). Loss probability estimation and control for ofdm/tdma wireless systems considering multifractal traffic characteristics. Computer Communications, 35(2):263–271.
