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Multivariate Fairly Normal Traffic Model for Aggregate Load in Large-Scale Data Networks

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 6074))

Abstract

Traffic models are crucial for network planning, design, performance evaluation and optimization. However, it is first necessary to assess the validity of the newly proposed models. In this paper we present the validation of a multivariate fairly normal model for aggregate traffic that exploits the well-known day-night traffic pattern, which was first assumed and applied in a former work to detect changes in the Internet links’ load on-line. The validation process entails several normality analytical and graphical tests which are applied to real network traffic measurements, on attempts to assess fairly normality both in the marginal and joint distributions of the multivariate model. The results of the normality tests provide evidence that our design is adequate to model aggregate traffic accurately capturing the day-night traffic pattern.

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Mata, F., García-Dorado, J.L., Aracil, J. (2010). Multivariate Fairly Normal Traffic Model for Aggregate Load in Large-Scale Data Networks. In: Osipov, E., Kassler, A., Bohnert, T.M., Masip-Bruin, X. (eds) Wired/Wireless Internet Communications. WWIC 2010. Lecture Notes in Computer Science, vol 6074. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13315-2_23

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  • DOI: https://doi.org/10.1007/978-3-642-13315-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13314-5

  • Online ISBN: 978-3-642-13315-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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