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Time Series Methods for Synthetic Video Traffic

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Advanced Computational Methods for Knowledge Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 453))

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Abstract

The scope of this paper is the creation of synthetic video traffic using time series models. Firstly, we discuss the procedure for creating video traffic with FARIMA models. However, the created traffic displays the LRD characteristic of real traffic, but underestimates its moments (mean, sd, skewness and kurtosis). We present two approaches for improving the popular FARIMA model for the creation of synthetic traffic. The first approach is to apply FARIMA models with heavy-tailed errors for traffic creation. The second is a two step procedure, where we build a FARIMA model with normal innovations and then we provide a statistical transformation for its projection in order to catch a desired marginal probability distribution. Using this procedure we approximated Student t and LogNormal as marginal distributions. The above procedures are applied to the performance evaluation of three real VBR traces.

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Authors and Affiliations

  1. Department of Electrical & Computer Engineering, University of Patras, Patras, Greece

    Christos Katris & Sophia Daskalaki

Authors
  1. Christos Katris
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  2. Sophia Daskalaki
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Corresponding author

Correspondence to Christos Katris .

Editor information

Editors and Affiliations

  1. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Thanh Binh Nguyen

  2. Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

    Tien van Do

  3. Laboratory of Theoretical and Applied Computer Science (LITA), UFR MIM, University of Lorraine, Ile du Saulcy, Metz, France

    Hoai An Le Thi

  4. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

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Katris, C., Daskalaki, S. (2016). Time Series Methods for Synthetic Video Traffic. In: Nguyen, T.B., van Do, T., An Le Thi, H., Nguyen, N.T. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-319-38884-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-38884-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38883-0

  • Online ISBN: 978-3-319-38884-7

  • eBook Packages: EngineeringEngineering (R0)

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