Long-range dependence analysis of Internet traffic

Cheolwoo Park, F�lix Hernández-Campos, Long Le, J. S. Marron, Juhyun Park, Vladas Pipiras, F. D. Smith, Richard L. Smith, Michele Trovero and Zhengyuan Zhu

Journal of Applied Statistics, 2011, vol. 38, issue 7, 1407-1433

Abstract: Long-range-dependent time series are endemic in the statistical analysis of Internet traffic. The Hurst parameter provides a good summary of important self-similar scaling properties. We compare a number of different Hurst parameter estimation methods and some important variations. This is done in the context of a wide range of simulated, laboratory-generated, and real data sets. Important differences between the methods are highlighted. Deep insights are revealed on how well the laboratory data mimic the real data. Non-stationarities, which are local in time, are seen to be central issues and lead to both conceptual and practical recommendations.

Date: 2011
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Citations: View citations in EconPapers (6)

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DOI: 10.1080/02664763.2010.505949

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