Long-range dependence analysis of Internet traffic

Date
2011-07-01
Authors
Park, Cheolwoo
Le, Long
Zhu, Zhengyuan
Marron, J.
Park, Juhyun
Pipiras, Vladas
Smith, F.
Smith, Richard
Trovero, Michele
Zhu, Zhengyuan
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Altmetrics
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Statistics
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Statistics
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.

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This is an Accepted Manuscript of an article published by Taylor & Francis as Park, Cheolwoo, Félix Hernández-Campos, Long Le, J. S. Marron, Juhyun Park, Vladas Pipiras, F. D. Smith, Richard L. Smith, Michele Trovero, and Zhengyuan Zhu. "Long-range dependence analysis of Internet traffic." Journal of Applied Statistics 38, no. 7 (2011): 1407-1433. Available online DOI: 10.1080/02664763.2010.505949. Posted with permission.

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