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Extreme-value analysis of teletraffic data

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  • Tsourti, Zoi
  • Panaretos, John

Abstract

An empirically verified characteristic of the expanding area of Internet is the longtailness of phenomena such as cpu time to complete a job, call holding times, files lengths requested, inter-arrival times and so on. Extreme values of the above quantities are liable to cause problems to the efficient operation of the network and call for effective design and management. Extreme-value analysis is an area of statistical analysis particularly concerned with the systematic study of extremes, providing useful insight to fields where extreme values are probable to occur and have detrimental effects, as is the case of teletraffics. In this paper we illustrate the main elements of this analysis and proceed to a detailed application of extreme-value analysis concepts to a specific teletraffic data set. This analysis verifies, too, the existence of long tails in the data.
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  • Tsourti, Zoi & Panaretos, John, 2004. "Extreme-value analysis of teletraffic data," Computational Statistics & Data Analysis, Elsevier, vol. 45(1), pages 85-103, February.
    • Handle: RePEc:eee:csdana:v:45:y:2004:i:1:p:85-103
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    References listed on IDEAS

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    1. Jon Danielsson & Casper G. de Vries, 1998. "Beyond the Sample: Extreme Quantile and Probability Estimation," FMG Discussion Papers dp298, Financial Markets Group.
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    Cited by:

    1. Lorenzo Hern'andez & Jorge Tejero & Alberto Su'arez & Santiago Carrillo-Men'endez, 2012. "Percentiles of sums of heavy-tailed random variables: Beyond the single-loss approximation," Papers 1203.2564, arXiv.org, revised Dec 2012.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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