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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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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 6391.

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Date of creation: 2004
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Handle: RePEc:pra:mprapa:6391

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Keywords: Teletraffic engineering; Long tails; Extreme-value index; Smoothing procedures;

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References

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  1. Jón Daníelsson & Casper G. de Vries, 1998. "Beyond the Sample: Extreme Quantile and Probability Estimation," Tinbergen Institute Discussion Papers 98-016/2, Tinbergen Institute.
  2. Einmahl, J. & Dekkers, A. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Open Access publications from Tilburg University urn:nbn:nl:ui:12-125712, Tilburg University.
  3. Tsourti, Zoi & Panaretos, John, 2001. "Extreme Value Index Estimators and Smoothing Alternatives: Review and Simulation Comparison," MPRA Paper 6384, University Library of Munich, Germany.
  4. M. I. Bar�o & J. A. Tawn, 1999. "Extremal analysis of short series with outliers: sea-levels and athletics records," Journal of the Royal Statistical Society Series C, Royal Statistical Society, Royal Statistical Society, vol. 48(4), pages 469-487.
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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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