Variable heavy tails in internet traffic

Authors:
F. Hernández-Campos;J. S. Marron;Gennady Samorodnitsky;F. D. Smith
Affiliations:
Department of Computer Science, University of North Carolina at Chapel Hill, NC;Department of Statistics, University of North Carolina at Chapel Hill NC and Department of Statistical Science, Cornell University, Ithaca, NY;School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY;Department of Computer Science, University of North Carolina at Chapel Hill, NC
Venue:
Performance Evaluation - Special issue: Distributed systems performance
Year:
2004

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Abstract

This paper studies tails of the size distribution of Internet data flows and their "heaviness". Data analysis motivates the concepts of moderate, far and extreme tails for understanding the richness of information available in the data. The data analysis also motivates a notion of "variable tail index", which leads to a generalization of existing theory for heavy-tail durations leading to long-range dependence.