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Proceedings Paper

Accurate and fast replication on the generation of fractal network traffic using alternative probablity models
Author(s): Stenio Fernandes; Carlos Kamienski; Djamel Sadok
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Paper Abstract

Synthetic self-similar traffic in computer networks simulation is of imperative significance for the capturing and reproducing of actual Internet data traffic behavior. A universally used procedure for generating self-similar traffic is achieved by aggregating On/Off sources where the active (On) and idle (Off) periods exhibit heavy tailed distributions. This work analyzes the balance between accuracy and computational efficiency in generating self-similar traffic and presents important results that can be useful to parameterize existing heavy tailed distributions such as Pareto, Weibull and Lognormal in a simulation analysis. Our results were obtained through the simulation of various scenarios and were evaluated by estimating the Hurst (H) parameter, which measures the self-similarity level, using several methods.

Paper Details

Date Published: 8 August 2003
PDF: 10 pages
Proc. SPIE 5244, Performance and Control of Next-Generation Communications Networks, (8 August 2003); doi: 10.1117/12.509375
Show Author Affiliations
Stenio Fernandes, Univ. Federal de Pernambuco (Brazil)
Carlos Kamienski, Univ. Federal de Pernambuco (Brazil)
Djamel Sadok, Univ. Federal de Pernambuco (Brazil)

Published in SPIE Proceedings Vol. 5244:
Performance and Control of Next-Generation Communications Networks
Robert D. van der Mei; Frank Huebner, Editor(s)

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