Discrete-time heavy-tailed chains, and their properties in modeling network traffic
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Association for Computing MachineryDate
2007-09Citation
10.1145/1276927.1276930
ACM Transactions on Modeling and Computer Simulation 17.4 (2004): 17
ISSN
1049-3301 (print); 1558-1195 (online)DOI
10.1145/1276927.1276930Funded by
The authors would like to acknowledge the support of the UKLight MASTS project (EPSRC, UK) and the DIOR project (MEC, Spain) to this work.Editor's Version
http://dx.doi.org/10.1145/1276927.1276930Subjects
Discrete-time heavy-tailed chains; Fractional Brownian motion; Heavy-tailed distributions; Long-range dependence; Self-similar processes; Informática; TelecomunicacionesNote
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Modeling and Computer Simulation , http://dx.doi.org/10.1145/10.1145/1276927.1276930Rights
© 2007 ACMAbstract
The particular statistical properties found in network measurements, namely self-similarity and long-range dependence, cannot be ignored in modeling network and Internet traffic. Thus, despite their mathematical tractability, traditional Markov models are not appropriate for this purpose, since their memoryless nature contradicts the burstiness of transmitted packets. However, it is desirable to find a similarly tractable model which is, at the same time, rigorous at capturing the features of network traffic.
This work presents discrete-time heavy-tailed chains, a tractable approach to characterize network traffic as a superposition of discrete-time “on/off” sources. This is a particular case of the generic “on/off” heavy-tailed model, thus shows the same statistical features as the former, particularly self-similarity and long-range dependence, when the number of aggregated sources approaches infinity.
The model is then applicable to characterize a number of discrete-time communication systems, for instance, ATM and optical packet switching, to further derive meaningful performance metrics such as average burst duration and the number of active sources in a random instant.
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Google Scholar:Hernández, José Alberto
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Phillips, Iain W.
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Aracil, Javier
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