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Discrete-time heavy-tailed chains, and their properties in modelling network traffic
journal contribution
posted on 2007-03-30, 10:46 authored by Jose A. Hernandez Gutierrez, Iain PhillipsIain Phillips, Javier AracilThe particular statistical properties found in network measurements, namely self-similarity and
long-range dependence, cannot be ignored in modelling 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 the discrete-time heavy-tailed chains, a tractable approach to characterise
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 showing 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 characterise a number of discrete-time communication systems,
for instance ATM and Optical Packet Switching, and further derive meaningful performance met-
rics, such as the average burst duration and the number of active sources in a random instant.
History
School
- Science
Department
- Computer Science
Pages
203897 bytesCitation
HERNANDEZ, J.-A., PHILLIPS, I.W. and ARACIL, A., 2007. Discrete-time heavy-tailed chains, and their properties in modelling network traffic. ACM Transactions on Modeling and Computer Simulation, 17 (4), article 17Publication date
2007Notes
This article was published in the journal, ACM Transactions on Modeling and Computer Simulation 17 (4) [© Association for Computing Machinery] and the definitive version is available at: http://doi.acm.org/10.1145/1276927.1276930ISSN
1049-3301Language
- en