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Title: Discrete-time heavy-tailed chains, and their properties in modelling network traffic
Authors: Hernández, José-Alberto
Phillips, Iain W.
Aracil, Javier
Keywords: Algorithms
Performance
Discrete-time heavy-tailed chains
Heavy-tailed distributions
Long-range dependence
Self-similar processes
Fractional Brownian motion
Issue Date: 2007
Citation: HERNADEZ, 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 17
Abstract: The 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.
Description: 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/10.1145/1276927.1276930
DOI: 10.1145/1276927.1276930
URI: https://dspace.lboro.ac.uk/2134/2738
ISSN: 1049-3301
Appears in Collections:Published Articles (Computer Science)

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