New models for pseudo self-similar traffic

Robert, Stephan (University of California, Berkeley, CA, USA) ; Le Boudec, Jean-Yves (EPFL, Lausanne, Switzerland)

After measurements on a LAN at Bellcore, it is known that data traffic is extremely variable on timescales ranging from milliseconds to days. The traffic behaves quite different from what has been assumed until now; traffic sources were generally characterized by short-term dependences but characteristics of the measured traffic have shown that it is long-term dependent. Therefore, new models (such as fractional Brownian motion, ARIMA processes and chaotic maps) have been applied. Although they are not easily tractable, one big advantage of these models is that they give a good description of the traffic using few parameters. In this paper, we describe a Markov chain emulating self-similarity which is quite easy to manipulate and depends only on two parameters (plus the number of states in the Markov chain). An advantage of using it is that it is possible to re-use the well-known analytical queuing theory techniques developed in the past in order to evaluate network performance. The tests performed on the model are the following: Hurst parameter (by the variances method) and the so-called “visual” test. A method of fitting the model to measured data is also given. In addition, considerations about pseudo long-range dependences are exposed.


Note: ROBERT, Stephan est un chercheur à la HES-SO, HEIG-VD, depuis 2001.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Date:
1997-01
Pagination:
12 p.
Published in:
Performance Evaluation
Numeration (vol. no.):
1997, vol. 30, no. 1-2, pp. 57-68
DOI:
ISSN:
0166-5316
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2021-02-19, last modified 2021-02-25

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