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Titolo:
NEW MODELS FOR PSEUDO SELF-SIMILAR TRAFFIC
Autore:
ROBERT S; LEBOUDEC JY;
Indirizzi:
UNIV CALIF BERKELEY,DEPT EECS BERKELEY CA 94720 UNIV CALIF BERKELEY,DEPT EECS BERKELEY CA 94720 SWISS FED INST TECHNOL,COMMUN RES LAB CH-1015 LAUSANNE SWITZERLAND
Titolo Testata:
Performance evaluation
fascicolo: 1-2, volume: 30, anno: 1997,
pagine: 57 - 68
SICI:
0166-5316(1997)30:1-2<57:NMFPST>2.0.ZU;2-2
Fonte:
ISI
Lingua:
ENG
Keywords:
SELF-SIMILAR PROCESSES; LAN TRAFFIC; MARKOV MODEL; HURST PARAMETER; FITTING; COURTOISS THEORY OF DECOMPOSABILITY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
17
Recensione:
Indirizzi per estratti:
Citazione:
S. Robert e J.Y. Leboudec, "NEW MODELS FOR PSEUDO SELF-SIMILAR TRAFFIC", Performance evaluation, 30(1-2), 1997, pp. 57-68

Abstract

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 itis 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 modelsis 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. (C) 1997 Elsevier Science B.V.

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Documento generato il 01/10/20 alle ore 01:29:55