Statistical Modelling 12 (2012), 29–65
Estimation of traffic matrices in the presence of long memory traffic
Pier Luigi Conti
Dipartimento di Statistica, Probabilità e Statistiche Applicate,
Sapienza Università di Roma
P.le A. Moro, 5
I–00185 Roma
Italy
eMail: pierluigi.conti@uniroma1.it
L De Giovanni
Università LUMSA,
Facoltà di Scienze della Formazione and Università LUISS Guido Carli
M Naldi
Università di Roma ’Tor Vergata’,
Dipartimento di Informatica, Sistemi e Produzione
Abstract:
The estimation of traffic matrices in a communications network on the basis of a set of traffic measurements on the network links is a well-known problem, for which a number of solutions have been proposed when the traffic does not show dependence over time, as in the case of the Poisson process. However, extensive measurements campaigns conducted on IP networks have shown that the traffic exhibits long range dependence. Here a method is proposed for the estimation of traffic matrices in the case of long range dependence, and its theoretical properties are studied. Its merits are then evaluated via a simulation study. Finally, an application to real data is provided.
Keywords:
network tomography; traffic estimation; self-similarity; long range dependence
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