Maximum Likelihood Estimation of the Flow Size Distribution Tail Index from Sampled Packet Data

Patrick Loiseau 1 Paulo Gonçalves 1 Stephane Girard 2 Florence Forbes 2 Pascale Vicat-Blanc Primet 3
1 RESO - Protocols and softwares for very high-performance network
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In the context of network traffic analysis, we address the problem of estimating the tail index of flow (or more generally of any group) size distribution from the observation of a sampled population of packets (individuals). We give an exhaustive bibliography of the existing methods and show the relations between them. The main contribution of this work is then to propose a new method to estimate the tail index from sampled data, based on the resolution of the maximum likelihood problem. To assess the performance of our method, we present a full performance evaluation based on numerical simulations, and also on a real traffic trace corresponding to internet traffic recently acquired.
Type de document :
Communication dans un congrès
SIGMETRICS '09 - 11th international joint conference on Measurement and modeling of computer systems, Jun 2009, Seattle, United States. ACM, pp.263-274, 2009, Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems. 〈10.1145/1555349.1555380〉
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https://hal.inria.fr/inria-00570448
Contributeur : Paulo Gonçalves <>
Soumis le : lundi 28 février 2011 - 14:47:58
Dernière modification le : mardi 16 janvier 2018 - 15:35:28

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Patrick Loiseau, Paulo Gonçalves, Stephane Girard, Florence Forbes, Pascale Vicat-Blanc Primet. Maximum Likelihood Estimation of the Flow Size Distribution Tail Index from Sampled Packet Data. SIGMETRICS '09 - 11th international joint conference on Measurement and modeling of computer systems, Jun 2009, Seattle, United States. ACM, pp.263-274, 2009, Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems. 〈10.1145/1555349.1555380〉. 〈inria-00570448〉

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