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Distribution of traffic among applications as measured in the French METROPOLIS project

Répartition du trafic parapplication mesuré dans le projet metropolis

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We investigate in this paper the time evolution and the composition in terms of applications of traffic in two different networks, namely the Renater network, dedicated to the French academic and research community, and the France Télécom backbone network supporting commercial traffic. For each network, we present the time evolution of traffic in terms of applications, the associated pie charts for global results, as well as, for each detected application, its flow size distribution, that should have an impact on the traffic nature (self-similarity or long range dependence due to the heavy tail of flow size distribution). Based on these results, this paper presents a discussion on the differences between academic and commercial traffic in terms of usage, as well as possible solutions against lrd and its associated degradation of network performance. For traffic analysis, we propose a new method of classifying traffic according to applications, which relies on applicative protocols recognition instead on the iana ports numbers.


Nous étudions dans cet article l’évolution au cours du temps et la composition du trafic en termes d’applications dans les deux réseaux que sont Rénater, utilisé par la communauté académique et de recherche française, et le réseau France Télécom qui transporte du trafic commercial. Pour chaque réseau, nous présentons l’évolution au cours du temps de la composition de trafic en termes d’applications, les camemberts associés pour présenter les résultats globaux ainsi que, pour chaque application observée, la distribution des tailles de ses flux qui ont un impact sur les caractéristiques du trafic (auto-similarité ou dépendance longue dues à des distributions de tailles de flux à décroissance lente). À partir de ces résultats, cet article étudie les différences entre les trafics académique et commercial en termes d’usages, ainsi que des solutions pour réduire la lrd et les baisses de performance réseau qu’elle induit. A noter également que pour l’analyse du trafic, nous proposons une nouvelle méthode de classification du trafic par application qui repose sur la reconnaissance des protocoles applicatifs plutôt que sur les numéros de port iana.

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Correspondence to Philippe Owezarski.

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Owezarski, P., Larrieu, N., Bernaille, L. et al. Distribution of traffic among applications as measured in the French METROPOLIS project. Ann. Telecommun. 62, 369–386 (2007). https://doi.org/10.1007/BF03253266

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  • Incoming Data
  • Port Number
  • Long Range Dependence
  • Traffic Trace
  • France Telecom