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Data-driven traffic and diffusion modeling in peer-to-peer networks: A real case study

Published online by Cambridge University Press:  18 November 2014

ROMAIN HOLLANDERS ,
DANIEL F. BERNARDES ,
BIVAS MITRA ,
RAPHAËL M. JUNGERS ,
JEAN-CHARLES DELVENNE  and
FABIEN TARISSAN
ROMAIN HOLLANDERS
Affiliation:
UCLouvain – Université catholique de Louvain / INMA, Avenue G. Lemaître 4, 1348 Louvain-la-Neuve, Belgium (e-mail: [email protected])
DANIEL F. BERNARDES
Affiliation:
LIP6 – CNRS and Université Pierre et Marie Curie / Paris 6, Place Jussieu, 75252 Paris cedex 05, France (e-mail: [email protected])
BIVAS MITRA
Affiliation:
UCLouvain – Université catholique de Louvain / INMA, Avenue G. Lemaître 4, 1348 Louvain-la-Neuve, Belgium Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, 721302, India (e-mail: [email protected])
RAPHAËL M. JUNGERS
Affiliation:
UCLouvain – Université catholique de Louvain / INMA, Avenue G. Lemaître 4, 1348 Louvain-la-Neuve, Belgium (e-mail: [email protected]) F.R.S./FNRS Research Associate, Rue d'Egmont 5, 1000 Bruxelles, Belgium
JEAN-CHARLES DELVENNE
Affiliation:
UCLouvain – Université catholique de Louvain / INMA, Avenue G. Lemaître 4, 1348 Louvain-la-Neuve, Belgium (e-mail: [email protected])
FABIEN TARISSAN
Affiliation:
LIP6 – CNRS and Université Pierre et Marie Curie / Paris 6, Place Jussieu, 75252 Paris cedex 05, France (e-mail: [email protected])
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Abstract

Peer-to-peer systems have driven a lot of attention in the past decade as they have become a major source of Internet traffic. The amount of data flowing through the peer-to-peer network is huge and hence challenging both to comprehend and to control. In this work, we take advantage of a new and rich dataset recording the peer-to-peer activity at a remarkable scale to address these difficult problems. After extracting the relevant and measurable properties of the network from the data, we develop two models that aim to make the link between the low-level properties of the network, such as the proportion of peers that do not share content (i.e., free riders) or the distribution of the files among the peers, and its high-level properties, such as the Quality of Service or the diffusion of content, which are of interest for supervision and control purposes. We observe a significant agreement between the high-level properties measured on the real data and on the synthetic data generated by our models, which is encouraging for our models to be used in practice as large-scale prediction tools. Relying on them, we demonstrate that spending efforts to reduce the amount of free riders indeed helps to improve the availability of files on the network. We observe however a saturation of this phenomenon after 60% of free riders.

Keywords

peer-to-peer networksbig dataspreading phenomenaMarkov chainsSIR models
Type
Research Article
Information
Network Science , Volume 2 , Issue 3 , December 2014 , pp. 341 - 366
Copyright
Copyright © Cambridge University Press 2014 

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