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Empirical investigation of BitTorrent community graphs

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Abstract

Most of the users of a BitTorrent community participate in multiple swarms, usually simultaneously as uploader and downloader. Thus the inter-swarm aspect of the bandwidth resource allocation has high relevance in the algorithm design of deployed BitTorrent clients. This inter-swarm connections among the users of a BitTorrent community can be represented by a flow network of special structure. Using this representation it has been demonstrated that de facto solutions to the resource allocation in BitTorrent communities are suboptimal. In this paper we investigate this conclusion in more details using graph measures and optimization techniques. We find that BitTorrent communities are sensitive for removal of highly contributing users, that the inter-swarm connectivity can lead to different performance results, and that torrent selection mechanisms can hardly improve the average download performance of users. Regarding the theoretical optimum of the discussed problem we show that it does not necessary comply with the BitTorrent protocol.

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Notes

  1. Other seeding incentive mechanisms are existing and successfully used in communities, e.g. based on effort [6].

  2. For the shake of simplicity we avoid the notion of time in the graph models.

  3. Note that in these particular experiments we did not take the users’ bandwidths directly into account.

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Acknowledgments

This work was partially supported by the European Union and the European Social Fund through project FuturICT.hu (Grant No.: TAMOP-4.2.2. C-11/1/KONV-2012-0013). T. Vinkó was supported by the Bolyai Scholarship of the Hungarian Academy of Sciences.

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  1. Institute of Informatics, University of Szeged, Árpád tér 2, Szeged, 6720, Hungary

    Tamás Vinkó & Bea Botyánszki

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  1. Tamás Vinkó
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  2. Bea Botyánszki
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Correspondence to Tamás Vinkó.

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Vinkó, T., Botyánszki, B. Empirical investigation of BitTorrent community graphs. Computing 98, 567–582 (2016). https://doi.org/10.1007/s00607-015-0470-9

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