Analysis of the evolution of peer-to-peer systems
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Analyzing peer-to-peer traffic across large networks
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Making gnutella-like P2P systems scalable
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Measurement, modeling, and analysis of a peer-to-peer file-sharing workload
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Exploiting Semantic Proximity in Peer-to-Peer Content Searching
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An analysis of internet content delivery systems
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Exploiting semantic clustering in the eDonkey P2P network
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Epidemic-Style management of semantic overlays for content-based searching
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Clustering in peer-to-peer file sharing workloads
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Know thy neighbor’s neighbor: better routing for skip-graphs and small worlds
IPTPS'04 Proceedings of the Third international conference on Peer-to-Peer Systems
Counteracting free riding in Peer-to-Peer networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Managing collaborative feedback information for distributed retrieval
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Modeling replication strategies in data grid systems with arbitrary clustered demands
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A recall-based cluster formation game in peer-to-peer systems
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Quantifying paedophile activity in a large P2P system
Information Processing and Management: an International Journal
COINS: COalitions and INcentiveS for effective Peer-to-Peer downloads
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A peer-to-peer recommender system for self-emerging user communities based on gossip overlays
Journal of Computer and System Sciences
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In this paper we present an empirical study of a workload gathered by crawling the eDonkey network --- a dominant peer-to-peer file sharing system --- for over 50 days.We first confirm the presence of some known features, in particular the prevalence of free-riding and the Zipf-like distribution of file popularity. We also analyze the evolution of document popularity.We then provide an in-depth analysis of several clustering properties of such workloads. We measure the geographical clustering of peers offering a given file. We find that most files are offered mostly by peers of a single country, although popular files don't have such a clear home country.We then analyze the overlap between contents offered by different peers. We find that peer contents are highly clustered according to several metrics of interest.We propose to leverage this property by allowing peers to search for content without server support, by querying suitably identified semantic neighbours. We find via trace-driven simulations that this approach is generally effective, and is even more effective for rare files. If we further allow peers to query both their semantic neighbours, and in turn their neighbours' neighbours, we attain hit rates as high as over 55% for neighbour lists of size 20.