Exploiting semantic clustering in the edonkey p2p network

Peer-to-peer file sharing now represents a significant portion of the Internet traffic and has generated a lot of interest from the research community. Some recent measurements studies of peer-to-peer workloads have demonstrated the presence of semantic proximity between peers. One way to improve performance of peer-to-peer file sharing systems is to exploit this locality of interest in order to connect semantically related peers so as to improve the search both in flooding- and server-based systems. Creating these additional connections raises interesting challenges and in particular (i) how to capture the semantic relationship between peers (ii) how to exploit these relationships and (iii) how to evaluate these improvements. In this paper, we evaluate several strategies to exploit the semantic proximity between peers against a real trace collected in November 2003 in the eDonkey 2000 peer-to-peer network. We present the results of this evaluation which confirm the presence of clustering in such networks and the interest to exploit it. 1 Introduction an

Clustering in Peer-to-Peer File Sharing Workloads

Peer-to-peer file sharing systems now generate a significant portion of Internet traffic. A good understanding of their workloads is crucial in order to improve their scalability, robustness and performance. Previous measurement studies on Kazaa and Gnutella were based on monitoring peer requests, and mostly concerned with peer and file availability and network traffic. In this paper, we take different measurements: instead of passively recording requests, we actively probe peers to get their cache contents information. This provides us with a map of contents, that we use to evaluate the degree of clustering in the system 1, and that could be exploited to improve significantly the search process.

Clustering in Peer-to-Peer File Sharing Workloads

Peer-to-peer file sharing systems now generate a significant portion of Internet tra#c. A good understanding of their workloads is crucial in order to improve their scalability, robustness and performance. Previous measurement studies on Kazaa and Gnutella were based on monitoring peer requests, and mostly concerned with peer and file availability and network tra#c. In this paper, we take di#erent measurements: instead of passively recording requests, we actively probe peers to get their cache contents information. This provides us with a map of contents, that we use to evaluate the degree of clustering in the system , and that could be exploited to improve significantly the search process

Peer Sharing Behaviour in the eDonkey Network, and Implications for the Design of Server-less File Sharing Systems

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 Zipflike 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

Peer-to-peer computing (Introduction to Topic 7)

Distributed systems have experienced a shift of scale in the past few years. This evolution has generated an interest in peer-to-peer systems and resulted in much interesting work. Peer-to-peer systems are characterized by their potential to scale due to their fully decentralized nature. They are self-organizing, adapting automatically to peer arrivals and departures, and are highly resilient to failures. They rely on a symmetric communication model where peers act both as servers and clients. As the peer-to-peer concepts and technologies become more mature, many distributed services and applications relying on this model are envisaged in the context of large-scale distributed and parallel systems. This topic examines peer-to-peer technologies, applications, and systems, and also identifies key research issues and challenges. Twenty-six papers were submitted to the track and we accepted six. We organized two sessions, the first devoted to the problem of query management in structured and unstructured overlay networks, the second containing a broader selection of topics

Comparing Overlapping Properties of Real Bipartite Networks

International audienceMany real-world networks lend themselves to the use of graphs for analysing and modelling their structure. But such a simple representation has proven to miss some important and non trivial properties hidden in the bipartite structure of the networks. Recent papers have shown that overlapping properties seem to be present in bipartite networks and that it could explain better the properties observed in simple graphs. This work intends to investigate this question by studying two proposed metrics to account for overlapping structures in bipartite networks. The study, conducted on four dataset stemming from very different contexts (computer science, juridical science and social science), shows that the most popular metrics, the clustering coefficient, turns out to be less relevant that the recent redundancy coefficient to analyse intricate overlapping properties of real networks