Integrating File Popularity and Peer Generosity in Proximity Measure for Semantic-based Overlays

Peer-to-peer file sharing systems are now at the origin of most of Internet traffic. Improving the performance of such systems has generated a lot of interest both in industry and academia. More specifically, many approaches focus on the improvement of the query mechanism in such systems. In a peer-to-peer system, peers are connected to a subset of other peers with which they can communicate. Each peer maintains a cache and makes available its contents to the rest of the system. Connecting peers sharing similar interest in the context of a given application has recently been identified as a sound basis to improve the search efficiency. However, capturing such interest-based (or semantic) proximity patterns is a difficult task. Most of current approaches measure this proximity between peers as the overlap between their cache contents. Given the well-known popularity patterns of peer-to-peer file sharing systems, the overlap between cache contents of two peers may not reflect accurately their semantic proximity. More specifically, this measure depends upon peer generosity and file popularity. In this paper we propose a refined proximity measure taking into account these factors. We evaluated the proposed solution by simulation against a real peer-to-peer system (eDonkey) workload and results show the effectiveness of the proposed approach. While peers generosity can easily be computed locally, file popularity may require a global knowledge of the system. We also propose in this paper an epidemic algorithm to compute in a fully decentralised fashion an estimation of files popularity. // Ce rapport présente une mesure de proximité sémantique permettant de prendre en compte les effets de bords induits par la générosité des noeuds et la popularité des fichiers d'un système de partage de fichiers. Cette mesure est introduite dans un protocôle épidémique permettant la comparaison des résultats obtenus. De plus, ce rapport introduit une approche épidémique d'évaluation locale de la popularité d'un fichier