Real datasets for file-sharing peer-to-peer systems

The fundamental drawback of unstructured peer-to-peer (P2P) networks is the flooding-based query processing protocol that seriously limits their scalability. As a result, a significant amount of research work has focused on designing efficient search protocols that reduce the overall communication cost. What is lacking, however, is the availability of real data, regarding the exact content of users' libraries and the queries that these users ask. Using trace-driven simulations will clearly generate more meaningful results and further illustrate the efficiency of a generic query processing protocol under a real-life scenario. Motivated by this fact, we developed a Gnutella-style probe and collected detailed data over a period of two months. They involve around 4,500 users and contain the exact files shared by each user, together with any available metadata (e.g., artist for songs) and information about the nodes (e.g., connection speed). We also collected the queries initiated by these users. After filtering, the data were organized in XML format and are available to researchers. Here, we analyze this dataset and present its statistical characteristics. Additionally, as a case study, we employ it to evaluate two recently proposed P2P searching techniques.