Clustering in P2P exchanges and consequences on performances.

We propose here an analysis of a rich dataset which gives an exhaustive and dynamic view of the exchanges processed in a running eDonkey system. We focus on correlation in term of data exchanged by peers having provided or queried at least one data in common. We introduce a method to capture these correlations (namely the data clustering), and study it in detail. We then use it to propose a very simple and efficient way to group data into clusters and show the impact of this underlying structure on search in typical P2P systems. Finally, we use these results to evaluate the relevance and limitations of a model proposed in a previous publication. We indicate some realistic values for the parameters of this model, and discuss some possible improvements

Statistical analysis of a P2P query graph based on degrees and their time-evolution

Despite their crucial impact on the performances of peer-to-peer systems, very few is known on peers behaviors in such networks. We propose here a study of these of these behaviors in a running environment using a semi-centralised p2p system (edonkey). To achieve this, we use a trace of the queries made to a large server managing up to fifty thousands peers simultaneously, and a few thousands query per second. We analyse these data using complex network methods, and focus in particular on the degrees, their correlations, and their time-evolution. Results show a large variety of observed phenomena, including the variety of peers behaviors and heterogeneity of data queries, which should be taken into account when designing p2p systems

A Multi-Layered Architecture for Collaborative and Decentralized Consequence Finding

The consequence finding problem consists in producing all the consequences of a logical theory or, depending on the application context, in a restricted subset of these consequences. When the available knowledge is naturally scattered among different sources of information, computing such consequences with respect to the global theory in a decentralized way is a challenging problem. This paper presents Somewhere2, a multilayered architecture that may be used to solve such consequence finding problems in peer-to-peer networks of collaborating entities, that may evolve over time. The general layout of this architecture is described as well as the roles of its main components. Thanks to a careful and modular design, the resulting framework is very generic. This facilitates alternative implementations of specific components as well as its extension with additional features. First experimental results are presented, illustrating the scalability and robustness of this architecture. This framework may be used as a robust building block for more advanced distributed applications, such as Peer Data Management Systems

On Using Matching Theory to Understand P2P Network Design

This paper aims to provide insight into stability of collaboration choices in P2P networks. We study networks where exchanges between nodes are driven by the desire to receive the best service available. This is the case for most existing P2P networks. We explore an evolution model derived from stable roommates theory that accounts for heterogeneity between nodes. We show that most P2P applications can be modeled using stable matching theory. This is the case whenever preference lists can be deduced from the exchange policy. In many cases, the preferences lists are characterized by an interesting acyclic property. We show that P2P networks with acyclic preferences possess a unique stable state with good convergence properties