Indexing techniques for file sharing in scalable peer-to-peer networks (Extended Abstract)

File sharing is a very popular service provided by peer-to-peer (P2P) networks. In a P2P file-sharing network, users share files and issue queries to the network to find the locations of the files residing at other peer nodes. Due to factors such as large user base and use of query broadcast protocols, each node in the network receives many search queries every second. Recently network developers have incorporated proxy-enabled peers, or supernodes, which are designed to enhance scalability by providing indexing services to nodes on slower network connections. Typically, supernodes build a vector or multi-index of all the filenames of the shared files stored on other (slower) peers nodes connected to them. In this paper we consider a new model whereby the index tables of the individual nodes are merged into a single data structure stored by the supernode. We analyze this new model in relation to the standard vectorized data structure. We compare the performance of these supernode indexing algorithms and provide a theoretical analysis that is asymptotic and probabilistic in nature. However, there are several significant constant factors that the theory does not account for, and which in practice are important for designing an optimal system solution. We report herein on a series of simulation experiments which provide 1) verification of the asymptotic analysis of the formal framework, and 2) tools to determine the magnitude of the constant factors involved in the performance analysis. Our general conclusion is that when the query rate exceeds the rate of data updates, the new merged model is preferable to the vector model. However, the details of our analysis allow us to consider combinations of several parameters, and thereby enable the design of optimal indexing schemes via the incorporation of measurements of the parameters of particular applications