Adaptively Routing P2P Queries Using Association Analysis

Authors:
Brian D. Connelly;Christopher W. Bowron;Li Xiao;Pang-Ning Tan;Chen Wang
Affiliations:
Michigan State University, USA;Michigan State University, USA;Michigan State University, USA;Michigan State University, USA;Michigan State University, USA
Venue:
ICPP '06 Proceedings of the 2006 International Conference on Parallel Processing
Year:
2006

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Abstract

Unstructured peer-to-peer networks have become a very popular method for content distribution in the past few years. By not enforcing strict rules on the network's topology or content location, such networks can be created quickly and easily. Unfortunately, because of the unstructured nature of these networks, in order to find content, query messages are flooded to nodes in the network, which results in a large amount of traffic. This work borrows the technique of association analysis from the data mining community and extends it to intelligently forward queries through the network. Because only a small subset of a node's neighbors are forwarded queries, the number of times those queries are propagated is also reduced, which results in considerably less network traffic. These savings enable the networks to scale to much larger sizes, which allows for more content to be shared and more redundancy to be added to the system, as well as allowing more users to take advantage of such networks.