ISW: Interest-Based Small World Network of P2P

ISW: Interest-Based Small World Network of P2P

Jinlong Zeng (School of Information Science & Technology, Sun Yat-sen University, Guangzhou, China & National Engineering Research Center of Digital Life, Guangzhou, China) and Guifeng Zheng (School of Software, Sun Yat-sen University, Guangzhou, China & National Engineering Research Center of Digital Life, Guangzhou, China)
DOI: 10.4018/jssci.2012100102
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Abstract

Content location in unstructured peer-to-peer (P2P) networks is a challenging problem. In this paper, the authors present a novel Interest-based Small World (ISW) network to address the problem, by constructing a cluster overlay in the unstructured P2P network based on the small world paradigm and user interest. There are many attractive properties of a small world network, such as low average hop distance and high clustering coefficient. Interest locality can improve the awareness of user’s indeed intentions. The authors’ scheme combines their advantage to create a better solution. The simulation results show that our scheme outperforms other schemes significantly.
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1. Method

The aim of our ISW overlay is to efficiently locate any object in the network. Our scheme uses two concepts, interest locality and small world. Here are some related works on them.

1.1. Interest Locality

Sripanidkulchai, etc. have proposed the concept interest locality in Sripanidkulchai, Maggs, and Zhang (2003). A group of nodes will exhibit interest locality if these nodes always have common interest in some same files. Figure 2 is cited from their paper to give an example of the interest locality. Node 0 in the middle is looking for files A, B and C. The two nodes in the right, node 3 and node 4, both have file A and node 3 has more match for B and C, Node 4 has more match for C. Therefore, node 0, 3 and 4 have the most common interests and form an interest group named {A, B, C}.

Figure 2.

Interest locality between peers

They present a search algorithm called shortcut method. They construct an interest overlay by query history on the top of physical overlay. Nodes in interest overlay are connected by the logical link named shortcut. If two nodes are linked by the shortcut that means they have common interest. Peers use these shortcuts to locate content. When shortcuts fail, peers will resort to the underlying overlay method, like flooding. The algorithm consists of shortcut discovery and shortcut selection. The interest shortcut method still has several aspects need to be improved: 1) they propose a simple method to create the interest shortcut, without considering further analysis and cluster of user interest; 2) The node's shortcut list can become too large to maintain, so that the search performance will degrade as the scale becomes larger and larger. In contrast, our approach not only establishes connections using user interest, but also automatically forms interest clusters, that can improve the search performance and scalability. We propose a practicable and detail clustering method to extend the abstract concept of the shortcut method.

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