An Efficient JSD-Based Search on Interest-Based Hierarchical Clustering of Overlay Networks

Abstract

In P2P networks, peers share contents, especially video files, which represent their interests. However, the underlying P2P topology may not represent this interest distribution. Thus, one important aspect of constructing an efficient P2P network is to exploit the interest similarity among peers. In this paper, we propose a hierarchical clustering mechanism for constructing an overlay network that takes interest similarity among peers into account. By measuring the similarity among interests of peers and clusters, interest-based hierarchical clusters are formed by using Jensen-Shannon Divergence metric. The clustering performance metrics, accuracy and correctness, are reported on PlanetLab. For limited keyword collections, a novel Jensen-Shannon Divergence-based search mechanism is implemented. It has been observed that the integrated mechanism provides an efficient method and better performance as compared to classical keyword-based search.