Handling Churn in Similarity Based Clustering Overlays Using Weighted Benefit

Abstract

Similarity based clustering (SBC) overlays are decentralized networks of nodes on the Internet edge, where each node maintains some number of direct connections to other nodes that are most "similar" to it. The challenge is: how do the nodes in the overlay converge to and maintain the most similar neighbors, given that the network is decentralized, is subject to churn and that similarity varies over time. Protocols that simultaneously provide fast convergence and low bandwidth consumption are the objective of this research. We present a protocol, that we call Weighted Benefit Scheme (WBS), that improves upon existing state-of-the-art in this area: it has equivalent convergence rate to the Optimum Benefit Protocol while simultaneously handling churn competitively to the Vicinity protocol. We use real world datasets from Yahoo WebScope that comprises of 15,400 users with 354,000 ratings about 1000 songs and our experiments are performed on the simulation test-bed PeerNet.