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Issue No.05  May (2008 vol.57)
pp: 619633
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TC.2007.70837
ABSTRACT
Network structure construction and global state maintenance are expensive in largescale, dynamic peertopeer (p2p) networks. With inherent topology independence and low state maintenance overhead, random walks have been widely used in such network environments. However, the current uses are limited to unguided or heuristic random walks with no guarantee on their converged node visitation probability distribution. Such a convergence guarantee is essential for strong analytical properties and high performance of many p2p applications. In this paper, we investigate an approach for random walks to converge to applicationdesired node visitation probability distributions while only requiring information about direct neighbors of each peer. Our approach is guided by the MetropolisHastings algorithm that is typically used in Monte Carlo Markov Chain sampling. We examine the effectiveness and practical issues of our approach using three application studies: random membership subset management, search, and load balancing. Both search and load balancing desire random walks with biased node visitation distributions to achieve applicationspecific analytical features. Our theoretical analysis, simulations, and Internet experiments demonstrate the advantage of our random walks compared with alternative topologyindependent indexfree approaches.
INDEX TERMS
Distributed networks, Distributed applications, Distributed systems
CITATION
Ming Zhong, Kai Shen, Joel Seiferas, "The ConvergenceGuaranteed Random Walk and Its Applications in PeertoPeer Networks", IEEE Transactions on Computers, vol.57, no. 5, pp. 619633, May 2008, doi:10.1109/TC.2007.70837
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