An Analytical Framework for Self-Organizing Peer-to-Peer Anti-Entropy Algorithms

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Executive Summary

An analytical framework is developed for establishing exact performance measures for Peer-To-Peer (P2P) anti-entropy paradigms used in biologically inspired epidemic data dissemination. Major benefits of these paradigms are that they are fully distributed, self-organizing, utilize local data only via pair-wise interactions, and provide eventual consistency, reliability and scalability. The authors derive exact expressions for infection probabilities through elaborated counting techniques on a digraph. Considering the first passage times of a Markov chain based on these probabilities, they find the expected message delay experienced by each peer and its overall mean as a function of initial number of infectious peers.

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