An analytical framework for self-organizing peer-to-peer anti-entropy algorithms
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 vi...
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| Published in: | Performance evaluation Vol. 67; no. 3; pp. 141 - 159 |
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Elsevier B.V
01-03-2010
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| Abstract | 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. We 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, we find the expected message delay experienced by each peer and its overall mean as a function of initial number of infectious peers. Further delay and overhead analysis is given through simulations and the analytical framework. The number of contacted peers at each round of the anti-entropy approach is an important parameter for both delay and overhead. These exact performance measures and theoretical results would be beneficial when utilizing the models in several P2P distributed system and network services such as replicated servers, multicast protocols, loss recovery, failure detection and group membership management. |
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| AbstractList | 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. We 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, we find the expected message delay experienced by each peer and its overall mean as a function of initial number of infectious peers. Further delay and overhead analysis is given through simulations and the analytical framework. The number of contacted peers at each round of the anti-entropy approach is an important parameter for both delay and overhead. These exact performance measures and theoretical results would be beneficial when utilizing the models in several P2P distributed system and network services such as replicated servers, multicast protocols, loss recovery, failure detection and group membership management. |
| Author | Yazıcı, Emine Şule Özkasap, Öznur Çağlar, Mine Küçükçifçi, Selda |
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| References_xml | – volume: 47 start-page: 213 year: 1987 end-page: 223 ident: b19 article-title: On spreading a rumor publication-title: SIAM Journal on Applied Mathematics contributor: fullname: Pittel – year: 1992 ident: b24 article-title: A Course in Combinatorics contributor: fullname: Wilson – volume: 11 start-page: 105 year: 2008 end-page: 120 ident: b20 article-title: Optimal scheduling of peer-to-peer file dissemination publication-title: Journal of Scheduling contributor: fullname: Weiss – volume: 53 start-page: 4640 year: 2007 end-page: 4654 ident: b23 article-title: Gossiping with multiple messages publication-title: IEEE Transactions on Information Theory contributor: fullname: Massoulie – year: 1992 ident: b21 article-title: Univariate Discrete Distributions contributor: fullname: Kemp – volume: 10 start-page: 360 year: 1992 end-page: 391 ident: b5 article-title: Grapevine: Providing availability using lazy replication publication-title: ACM Transactions on Computer 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| Title | An analytical framework for self-organizing peer-to-peer anti-entropy algorithms |
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