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首页> 外文会议>International Symposium on Computer and Information Sciences(ISCIS 2006); 20061101-03; Istanbul(TR) >Exact Performance Measures for Peer-to-Peer Epidemic Information Diffusion
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Exact Performance Measures for Peer-to-Peer Epidemic Information Diffusion

机译：对等流行病信息传播的精确性能指标

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摘要

We consider peer-to-peer anti-entropy paradigms for epidemic information diffusion, namely pull, push and hybrid cases, and provide exact performance measures for them. Major benefits of the proposed epidemic algorithms are that they are fully distributed, utilize local information only via pair-wise interactions, and provide eventual consistency, scalability and communication topology-independence. Our contribution is the derivation of 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. In terms of these criteria, the hybrid approach outperforms pull and push paradigms, and push is better than the pull case. Such theoretical results would be beneficial when integrating the models in several peer-to-peer distributed application scenarios.
机译：我们考虑了流行信息传播的点对点反熵范式，即拉，推和混合情况，并为其提供了精确的性能度量。所提出的流行病算法的主要优点是它们是完全分布式的，仅通过成对交互使用本地信息，并提供最终的一致性，可伸缩性和通信拓扑独立性。我们的贡献是通过有向图上详尽的计数技术得出感染概率的精确表达。考虑基于这些概率的马尔可夫链的第一次通过时间，我们发现每个对等点经历的预期消息延迟及其总体均值是感染性对等点初始数量的函数。根据这些标准，混合方法的性能优于拉式和推式，推式比拉式要好。当在几个对等分布式应用程序场景中集成模型时，这样的理论结果将是有益的。

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