PlanetP: using gossiping and random replication to support reliable peer-to-peer content search and retrieval
PlanetP: using gossiping and random replication to support reliable peer-to-peer content search and retrieval
复制标题
PlanetP：使用八卦和随机复制来支持可靠的点对点内容搜索和检索
DOI：
10.7282/t3-mq2a-y165
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
Thu D. Nguyen
中科院分区：
文献类型：
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作者：
F. M. Cuenca;R. Martin;Thu D. Nguyen
我们介绍了PlanetP系统，它探讨了一个可靠的点对点（P2P）的内容搜索和检索服务，使用随机循环的全球状态之间的同行的非结构化社区的建设。我们的工作是一种新的替代方法，最近的P2P系统，专注于使非常大规模的基于名称的对象定位使用复杂的分布式数据结构。我们表明，我们更简单的方法可扩展到几千个同行（最终目标约一万政权），并在几分钟内收敛，仅使用适度的带宽，同时仍然保持可靠的搜索，排名和检索类似的互联网搜索引擎。然而，与当前的搜索引擎或其他P2P系统不同，PlanetP不需要集中式目录或管理，也不需要构建复杂的分布式数据结构。PlanetP通过三个主要组件实现其目标。首先，对等体协作以使用随机闲聊算法来维护全局成员目录的本地副本沿着以及共享内容的紧凑摘要。第二，peers实现了基于文本的排序算法，以帮助用户忽略不相关的文档。最后，同行合作复制不受欢迎的内容-流行的内容自然是高度复制通过囤积-使用里德所罗门擦除编码，以增加的概率，
We introduce the PlanetP system, which explores the construction of a reliable peer-to-peer (P2P) content search and retrieval service using randomly circulated global state between peers of an unstructured community. Our work represents a novel alternative approach to recent P2P systems that focus on enabling very largescale name-based object location using sophisticated distributed data structures. We show that our simpler approach scales to several thousand peers (ultimately targeting the regime of about ten thousand) and converges in several minutes using only modest bandwidth while still maintaining reliable search, ranking, and retrieval similar to an Internet search engine. Unlike current search engines or other P2P systems, however, PlanetP does not require centralized directories or management, nor builds a complex distributed data structure. PlanetP achieves its goals using three major components. First, peers collaborate to maintain local copies of the global membership directory along with compact summaries of shared content using a randomized gossiping algorithm. Second, peers implements a per query, text-based ranking algorithm to help users ignore irrelevant documents. Finally, peers collaborate to replicate unpopular content—popular content is naturally highly replicated via hoarding—using ReedSolomon erasure coding to increase the probability of suc-