ITR/SY Algorithmic Issues in Large Scale Dynamic Networks

大规模动态网络中的 ITR/SY 算法问题

• 批准号: 0121154
• 负责人: Eli Upfal
• 金额: $ 52.4万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0121154&HistoricalAwards=false
• 关键词: ITR; SY; Algorithmic; Issues; Large

Algorithmic Issues in Large Scale Dynamic NetworksMichael Mitzenmacher, Harvard UniversityEli Upfal, Brown UniversityWe propose to develop a theoretically well-founded framework for thedesign and analysis of algorithms for large scale dynamic networks.In particular, for the Web and related dynamic networks, such as the underlying Internet topology andInternet-based peer to peer ad hoc networks. We plan to develop rigorous mathematical models that capture keycharacteristics and can make reliable predictions about features suchas connectivity, information content, and dynamic of these networks.We plan to apply this framework to test existingalgorithms and construct improved new algorithms.The mail benefits of developing the mathematical models of the Web structure and dynamics will bethe improved theoretical foundation for the design, analysis, andtesting of algorithms that operate in the web environment. The tangible results of this work will therefore be models that can besubjected to experimental verification, analyses of algorithms basedupon these models, new algorithms that benefit from these analyses,and, finally, proof-of-concept demonstrations and experimentalevaluations of such algorithms.

大规模动态网络中的算法问题Michael Mitzenmacher，哈佛大学Eli Upfal，布朗大学我们建议开发一个理论基础良好的框架，用于大规模动态网络算法的设计和分析。特别是对于Web和相关动态网络，例如底层互联网拓扑和基于互联网的点对点自组织网络。我们计划开发严格的数学模型，捕获关键特征，并能够对这些网络的连通性、信息内容和动态等特征做出可靠的预测。我们计划应用这个框架来测试现有算法并构建改进的新算法。开发网络结构和动态的数学模型的主要好处将是为在网络环境中运行的算法的设计、分析和测试提供改进的理论基础。因此，这项工作的切实成果将是可以进行实验验证的模型、基于这些模型的算法分析、受益于这些分析的新算法，以及最后对此类算法进行概念验证演示和实验评估。