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CISE Postdoctoral Research Associates in Experimental Computer Science: Support for Developing Highly Available Distributed Applications

CISE Postdoctoral Research Associates in Experimental Computer Science: Support for Developing Highly Available Distributed Applications
CISE 实验计算机科学博士后研究员:支持开发高度可用的分布式应用程序
批准号:
9901592
负责人:
Nancy Lynch
金额:
$6.6万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-08-15 至 2001-07-31

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中文摘要
翻译
9901592 Lynch,Nancy AMassachusetts理工学院CSE实验计算机科学博士后研究助理:支持开发高可用的分布式应用程序这项高可用的分布式算法和系统的研究重点是组通信服务及其应用。群组通信技术在学术界和工业界得到了普遍的应用,群组通信范式的重要性也得到了广泛的认可。该工作的目标是:1)基于群组通信范型,构建一个用于构建群件和计算机支持的协同工作应用的综合框架;2)以群组通信系统为重点,形式化地建模复杂的分布式系统,并以允许验证实际实现的方式实现算法和通信协议;3)更多地了解容错分布式系统的内在局限性以及在构建此类系统时所涉及的权衡。
英文摘要
9901592Lynch, Nancy AMassachusetts Institute of TechnologyCISE Postdoctoral Research Associates in Experimental Computer Science: Support for Developing Highly Available Distributed ApplicationsThe focus of this research in highly available distributed algorithms and systems is on groupcommunications services and their applications. Group communication technology is becoming commonly exploited in academia and industry, and the significance of the group communication paradigm is becoming widely acknowledged. The goals of the work are to 1) construct a comprehensive framework for the construction of groupware and computer supported cooperative work applications, based on the group communication paradigm, 2) model complex distributed systems formally, focusing on group communication systems, and implement algorithms and communication protocols in a way that would allow verification of the actual implementation, and 3) understand more of the inherent limitations of fault tolerant distributed systems and the tradeoffs involved in building such systems.
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会议论文
AF: Small: An Algorithmic Theory of Brain Behavior: Concept Representation and Learning in Spiking Neural Networks
AF: Small: Distributed Algorithms for Dynamic, Noisy Platforms: Wireless Networks, Robot Swarms, and Insect Colonies
NSF-BSF: AF: Small: An Algorithmic Theory of Brain Networks
AF: Medium: Distributed Algorithms for Resource-Constrained and Dynamic Settings
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