CSR: Small: Collaborative Research: Towards Collaborative Overlay Problem Diagnosis Using Evidential Reasoning and Adaptive Monitoring

CSR：小型：协作研究：使用证据推理和自适应监控进行协作叠加问题诊断

• 批准号: 1017237
• 负责人: Ehab Al-Shaer
• 金额: $ 25万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017237&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Towards

This project presents a new paradigm-shift approach in fault diagnosis by investigating network problems without requiring any monitoring sensors or active measurements, and assuming little or no knowledge about the network. The goal is to develop accurate, scalable and cost-effective network problem diagnosis that reason about uncertainty in case of incomplete knowledge without intrusive active probing or network monitoring. This project investigates a novel approach that uses evidential reasoning based on user observations to analyze the end-user views as evidence and compute a combined belief for determining the most possible root causes in overlay networks at real-time. The project also investigates techniques to rank the overlay paths based on their quality. The reasoning results can then be fedback into adaptive active monitoring, and dynamic virtual assignment/reconfiguration systems to optimize problem monitoring and recovery, respectively. Developing techniques and tools that enable sharing and analyzing end-host observations provide powerful diagnosing capabilities to service providers, system developers, and administrators to in problem determination, characterizing network conditions, configuration debugging and troubleshooting. These techniques are applicable on both overlay and traditional networks. This project enables trained workforce in this area through teaching and supervising students.

该项目提出了一种新的范式转换方法，在故障诊断调查网络问题，而不需要任何监测传感器或主动测量，并假设很少或根本没有知识的网络。我们的目标是开发准确，可扩展和具有成本效益的网络问题诊断，在不完整的知识的情况下，无需侵入式主动探测或网络监控的不确定性的原因。该项目研究了一种新的方法，该方法使用基于用户观察的证据推理来分析最终用户的观点作为证据，并计算组合信念，以确定覆盖网络中最可能的根本原因。该项目还研究了基于质量对重叠路径进行排名的技术。然后，推理结果可以反馈到自适应主动监测，和动态虚拟分配/重新配置系统，以优化问题的监测和恢复，分别。开发能够共享和分析终端主机观察结果的技术和工具为服务提供商、系统开发人员和管理员提供强大的诊断能力，以确定问题、表征网络条件、配置调试和故障排除。这些技术适用于覆盖网络和传统网络。该项目通过教学和监督学生，使这一领域的劳动力得到培训。