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

该项目提出了一种新的故障诊断范式转变方法，通过调查网络问题，无需任何监控传感器或主动测量，并且假设对网络知之甚少或根本不了解。目标是开发准确、可扩展且经济高效的网络问题诊断，在知识不完整的情况下推断不确定性，而无需侵入式主动探测或网络监控。该项目研究了一种新颖的方法，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，并计算综合信念，以实时确定覆盖网络中最可能的根本原因。该项目还研究了根据覆盖路径的质量对覆盖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控和动态虚拟分配/重新配置系统中，以分别优化问题监控和恢复。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功能，帮助他们确定问题、描述网络状况、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，培养该领域受过培训的劳动力。

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.