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范式转换方法，通过调查网络问题而不需要任何监测传感器或主动测量，并且对网络知之甚少或根本不了解。目标是开发准确的、可扩展的和经济有效的网络问题诊断，在没有侵入式主动探测或网络监控的情况下，在不完全知识的情况下对不确定性进行诊断。该项目研究了一种新方法，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，并计算一个组合信念，以实时确定覆盖网络中最可能的根本原因。该项目还研究了基于覆盖路径质量排序的技术。然后将推理结果反馈到自适应主动监控和动态虚拟分配/重新配置系统中，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了强大的诊断能力，以确定问题、描述网络条件、配置调试和故障排除。这些技术既适用于覆盖网络，也适用于传统网络。该项目通过教学和监督学生，培养了该领域训练有素的劳动力。

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.