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.
