WebProfiler: cooperative diagnosis of Web failures

Sharad Agarwal, Nikitas Liogkas, Prashanth Mohan, and Venkata N. Padmanabhan


Despite tremendous growth in the importance and reach of the Web, users have little recourse when a Web page fails to load. Web browsers provide little feedback on such failures, and suggest re-checking the URL or the machine's network settings. Hence, users are often unable to diagnose Web access problems, and resort to haphazardly modifying their settings or simply trying again later. We advocate a client-based collaborative approach for diagnosing Web browsing failures. Our system, WebProfiler, leverages end-host cooperation to pool together observations on the success or failure of Web accesses from multiple vantage points. These are fed into a simple, collaborative blame attribution algorithm. Our evaluation on a controlled testbed shows WebProfiler can accurately diagnose 3.6 times as many failures than possible from a single client's perspective. We present the design and prototype implementation of WebProfiler for an enterprise network.


Publication typeTechReport
InstitutionMicrosoft Research
> Publications > WebProfiler: cooperative diagnosis of Web failures