Analyzing Web Logs to Detect User-Visible Failures

Web applications suffer from poor reliability. Practitioners commonly rely on fast failure detection to recover their applications quickly to reduce the effects of the failures on other users. In this paper, we present a technique for detecting user-visible failures by analyzing Web logs. Our technique applies a first-order Markov model to infer anomalous browsing behavior discovered in Web logs as indicators that users have encountered failures. We implemented our technique in a tool called REBA (REcursive Byesian Analysis of Web Logs). We evaluated our technique using REBA applied to the Web site of NASA. The results demonstrate that our technique can detect user-visible failures with reasonable cost.