Combining visualization and statistical analysis to improve operator confidence and efficiency for failure detection and localization

Web applications suffer from software and configuration faults that lower their availability. Recovering from failure is dominated by the time interval between when these faults appear and when they are detected by site operators. We introduce a set of tools that augment the ability of operators to perceive the presence of failure: an automatic anomaly detector scours HTTP access logs to find changes in user behavior that are indicative of site failures, and a visualizer helps operators rapidly detect and diagnose problems. Visualization addresses a key question of autonomic computing of how to win operators' confidence so that new tools will be embraced. Evaluation performed using HTTP logs from demonstrates that these tools can enhance the detection of failure as well as shorten detection time. Our approach is application-generic and can be applied to any Web application without the need for instrumentation

Published in:
Proceedings. Second International Conference on Autonomic Computing, 89-100
visualization;statistical analysis;operator confidence;failure detection;failure localization;Web applications;software fault;configuration fault;software availability;failure recovery;automatic anomaly detector;HTTP access logs;user behavior;site failures;failure diagnosis;autonomic computing;;

 Record created 2006-12-22, last modified 2018-03-17

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