Title :
Detecting application-level failures in component-based Internet services
Author :
Kiciman, Emre ; Fox, Armando
Author_Institution :
Dept. of Comput. Sci., Stanford Univ., CA, USA
Abstract :
Most Internet services (e-commerce, search engines, etc.) suffer faults. Quickly detecting these faults can be the largest bottleneck in improving availability of the system. We present Pinpoint, a methodology for automating fault detection in Internet services by: 1) observing low-level internal structural behaviors of the service; 2) modeling the majority behavior of the system as correct; and 3) detecting anomalies in these behaviors as possible symptoms of failures. Without requiring any a priori application-specific information, Pinpoint correctly detected 89%-96% of major failures in our experiments, as compared with 20%-70% detected by current application-generic techniques.
Keywords :
Internet; authorisation; information services; Pinpoint; anomaly detection; application-generic techniques; application-level failures; component-based Internet services; e-commerce; fault detection; search engines; Application software; Availability; Computer science; Computerized monitoring; Condition monitoring; Fault detection; Prototypes; Search engines; Testing; Web and internet services; Anomaly detection; Internet services; application-level failures; Algorithms; Artifacts; Artificial Intelligence; Computer Simulation; Information Storage and Retrieval; Internet; Models, Statistical; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Telecommunications;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.853411