Emre Kiciman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Emre Kiciman has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
A. Fox
Dept. of Comput. Sci., Stanford Univ., CA, USA
1 shared publication- Armando Fox
University of California, Berkeley, Berkeley, USA
1 shared publication - George Candea
Stanford University
1 shared publication - Shinichi Kawamoto
Stanford University
1 shared publication
Latest Publications
- 1 April 2006
- Published by Springer Nature in Cluster Computing
- Vol. 9 (2) , 175-190
Autonomous recovery in componentized Internet applications
- 1 April 2006
- Published by Springer Nature in Cluster Computing
- Vol. 9 (2) , 175-190
Autonomous recovery in componentized Internet applications
- 19 September 2005
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 16 (5) , 1027-1041
Detecting Application-Level Failures in Component-Based Internet Services
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.
- 19 September 2005
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 16 (5) , 1027-1041
Detecting Application-Level Failures in Component-Based Internet Services
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.