Ensembles of Models for Automated Diagnosis of System Performance Problems
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Why do internet services fail, and what can be done about it?
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Semantic-Driven Model Composition for Accurate Anomaly Diagnosis
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Guided Problem Diagnosis through Active Learning
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
On the use of computational geometry to detect software faults at runtime
Proceedings of the 7th international conference on Autonomic computing
Detecting application-level failures in component-based Internet services
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Complex software systems have become commonplace in modern organizations and are considered critical to their daily operations. They are expected to run on a diverse set of platforms while interoperating with a wide variety of other applications. Although there have been advances in the discipline of software engineering, software faults, and malicious attacks still regularly cause system downtime [1]. Downtime of critical applications can create additional work, cause delays, and lead to financial loss [2]. This paper presents a computational geometry technique to tackle the problem of timely failure diagnosis during the execution of a software application. Our approach to failure diagnosis involves collecting a set of software metrics and building a geometric enclosures corresponding to known classes of faults. The geometric enclosures are then used to partition the state space defined by the metrics