Automatic software interference detection in parallel applications
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
R-functions based classification for abnormal software process detection
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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Fault detection is one of the important constituents of fault tolerance, which in turn defines the dependability of autonomic computing. In presented work several pattern recognition tools were investigated in application to early fault detection. The optimal margin classifier technique was utilized to detect the abnormal behavior of software processes. The comparison with the performance of the quadratic classifiers is reported. The optimal margin classifiers were also implemented to the fault detection in hardware components. The impulse parameter probing technique was introduced to mitigate intermittent and transient fault problems. The pattern recognition framework of analysis of responses to a controlled component perturbation yielded promising results.