Fault Detection and Diagnosis from the Logging and Bookkeping Data

Xiangliang Zhang 1 Michèle Sebag 1 Cecile Germain-Renaud 1
1 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France, X - École polytechnique, CNRS - Centre National de la Recherche Scientifique : UMR7161
Abstract : Autonomic Computing (AC) is defined as ‘computing systems that manage themselves in accordance with high-level objectives from humans'. AC is now a well-established scientific domain, and a priority for industry. Automated detection, diagnosis, and ultimately management, of software/hardware problems define autonomic dependability. The paper reports on applying state of the art autonomic dependability methods to the Logging and Bookkeeping data, with promising results on detection.
Type de document :
Communication dans un congrès
2nd EGEE User Forum, May 2007, Manchester, United Kingdom. 2007
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https://hal.inria.fr/inria-00174286
Contributeur : Cecile Germain <>
Soumis le : lundi 24 septembre 2007 - 09:20:52
Dernière modification le : jeudi 10 mai 2018 - 02:06:55

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  • HAL Id : inria-00174286, version 1

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Xiangliang Zhang, Michèle Sebag, Cecile Germain-Renaud. Fault Detection and Diagnosis from the Logging and Bookkeping Data. 2nd EGEE User Forum, May 2007, Manchester, United Kingdom. 2007. 〈inria-00174286〉

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