Autonomous fault detection in self-healing systems : comparing Hidden Markov Models and artificial neural networks
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Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults. Specifically, when historical feature data is present, Hidden Markov Models can be used to heuristically identify the root cause of a fault in an unsupervised manner. This approach improves the state of the art by allowing self-healing systems to detect faults with greater autonomy than existing methodologies, and thus further reduce operational costs.
Schneider , C , Barker , A D & Dobson , S A 2014 , Autonomous fault detection in self-healing systems : comparing Hidden Markov Models and artificial neural networks . in ADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systems . ACM , New York, NY , pp. 24-31 , 4th International Workshop on Adaptive Self-tuning Computing Systems , Vienna , Australia , 22-22 January . DOI: 10.1145/2553062.2553065workshop
ADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systems
© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 4th International Workshop on Adaptive Self-Tuning Systems, ADAPT 2014, available from http://dl.acm.org
Funding for this research was provided by the Scottish Informatics and Computer Science Alliance.
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