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dc.contributor.authorSchneider, Christopher
dc.contributor.authorBarker, Adam David
dc.contributor.authorDobson, Simon Andrew
dc.date.accessioned2015-04-24T09:01:09Z
dc.date.available2015-04-24T09:01:09Z
dc.date.issued2014-01-22
dc.identifier.citationSchneider , 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 , 22/01/14 . https://doi.org/10.1145/2553062.2553065en
dc.identifier.citationworkshopen
dc.identifier.isbn9781450325141
dc.identifier.otherPURE: 93686326
dc.identifier.otherPURE UUID: 1f3233c8-8e91-44a1-a4d9-b7f61befacf4
dc.identifier.otherScopus: 84893948401
dc.identifier.otherORCID: /0000-0001-9633-2103/work/70234148
dc.identifier.urihttp://hdl.handle.net/10023/6567
dc.descriptionFunding for this research was provided by the Scottish Informatics and Computer Science Alliance.en
dc.description.abstractAutonomously 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.
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systemsen
dc.rights© 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.orgen
dc.subjectSelf-healing systemsen
dc.subjectFault detectionen
dc.subjectMachine learningen
dc.subjectAutonomic computingen
dc.subjectArtificial neural networksen
dc.subjectHidden Markov Modelsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleAutonomous fault detection in self-healing systems : comparing Hidden Markov Models and artificial neural networksen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1145/2553062.2553065
dc.identifier.urlhttp://dl.acm.org/citation.cfm?id=2553062en
dc.identifier.urlhttp://adapt-workshop.org/2014/en


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