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Autonomous fault detection in self-healing systems using Restricted Boltzmann Machines
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dc.contributor.author | Schneider, Christopher | |
dc.contributor.author | Barker, Adam David | |
dc.contributor.author | Dobson, Simon Andrew | |
dc.date.accessioned | 2015-07-06T16:10:00Z | |
dc.date.available | 2015-07-06T16:10:00Z | |
dc.date.issued | 2014-09-24 | |
dc.identifier.citation | Schneider , C , Barker , A D & Dobson , S A 2014 , Autonomous fault detection in self-healing systems using Restricted Boltzmann Machines . in Proceedings of the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014) . Laurel, MD , 11th IEEE International Conference and Workshops on the Engineering of Autonomic and Autonomous Systems , Laurel, Maryland , United States , 24/09/14 . | en |
dc.identifier.citation | conference | en |
dc.identifier.other | PURE: 155034863 | |
dc.identifier.other | PURE UUID: 86a13b07-6bb6-455a-a9ba-7560baad2327 | |
dc.identifier.uri | https://hdl.handle.net/10023/6916 | |
dc.description | Funding for this research was provided by the Scottish Informatics and Computer Science Alliance (SICSA). | en |
dc.description.abstract | 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, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information. | |
dc.language.iso | eng | |
dc.relation.ispartof | Proceedings of the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014) | en |
dc.rights | © © 2014. IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
dc.subject | Self-healing systems | en |
dc.subject | Fault detection | en |
dc.subject | Machine learning | en |
dc.subject | Computational intelligence | en |
dc.subject | Autonomic computing | en |
dc.subject | Artificial neural networks | en |
dc.subject | Restricted Boltzmann Machines | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject.lcc | QA75 | en |
dc.title | Autonomous fault detection in self-healing systems using Restricted Boltzmann Machines | en |
dc.type | Conference item | en |
dc.description.version | https://doi.org/Postprint | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.identifier.url | http://tab.computer.org/aas/ease/2014/index.html | en |
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