Autonomous fault detection in self-healing systems using Restricted Boltzmann Machines
MetadataShow full item record
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.
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 , 24-26 September .conference
Proceedings of the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014)
© © 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.
Funding for this research was provided by the Scottish Informatics and Computer Science Alliance (SICSA).
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.