Autonomous fault detection in self-healing systems using Restricted Boltzmann Machines

Christopher Schneider, Adam David Barker, Simon Andrew Dobson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014)
Place of PublicationLaurel, MD
Publication statusAccepted/In press - 24 Sept 2014
Event11th IEEE International Conference and Workshops on the Engineering of Autonomic and Autonomous Systems - John Hopkins Applied Physics Laboratory , Laurel, Maryland, United States
Duration: 24 Sept 201426 Sept 2014

Conference

Conference11th IEEE International Conference and Workshops on the Engineering of Autonomic and Autonomous Systems
Country/TerritoryUnited States
CityLaurel, Maryland
Period24/09/1426/09/14

Keywords

  • Self-healing systems
  • Fault detection
  • Machine learning
  • Computational intelligence
  • Autonomic computing
  • Artificial neural networks
  • Restricted Boltzmann Machines

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