Autonomous Fault Detection in Self-Healing Systems: Comparing Hidden Markov Models and Artificial Neural Networks

Authors:
Chris Schneider;Adam Barker;Simon Dobson
Affiliations:
University of St Andrews, School of Computer Science Fife, Scotland;University of St Andrews, School of Computer Science Fife, Scotland;University of St Andrews, School of Computer Science Fife, Scotland
Venue:
Proceedings of International Workshop on Adaptive Self-tuning Computing Systems
Year:
2014

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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. 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.