Autonomous fault detection in self-healing systems: comparing Hidden Markov Models and artificial neural networks

Christopher Schneider; Adam David Barker; Simon Andrew Dobson

doi:10.1145/2553062.2553065

Autonomous fault detection in self-healing systems: comparing Hidden Markov Models and artificial neural networks

Christopher Schneider, Adam David Barker, Simon Andrew Dobson

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	ADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systems
Place of Publication	New York, NY
Publisher	ACM
Pages	24-31
ISBN (Print)	9781450325141
DOIs	https://doi.org/10.1145/2553062.2553065
Publication status	Published - 22 Jan 2014
Event	4th International Workshop on Adaptive Self-tuning Computing Systems - Vienna Marriott Hotel, Ballroom B, Vienna, Australia Duration: 22 Jan 2014 → 22 Jan 2014

Workshop

Workshop	4th International Workshop on Adaptive Self-tuning Computing Systems
Country/Territory	Australia
City	Vienna
Period	22/01/14 → 22/01/14

Keywords

Self-healing systems
Fault detection
Machine learning
Autonomic computing
Artificial neural networks
Hidden Markov Models

Access to Document

10.1145/2553062.2553065

EASe_2014
© 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.org
Accepted author manuscript, 127 KB

Cite this

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title = "Autonomous fault detection in self-healing systems: comparing Hidden Markov Models and artificial neural networks",

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 improvesthe state of the art by allowing self-healing systems to detect faults with greater autonomy than existing methodologies, and thus further reduce operational costs.",

keywords = "Self-healing systems, Fault detection, Machine learning, Autonomic computing, Artificial neural networks, Hidden Markov Models",

author = "Christopher Schneider and Barker, {Adam David} and Dobson, {Simon Andrew}",

note = "Funding for this research was provided by the Scottish Informatics and Computer Science Alliance. ; 4th International Workshop on Adaptive Self-tuning Computing Systems ; Conference date: 22-01-2014 Through 22-01-2014",

year = "2014",

month = jan,

day = "22",

doi = "10.1145/2553062.2553065",

language = "English",

isbn = "9781450325141",

pages = "24--31",

booktitle = "ADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systems",

publisher = "ACM",

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}

Schneider, C, Barker, AD & Dobson, SA 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/10.1145/2553062.2553065

Autonomous fault detection in self-healing systems: comparing Hidden Markov Models and artificial neural networks. / Schneider, Christopher; Barker, Adam David; Dobson, Simon Andrew.
ADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systems. New York, NY: ACM, 2014. p. 24-31.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Autonomous fault detection in self-healing systems

T2 - 4th International Workshop on Adaptive Self-tuning Computing Systems

AU - Schneider, Christopher

AU - Barker, Adam David

AU - Dobson, Simon Andrew

N1 - Funding for this research was provided by the Scottish Informatics and Computer Science Alliance.

PY - 2014/1/22

Y1 - 2014/1/22

N2 - 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 improvesthe state of the art by allowing self-healing systems to detect faults with greater autonomy than existing methodologies, and thus further reduce operational costs.

AB - 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 improvesthe state of the art by allowing self-healing systems to detect faults with greater autonomy than existing methodologies, and thus further reduce operational costs.

KW - Self-healing systems

KW - Fault detection

KW - Machine learning

KW - Autonomic computing

KW - Artificial neural networks

KW - Hidden Markov Models

UR - http://dl.acm.org/citation.cfm?id=2553062

UR - http://adapt-workshop.org/2014/

U2 - 10.1145/2553062.2553065

DO - 10.1145/2553062.2553065

M3 - Conference contribution

SN - 9781450325141

SP - 24

EP - 31

BT - ADAPT '14 The 4th International Workshop on Adaptive Self-tuning Computing Systems

PB - ACM

CY - New York, NY

Y2 - 22 January 2014 through 22 January 2014

ER -

Autonomous fault detection in self-healing systems: comparing Hidden Markov Models and artificial neural networks

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Keywords

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