Abnormality detection in the cloud using correlated performance metrics

Sally McClean; Naveed Khan; Adam Currie; Kashaf Khan

doi:10.1007/978-3-030-04191-5_12

Abnormality detection in the cloud using correlated performance metrics

Sally McClean, Naveed Khan^*, Adam Currie, Kashaf Khan

^*Corresponding author for this work

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

Abstract

Virtualisation has revolutionised computing, enabling applications to be quickly provisioned and deployed compared to traditional systems and ensuring that client applications have an ongoing quality of service, with dynamic resourcing in response to demand. However, this requires the use of performance metrics, to recognise current or evolving resourcing situations and ensure timely reprovisioning or redeployment. Associated monitoring systems should thus be aware of not only individual metric behaviours but also of the relationship between related metrics so that system alarms can be triggered when the metrics fall outside normal operational parameters. We here consider multivariate approaches, namely analysis of correlation structure and multivariate exponentially weighted moving averages (MEWMA), for detecting abnormalities in cloud performance data with a view to timely intervention.

Original language	English
Title of host publication	Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings
Editors	Max Bramer, Miltos Petridis
Publisher	Springer Verlag
Pages	159-164
Number of pages	6
ISBN (Print)	9783030041908
DOIs	https://doi.org/10.1007/978-3-030-04191-5_12
Publication status	Published - 2018
Externally published	Yes
Event	38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018 - Cambridge, United Kingdom Duration: 11 Dec 2018 → 13 Dec 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11311 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018
Country/Territory	United Kingdom
City	Cambridge
Period	11/12/2018 → 13/12/2018

Bibliographical note

Funding Information:
Acknowledgement. This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

Abnormality detection
Cloud computing
Multivariate exponentially weighted moving average (MEWMA)
Online monitoring

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-04191-5_12Licence: Unspecified

Cite this

McClean, S., Khan, N., Currie, A., & Khan, K. (2018). Abnormality detection in the cloud using correlated performance metrics. In M. Bramer, & M. Petridis (Eds.), Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings (pp. 159-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11311 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-04191-5_12

McClean, Sally ; Khan, Naveed ; Currie, Adam et al. / Abnormality detection in the cloud using correlated performance metrics. Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings. editor / Max Bramer ; Miltos Petridis. Springer Verlag, 2018. pp. 159-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Virtualisation has revolutionised computing, enabling applications to be quickly provisioned and deployed compared to traditional systems and ensuring that client applications have an ongoing quality of service, with dynamic resourcing in response to demand. However, this requires the use of performance metrics, to recognise current or evolving resourcing situations and ensure timely reprovisioning or redeployment. Associated monitoring systems should thus be aware of not only individual metric behaviours but also of the relationship between related metrics so that system alarms can be triggered when the metrics fall outside normal operational parameters. We here consider multivariate approaches, namely analysis of correlation structure and multivariate exponentially weighted moving averages (MEWMA), for detecting abnormalities in cloud performance data with a view to timely intervention.",

keywords = "Abnormality detection, Cloud computing, Multivariate exponentially weighted moving average (MEWMA), Online monitoring",

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note = "Funding Information: Acknowledgement. This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018 ; Conference date: 11-12-2018 Through 13-12-2018",

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doi = "10.1007/978-3-030-04191-5_12",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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McClean, S, Khan, N, Currie, A & Khan, K 2018, Abnormality detection in the cloud using correlated performance metrics. in M Bramer & M Petridis (eds), Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11311 LNAI, Springer Verlag, pp. 159-164, 38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018, Cambridge, United Kingdom, 11/12/2018. https://doi.org/10.1007/978-3-030-04191-5_12

Abnormality detection in the cloud using correlated performance metrics. / McClean, Sally; Khan, Naveed; Currie, Adam et al.
Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings. ed. / Max Bramer; Miltos Petridis. Springer Verlag, 2018. p. 159-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11311 LNAI).

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

TY - GEN

T1 - Abnormality detection in the cloud using correlated performance metrics

AU - McClean, Sally

AU - Khan, Naveed

AU - Currie, Adam

AU - Khan, Kashaf

N1 - Funding Information: Acknowledgement. This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland. Publisher Copyright: © Springer Nature Switzerland AG 2018. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2018

Y1 - 2018

N2 - Virtualisation has revolutionised computing, enabling applications to be quickly provisioned and deployed compared to traditional systems and ensuring that client applications have an ongoing quality of service, with dynamic resourcing in response to demand. However, this requires the use of performance metrics, to recognise current or evolving resourcing situations and ensure timely reprovisioning or redeployment. Associated monitoring systems should thus be aware of not only individual metric behaviours but also of the relationship between related metrics so that system alarms can be triggered when the metrics fall outside normal operational parameters. We here consider multivariate approaches, namely analysis of correlation structure and multivariate exponentially weighted moving averages (MEWMA), for detecting abnormalities in cloud performance data with a view to timely intervention.

AB - Virtualisation has revolutionised computing, enabling applications to be quickly provisioned and deployed compared to traditional systems and ensuring that client applications have an ongoing quality of service, with dynamic resourcing in response to demand. However, this requires the use of performance metrics, to recognise current or evolving resourcing situations and ensure timely reprovisioning or redeployment. Associated monitoring systems should thus be aware of not only individual metric behaviours but also of the relationship between related metrics so that system alarms can be triggered when the metrics fall outside normal operational parameters. We here consider multivariate approaches, namely analysis of correlation structure and multivariate exponentially weighted moving averages (MEWMA), for detecting abnormalities in cloud performance data with a view to timely intervention.

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KW - Cloud computing

KW - Multivariate exponentially weighted moving average (MEWMA)

KW - Online monitoring

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SN - 9783030041908

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings

A2 - Bramer, Max

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T2 - 38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018

Y2 - 11 December 2018 through 13 December 2018

ER -

McClean S, Khan N, Currie A, Khan K. Abnormality detection in the cloud using correlated performance metrics. In Bramer M, Petridis M, editors, Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings. Springer Verlag. 2018. p. 159-164. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2018 Nov 16. doi: 10.1007/978-3-030-04191-5_12

Abnormality detection in the cloud using correlated performance metrics

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this