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 proceedingConference 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 languageEnglish
Title of host publicationArtificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings
EditorsMax Bramer, Miltos Petridis
PublisherSpringer Verlag
Pages159-164
Number of pages6
ISBN (Print)9783030041908
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018 - Cambridge, United Kingdom
Duration: 11 Dec 201813 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11311 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018
CountryUnited Kingdom
CityCambridge
Period11/12/201813/12/2018

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
  • Computer Science(all)

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