Abnormality detection in the cloud using correlated performance metrics

Sally I McClean, Naveed Khan, Adam Currie, Kashaf Khan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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
Number of pages6
ISBN (Print)9783030041908
Publication statusPublished online - 16 Nov 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


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