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.
|Title of host publication||Artificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018: Proceedings|
|Editors||Max Bramer, Miltos Petridis|
|Number of pages||6|
|Publication status||Published - 2018|
|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
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018|
|Period||11/12/2018 → 13/12/2018|
Bibliographical noteFunding Information:
Acknowledgement. This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.
© Springer Nature Switzerland AG 2018.
Copyright 2019 Elsevier B.V., All rights reserved.
- Abnormality detection
- Cloud computing
- Multivariate exponentially weighted moving average (MEWMA)
- Online monitoring
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)