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|
|Number of pages||6|
|Publication status||Published online - 16 Nov 2018|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|