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Daleel: simplifying cloud instance selection using machine learning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Publication date25/04/2016
Host publicationNetwork Operations and Management Symposium (NOMS), 2016 IEEE/IFIP
ISBN (electronic)9781509002238
<mark>Original language</mark>English

Publication series

NameNetwork Operations and Management Symposium (NOMS), 2016 IEEE/IFIP
ISSN (electronic)2374-9709


Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules; each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.