Daleel: simplifying cloud instance selection using machine learning

SAMREEN, Faiza, ELKHATIB, Yehia, ROWE, Matthew and BLAIR, Gordon S (2016). Daleel: simplifying cloud instance selection using machine learning. In: NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. IEEE.

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Open Access URL: https://ieeexplore.ieee.org/document/7502858
Link to published version:: https://doi.org/10.1109/noms.2016.7502858
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    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.

    Item Type: Book Section
    Identification Number: https://doi.org/10.1109/noms.2016.7502858
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 17 Oct 2022 16:04
    Last Modified: 17 Oct 2022 16:04
    URI: https://shura.shu.ac.uk/id/eprint/26225

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