A dynamic programming-based approach for cloud instance type selection and optimisation
by Pengwei Wang; Wanjun Zhou; Caihui Zhao; Yinghui Lei; Zhaohui Zhang
International Journal of Information Technology and Management (IJITM), Vol. 19, No. 4, 2020

Abstract: With the advantages of cloud computing gradually highlighted, users increasingly want to deploy their applications and services on the cloud to reduce costs and obtain high computing capacity. Nowadays, cloud providers (e.g., Amazon, Microsoft) at home and abroad provide a large amount of cloud instance types optimised to fit different use cases, such as compute optimised and memory optimised. Due to the potentially large quantity of cloud instance types in the public cloud market, it is often a challenge for users to select an optimal set of cloud instance types subject to limited resource capacity. In this paper, a dynamic programming-based approach is proposed for cloud instance type selection, which can provide optimal combination of cloud instance types to users. Experiments are performed based on real-world cloud information to evaluate the proposed method.

Online publication date: Mon, 12-Oct-2020

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