Title: A dynamic programming-based approach for cloud instance type selection and optimisation

Authors: Pengwei Wang; Wanjun Zhou; Caihui Zhao; Yinghui Lei; Zhaohui Zhang

Addresses: School of Computer Science and Technology, Donghua University, Shanghai, China ' School of Computer Science and Technology, Donghua University, Shanghai, China ' School of Computer Science and Technology, Donghua University, Shanghai, China ' School of Computer Science and Technology, Donghua University, Shanghai, China ' School of Computer Science and Technology, Donghua University, Shanghai, China

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.

Keywords: cloud computing; cloud instance type; dynamic programming; selection; optimisation; knapsack problem.

DOI: 10.1504/IJITM.2020.10028804

International Journal of Information Technology and Management, 2020 Vol.19 No.4, pp.358 - 375

Received: 20 Jul 2018
Accepted: 26 Mar 2019

Published online: 23 Apr 2020 *

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