A dynamic programming-based approach for cloud instance type selection and optimisation. (23rd April 2020)

• Record Type: Journal Article
• Authors: Wang, Pengwei; Zhou, Wanjun; Zhao, Caihui; Lei, Yinghui; Zhang, Zhaohui
• Is Part Of: International journal of information technology and management. Volume 19:Number 4(2020)
• Page Start: 358
• Page End: 375
• Publication Date: 2020-04-23
• Subjects: cloud computing -- cloud instance type -- dynamic programming -- selection -- optimisation -- knapsack problem; Management information systems -- Periodicals; Information technology -- Periodicals; Management -- Data processing -- Periodicals; 658.403805
• Languages: English
• ISSNs: 1461-4111

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.