Dynamic Investment and Retirement Operations of Cloud Computing Service Management

26 Pages Posted: 7 Dec 2022

See all articles by Rongjinzi Wang

Rongjinzi Wang

Peking University

Jie Song

Peking University

Yunzhe Qiu

Peking University - Department of Information Management

Date Written: November 24, 2022

Abstract

The emerging market of cloud computing encourages cloud service providers (CSPs) to deliver efficient and effective services on the basis of appropriate provisioning of service resources. Overprovisioning and underprovisioning of service capacity lead to the wastage of cloud resources and degradation in service performance, respectively, causing unnecessary mismatching costs. In this paper, we study the cloud service management problem to find optimal dynamic investment and retirement strategies over the complete life cycle of a cloud service product involving growth, maturity, and declining stages. We extend the traditional dynamic inventory model to the declining stage by showing that time-dependent (st, St) policies remain optimal, and propose a near-optimal Adaptive-Approximation-Algorithm-Based policy with the guaranteed performance of 3. We also design a data-driven framework for the cloud service management problem based on the auxiliary data predictive of the demand and utilize predictive prescriptive methods to operate the online investment and retirement strategies. Numerical experiments show that predictive prescriptive methods significantly improve the effectiveness of cloud service management by reducing costs and improving the coefficient of prescriptiveness.

Keywords: cloud computing, service resource management, life cycle management, predictive prescription, (s, S) policy

JEL Classification: C61, M15

Suggested Citation

Wang, Rongjinzi and Song, Jie and Qiu, Yunzhe, Dynamic Investment and Retirement Operations of Cloud Computing Service Management (November 24, 2022). Available at SSRN: https://ssrn.com/abstract=4285651 or http://dx.doi.org/10.2139/ssrn.4285651

Rongjinzi Wang

Peking University ( email )

No. 60 Yannan Park
Beijing, 100871
China
100871 (Fax)

Jie Song

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, 100871
China

Yunzhe Qiu (Contact Author)

Peking University - Department of Information Management ( email )

Beijing, 100087
China

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