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Resource Management in Cloud Computing with Optimal Pricing Policies

Haiyang Zhang1, Guolong Chen2, Xianwei Li2,3,*

1 School of Environment and Geomatics Engineering, Suzhou University, Suzhou, Anhui Province, China
2 School of Information Engineering, Suzhou University, Suzhou, Anhui Province, China
3 Global Information and Telecommunication Institute, Waseda University, Tokyo, Japan

* Corresponding Author: email

Computer Systems Science and Engineering 2019, 34(4), 249-254. https://doi.org/10.32604/csse.2019.34.249

Abstract

As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue by means of optimal pricing schemes in what is a largely monopolized cloud market. We first study how the revenue of a cloud provider can be maximised using an on-demand pricing scheme. We then turn to the study of revenue maximization with a reserved pricing scheme and, finally, we compare the revenues obtained from the two pricing schemes.

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Cite This Article

APA Style
Zhang, H., Chen, G., Li, X. (2019). Resource management in cloud computing with optimal pricing policies. Computer Systems Science and Engineering, 34(4), 249-254. https://doi.org/10.32604/csse.2019.34.249
Vancouver Style
Zhang H, Chen G, Li X. Resource management in cloud computing with optimal pricing policies. Comput Syst Sci Eng. 2019;34(4):249-254 https://doi.org/10.32604/csse.2019.34.249
IEEE Style
H. Zhang, G. Chen, and X. Li, “Resource Management in Cloud Computing with Optimal Pricing Policies,” Comput. Syst. Sci. Eng., vol. 34, no. 4, pp. 249-254, 2019. https://doi.org/10.32604/csse.2019.34.249

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cc Copyright © 2019 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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