Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/94751
Title: Profit maximization model for cloud provider based on Windows Azure platform
Authors: Chaisiri, Sivadon
Lee, Bu-Sung
Niyato, Dusit
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2012
Source: Chaisiri, S., Lee, B. S., & Niyato, D. (2012). Profit maximization model for cloud provider based on Windows Azure platform. In Proceedings of 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp.1-4.
Abstract: This paper studies a cloud computing market where a cloud provider rents a set of computing resources from Windows Azure operated by Microsoft. The cloud provider can integrate value-added services to the resources. Then, the services can be sold to customers, and the cloud provider can earn a profit. Moreover, the cloud provider could save much cost and increase higher profit with the 6-month subscription plan offered by Windows Azure. However, the maximization of profit is not trivial to be achieved since the amount of the customers' demand cannot be perfectly known in advance. Consequently, the subscription plan could not be optimally purchased. To deal with such a maximization problem, the paper proposes a stochastic programming model with two-stage recourse. The numerical studies show that the model can maximize the profit under the customers' demand uncertainty.
URI: https://hdl.handle.net/10356/94751
http://hdl.handle.net/10220/8418
DOI: http://dx.doi.org/10.1109/ECTICon.2012.6254333
Rights: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [DOI: http://dx.doi.org/10.1109/ECTICon.2012.6254333].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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