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The Analysis of Commercial Pattern of Cloud Computing Platform

Published: 18 April 2022 Publication History

Abstract

For solving the problem which there have not suitable commercial pattern of cloud computing platform yet, a commercial pattern of cloud computing platform was proposed and constructed. The solving scheme are provided as follows. After explicitly demand surveyed, function analyzed and framework designed, an standard cloud computing platform with local characteristics and IoT think was constructed within 6 months successfully. After a period of time for operating and managing, for better operating the cloud computing platform, pursuing the continous income and excess profit, a creative and practical commercial pattern of cloud computing platform was presented and applied by this paper, that included in Data Operation (DO) and Joint Credit Reference (JCR), which could solve the problem that the income of many cloud computing platform are not continuous. Then the performance of cloud computing platform was tentatively evaluated by Markov Random Field theory and Belief Propagation algorithm. Moreover, Cumulative Vote Scheme (CVS) which was also presented by this paper was used to analyze and evaluate the performance of commercial pattern of cloud computing platform. Three different random group samples were used to test and evaluate the performance of commercial pattern by independent practical experiments. Finally, the commercial pattern of cloud computing platform was proved to be very effectiveness by experiment results, and the performance of cloud computing platform was very robustness.

References

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C. Kang, W. M. Zheng, Cloud Computing: System Instances and Current Research[J]. Journal of Software, 2009.
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F. F. Moghaddam, S. G. Moghaddam, S. Rouzbeh, A scalable and efficient user authentication scheme for cloud computing environments[C]. 2014 IEEE REGION 10 SYMPOSIUM. IEEE, 2019.
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N. Vaanchig, Z. Qin, B. Ragchaasuren, Constructing secure‐channel free identity‐based encryption with equality test for vehicle‐data sharing in cloud computing[J]. Transactions on Emerging Telecommunications Technologies, 2020.
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L. Jiang, L. D. Xu, H. Cai, An IoT-Oriented Data Storage Framework in Cloud Computing Platform[J]. IEEE Transactions on Industrial Informatics, 2014, 10(2):1443-1451.
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Nurmi D, Wolski R, Grzegorczyk C, The Eucalyptus Open-Source Cloud-Computing System[C]// IEEE/ACM International Symposium on Cluster Computing & the Grid. ACM, 2009.
[6]
J. S. Yedidia, W. T. Freeman, and Y. Weiss., Understanding Belief Propagation and Its Generalizations. Exploring Artificial Intelligence in the New Millennium, 2003, 8, 236-239.

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ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering Conference
February 2022
202 pages
ISBN:9781450387453
DOI:10.1145/3523181
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2022

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  • Research-article
  • Research
  • Refereed limited

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  • Nanning Science and Technology Major Project of China
  • the First Batch Project of China-ASEAN AI Innovation Center in 2021
  • the Development Strategy Study Project of Guangxi Science and Technology in 2021

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ASSE' 22

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