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Machine Learning Based Resource Allocation of Cloud Computing in Auction

Jixian Zhang1, Ning Xie1, Xuejie Zhang1, Kun Yue1, Weidong Li2,*, Deepesh Kumar3
School of Information Science and Engineering, Yunnan University, Kunming 650504, PR China.
School of Mathematics and Statistics, Yunnan University, Kunming 650504, PR China.
Computer Science & Engineering SSITM, Aktu, Lucknow 226014, India.
* Corresponding Author: Weidong Li. Email: .

Computers, Materials & Continua 2018, 56(1), 123-135. https://doi.org/ 10.3970/cmc.2018.03728

Abstract

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.

Keywords

Cloud computing, resource allocation, machine learning, linear regression, logistic regression.

Cite This Article

J. . Zhang, N. . Xie, X. . Zhang, K. . Yue, W. . Li et al., "Machine learning based resource allocation of cloud computing in auction," Computers, Materials & Continua, vol. 56, no.1, pp. 123–135, 2018.



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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