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Issue title: Fuzzy Systems for Medical Image Analysis
Guest editors: Weiping Zhang
Article type: Research Article
Authors: Liu, Yunpenga | Dong, Xinlingb; *
Affiliations: [a] College of Information Engineering, Jiaozuo University, Jiaozuo, Henan, China | [b] China Institute of Defence Science and Technology, Beijing, China
Correspondence: [*] Corresponding author. Xinling Dong, China Institute of Defence Science and Technology, Beijing, China. E-mail: [email protected].
Abstract: Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and efficiency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved “learn to use”. Apply the design of adaptive fuzzy control to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased.
Keywords: Fuzzy Algorithm, Network Virtualization, Cloud Computing, Network Load
DOI: 10.3233/JIFS-179602
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3793-3801, 2020
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