GCMD: Genetic Correlation Multi-Domain Virtual Network Embedding Algorithm

被引:13
|
作者
Zhang, Peiying [1 ]
Pang, Xue [1 ]
Kibalya, Godfrey [2 ]
Kumar, Neeraj [3 ,4 ,5 ]
He, Shuqing [6 ]
Zhao, Bin [6 ]
机构
[1] China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
[2] Tech Univ Catalonia UPC, Dept Network Engn, Barcelona 08034, Spain
[3] Thapar Univ, Dept Comp Sci & Engn, Patiala 147004, Punjab, India
[4] Asia Univ, Dept Comp Sci & Informat Engn, Taichung 41354, Taiwan
[5] Univ Petr & Energy Studies, Sch Comp Sci, Dehra Dun 248007, Uttarakhand, India
[6] Linyi Univ, Sch Comp Sci & Engn, Linyi 276000, Shandong, Peoples R China
来源
IEEE ACCESS | 2021年 / 9卷
关键词
Substrates; Virtualization; Indium phosphide; III-V semiconductor materials; Genetic algorithms; Resource management; Genetics; Network virtualization; network function virtualization; virtual network embedding; future internet; cross-domain mapping algorithm; genetic algorithm; INTERNET;
D O I
10.1109/ACCESS.2021.3076916
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the increase of network scale and the complexity of network structure, the problems of traditional Internet have emerged. At the same time, the appearance of network function virtualization (NFV) and network virtualization technologies has largely solved this problem, they can effectively split the network according to the application requirements, and flexibly provide network functions when needed. During the development of virtual network, how to improve network performance, including reducing the cost of embedding process and shortening the embedding time, has been widely concerned by the academia. Combining genetic algorithm with virtual network embedding problem, this paper proposes a genetic correlation multi-domain virtual network embedding algorithm (GCMD-VNE). The algorithm improves the natural selection stage and crossover stage of genetic algorithm, adds more accurate selection formula and crossover conditions, and improves the performance of the algorithm. Simulation results show that, compared with the existing algorithms, the algorithm has better performance in terms of embedding cost and embedding time.
引用
收藏
页码:67167 / 67175
页数:9
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