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Technology of Network & Communication
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1809-1813,1819

High-reliability multi-domain virtual network mapping algorithm based on reinforcement learning

Zhao Jihong1,2
Song Hang1
Qu Hua2
Lei Zhilin1
1. School of Communication & Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
2. School of Electronic & Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Abstract

Most of the existing virtual network mapping algorithms rely on manual rules to sort nodes and determine the sequence of node mapping so as to optimize node mapping and improve the success rate of virtual network requests. In the link mapping stage, it generally uses the breadth-first search algorithm, ignoring the strong correlation between node resources and link resources, so that it can only obtain local optimal mapping results. In response to the above problems, based on the 5G multi-domain heterogeneous network environment, from the perspective of network survivability protection, this paper proposed a virtual network mapping algorithm using two-layer reinforcement learning. It applied reinforcement learning to both the node and link stages of network mapping, used the gradient strategy and back propagation method to train the network model of this paper, and used the training model of this paper to complete the mapping. The simulation results show that, compared with the comparison algorithms, the algorithm optimizes the link mapping while optimizing the node mapping, and achieves better results in the mapping success rate, long-term return rate, and node and link utilization rate.

Foundation Support

国家自然科学基金资助项目(61531013)
国家重点研发计划重点专项资助项目(2018YFB1800300)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0594
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Technology of Network & Communication
Pages: 1809-1813,1819
Serial Number: 1001-3695(2022)06-035-1809-05

Publish History

[2022-01-11] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

赵季红, 宋航, 曲桦, 等. 基于强化学习的高可靠性多域虚拟网络映射算法 [J]. 计算机应用研究, 2022, 39 (6): 1809-1813,1819. (Zhao Jihong, Song Hang, Qu Hua, et al. High-reliability multi-domain virtual network mapping algorithm based on reinforcement learning [J]. Application Research of Computers, 2022, 39 (6): 1809-1813,1819. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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