Research on QoS Flow Path Intelligent Allocation of Multi-Services in 5G and Industrial SDN Heterogeneous Network for Smart Factory

Qing Guo, Qibing Jin, Zhen Liu, Mingshi Luo, Liangchao Chen (), Zhan Dou and Xu Diao ()
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Qing Guo: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Qibing Jin: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Zhen Liu: College of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, China
Mingshi Luo: School of Computer Science, Xi’an Shiyou University, Xi’an 710065, China
Liangchao Chen: College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Zhan Dou: College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Xu Diao: China Academy of Safety Science and Technology, Beijing 100012, China

Sustainability, 2023, vol. 15, issue 15, 1-15

Abstract: In this paper, an intelligent multiple Quality of Service (QoS) constrained traffic path allocation scheme with corresponding algorithm is proposed. The proposed method modifies deep Q-learning network (DQN) by graph neural network (GNN) and prioritized experience replay to fit the heterogeneous network, which is applied for production management and edge intelligent applications of smart factory. Moreover, through designing the reward function, the learning efficiency of the agent is improved under the sparse reward condition, and the multi-object optimization is realized. The simulation results show that the proposed method has high learning efficiency, and strong generalization ability adapting the changing of topological structure of network caused by network error, which is more suitable than the compared methods. In addition, it is also verified that combining the field knowledge and deep reinforcement learning (DRL) can improve the performance of the agent. The proposed method can achieve good performance in the network slicing scenario as well.

Keywords: heterogeneous network; edge computing; multiple parameter QoS traffic flow path allocation; deep reinforcement learning; reward function optimization; network slicing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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