Intelligent routing method based on Dueling DQN reinforcement learning and network traffic state prediction in SDN.

Autor:	Huang, Linqiang, Ye, Miao, Xue, Xingsi, Wang, Yong, Qiu, Hongbing, Deng, Xiaofang
Předmět:	COMPUTER network traffic REINFORCEMENT learning DEEP reinforcement learning MACHINE learning ROUTING algorithms SOFTWARE-defined networking
Zdroj:	Wireless Networks (10220038); Jul2024, Vol. 30 Issue 5, p4507-4525, 19p
Abstrakt:	The traditional routing method makes use of limited information on the network links to make routing decisions, which makes it difficult to adapt to the dynamic and complex network and adjust the router's forward strategy. To address these issues, this paper proposes an intelligent routing method based on the Software Defined Network (SDN), Dueling DQN (a Deep Reinforcement Learning algorithm) and network traffic state prediction. First, the global network awareness information is obtained with the SDN network measurement mechanism, which is converted into a traffic matrix consisting of multiple network link status information such as bandwidth and delay, etc. Then, the optimal forwarding route under the current network state is generated by predicting the network traffic matrix and the Dueling DQN. The experimental results show that: (1) compared with the traditional Dijkstra and OSPF routing methods, the proposed method significantly improves the network throughput and effectively reduces the network delay and packet loss rate; (2) comparing with the reinforcement learning algorithms DDPG and PPO, the proposed approach achieves a faster convergence state, which improves the efficiency of network routing. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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