A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning

Julong LAN; Changhe YU; Yuxiang HU; Ziyong LI

doi:10.11999/JEIT180870

Volume 41 Issue 11

Nov. 2019

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Julong LAN, Changhe YU, Yuxiang HU, Ziyong LI. A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2669-2674. doi: 10.11999/JEIT180870

Citation:

Julong LAN, Changhe YU, Yuxiang HU, Ziyong LI. A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2669-2674. doi: 10.11999/JEIT180870

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A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning

doi: 10.11999/JEIT180870

National Digital Switching System Engineering & Technological Research Center, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China for Innovative Research Groups (61521003), The National Natural Science Foundation of China (61502530)

Received Date: 2018-09-06
Rev Recd Date: 2019-05-12

Available Online: 2019-05-27

Publish Date: 2019-11-01

Abstract

Abstract

In order to achieve routing optimization in the Software Defined Network (SDN) environment, deep reinforcement learning is imposed to the SDN routing process and a mechanism based on deep reinforcement learning is proposed to optimize routing. This mechanism can improve network performance such as delay, throughput, and realize black-box optimization in continuous time, which surely reduces network operation and maintenance costs. Besides, the proposed routing optimization mechanism is evaluated through a series of experiments. The experimental results show that the proposed SDN routing optimization mechanism has good convergence and effectiveness, and can provide better routing configurations and performance stability than traditional routing protocols.
- Software Defined Network (SDN),
- Routing optimization,
- Deep reinforcement learning

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References(17)

References

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