| 摘要: |
| 为解决软件定义网络（SDN）中的流量工程（TE）问题，提出了一种深度强化学习路由（DRL-Routing）算法.该算法使用较全面的网络信息来表示状态，并使用一对多的网络配置来进行路由选择，奖励函数可以调整往返路径的网络吞吐量.仿真结果表明，DRL-Routing可以获得更高的奖励，并且经过适当的训练后，能使各交换机之间获得更优的路由策略，从而增大了网络吞吐量，降低了网络延迟和数据丢包率. |
| 关键词: 软件定义网络(SDN) 流量工程(TE) 奖励函数 深度强化学习路由(DRL-Routing) |
| DOI：10.3969/J.ISSN.1000-5137.2021.01.018 |
| 分类号:TP312 |
| 基金项目:安徽文达信息工程学院校级科研项目（XZR2020A08；XZR2020A09）；安徽省自然科学基金（KJ2020A0814） |
|
| An SDN routing algorithm based on deep reinforcement learning |
|
DING Huaibao
|
|
School of Computer Engineering, Anhui Wenda University of Information Engineering, Hefei 231201, Anhui, China
|
| Abstract: |
| In this paper, a deep reinforcement learning routing(DRL-Routing) algorithm was proposed to solve the traffic engineering(TE)problem in software defined networking(SDN). The algorithm proposed made use of more comprehensive network information to represent the state, and adopted one-to-many network configuration for routing selection. Besides, the reward function was able to adjust the network traffic of the round-trip path. The simulation results showed that DRL-Routing could obtain higher rewards. After proper training, the agent could learn a more excellent routing strategy between the switches, which increased network traffic and reduced network delay and data packet loss rate. |
| Key words: software defined network(SDN) traffic engineering(TE) reward function deep reinforcement learning routing(DRL-routing) |
Editorial Board