| 一种基于深度强化学习的SDN路由算法 |
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| 引用本文： | 丁怀宝.一种基于深度强化学习的SDN路由算法[J].上海师范大学学报(自然科学版),2021,50(1):128-132. |
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| 作者姓名： | 丁怀宝 |
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| 作者单位： | 安徽文达信息工程学院 计算机工程学院, 安徽 合肥 231201 |
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| 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） |
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| 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...
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| 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) |
| 收稿时间： | 2020/11/13 0:00:00 |
An SDN routing algorithm based on deep reinforcement learning |
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| DING Huaibao.An SDN routing algorithm based on deep reinforcement learning[J].Journal of Shanghai Normal University(Natural Sciences),2021,50(1):128-132. |
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| Authors: | DING Huaibao |
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| Institution: | School of Computer Engineering, Anhui Wenda University of Information Engineering, Hefei 231201, Anhui, China |
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| 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. |
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| Keywords: | software defined network(SDN) traffic engineering(TE) reward function deep reinforcement learning routing(DRL-routing) |
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