基于深度增强学习的软件定义网络路由优化机制

为优化软件定义网络(SDN)的路由选路，该文将深度增强学习原理引入到软件定义网络的选路过程，提出一种基于深度增强学习的路由优化选路机制，用以削减网络运行时延、提高吞吐量等网络性能，实现连续时间上的黑盒优化，减少网络运维成本。此外，该文通过实验对所提出的路由优化机制进行评估，实验结果表明，路由优化机制具有良好的收敛性与有效性，较传统路由协议可提供更优的路由方案与实现更稳定的性能。

A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning

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.