基于切比雪夫神经网络的软件定义卫星网络智能路由策略

针对现有的软件定义卫星网络中流表占用的三态内容寻址存储器空间不断增加,复杂的流表项查找、匹配过程导致路由转发效率降低,无法满足多样化应用需求的问题,提出基于神经网络的软件定义卫星网络智能路由架构。控制器通过训练神经网络获取数据流的传输模式,并用训练后的神经网络代替流表,在此基础上提出基于Chebyshev神经网络的智能路由策略,交换机根据数据流的业务类型预测其转发路径,以满足卫星网络应用的服务质量要求。仿真结果表明:所提路由策略显著减少了占用的三态内容寻址存储器存储空间,提高了路由效率。

Intelligent routing strategy for software-defined satellite network based on Chebyshev neural network

In the existing software-defined satellite network, the storage space of ternary content addressable memory occupied by the flow table is increasing, and the complex flow entry lookup and matching processes will bring about reduced route forwarding efficiency, which cannot meet the requirements of diverse application requirements. A neural network-based software-defined satellite network intelligent routing framework was proposed. The controller acquired the transmission mode of the data flow by training the neural network, and replaced the flow table with the trained neural network. Based on this framework, an intelligent routing strategy based on Chebyshev neural network was proposed. The switch predicts the forwarding path of data flow according to the service type of data flow to meet the quality of service requirements of satellite network applications. The simulation results show that the proposed routing strategy significantly reduces the occupied storage space of ternary content addressable memory and improves the routing efficiency.