基于LSTM的TTE网络速率约束流量预测

时间触发以太网（TTE）中的速率约束（RC）流量为事件触发流量，在RC流量动态调度的应用场景下，若能预测未来短时间内数条RC流量到达交换节点的序列，使交换节点提前进行调度决策，以减小RC流量时延，提高网络吞吐量。对RC流量到达序列预测问题进行了研究，建立了RC流量的到达序列模型，提出了基于长短期记忆网络（LSTM）算法的RC流量预测算法。利用OMNET++工具进行TTE网络仿真，得到多组混合关键性配置下RC流量的传输数据；以此作为输入样本对预测算法进行训练和测试。实验结果显示，LSTM算法在RC流量预测问题的准确率达到了70%以上。通过对比实验说明所提算法适用于RC流量预测场景。 

Rate-constrained traffic prediction of TTE network based on LSTM

The rate constraint (RC) traffic in time triggered Ethernet (TTE) is event-triggered traffic. In the application scenario of dynamic scheduling of RC traffic, if it can predict the sequence of several RC traffic arriving at the switching node in a short time in the future, the switching node can make scheduling decision in advance to reduce RC traffic delay and improve network throughput. In this paper, the arrival sequence model of RC traffic is established, and an algorithm of RC traffic prediction based on long-term memory network (LSTM) is proposed. Using OMNET++ tool to simulate TTE network, we can get the data of RC traffic transmission under multiple groups of mixed critical configuration, and train and test the prediction algorithm as an input sample. The experimental results show that the accuracy of LSTM algorithm in RC traffic prediction is more than 70%. The experimental results show that the proposed algorithm is suitable for RC traffic prediction scenarios.