基于LSTM与传统神经网络的网络流量预测及应用

Network Traffic Prediction and Application Based on LSTM and Traditional Neural Networks

提出了一个基于长短期记忆循环神经网络与传统神经网络的网络流量预测模型。通过与传统机器学习流量预测方法相对比,验证了该模型在网络流量预测中的适用性与更高的准确性。将上述流量预测模型应用在基于软件定义网络技术的智能化承载网切片系统中,以提升网络资源利用率,并提供了实验室验证结果。

This paper proposes a traffi c prediction model based on long short term memory(LSTM)and traditional neural networks.Compared with traditional machine learning-based traffi c prediction methods,experiments show that the proposed model has the applicability and higher accuracy in the network traffi c prediction.Finally,the above traffi c prediction model is applied in the SDN-based intelligent transport network slicing systems to improve the utilization of network resources,and the laboratory verifi cation results are also provided.