A complex network traffic prediction method for IP network expansion applications

IP network expansion is a common maintenance method for communication operators to keep the network running smoothly.The core is to predict the network traffic trend in the future period.IP network traffic is very complex, with local uncertainty, burstiness, heterogeneity, etc., which brings difficulties to prediction.A prediction method for complex network traffic was proposed.It adopted an encode-decode structure, adding global features to the encoding layer, and combining global features and local features in the decoding layer to solve local uncertainties.The model used sample balance, normalization and other methods to extract the commonality of the data as much as possible to avoid the heterogeneity of the data.And emergencies were alleviated by increasing prior knowledge.The overall model had fewer parameters and had strong generalization performance, at the same time, the combination of artificial features and automatic features ensured the accuracy of the shallow network.The experimental results show that the proposed method has the characteristics of high accuracy and strong generalization performance.At present, this method has been applied on a large scale in engineering.