Time-Varying-Aware Network Traffic Prediction Via Deep Learning in IIoT

With the rise of the Industrial Internet of Things (IIoT), more and more industrial devices can be connected via the network. Data collection, processing, analysis, task execution, and other devices that can product network traffic volume are gradually being deployed to IIoT. However, under the limited spectrum resources and low-cost and low-energy production requirements of enterprises, how to ensure the interconnection and intercommunication of industrial networks while realizing the effective use of network communication resources is currently a hot topic. Among them, network traffic prediction is considered to be a very important task. The time variability and interpretability, especially the time-varying features of traffic sequences, greatly challenge this task. To address those, this article proposes a method called Flow2graph to predict network traffic in IIoT. Specifically, some key segments, i.e., shapelets are extracted from the network traffic sequence according to time-varying traffic; then uses the relationship between the traffic sequence and shapelets to convert the flow into a shapelets conversion graph; Subsequently, the graph isomorphism network are used to learn the specificity of the flow sequence from different devices, thereby to predict its traffic value for a period of time in the future; finally, we conduct extensive experiments on real data to verify the effectiveness of the proposed method.