A Multitask Learning-Based Network Traffic Prediction Approach for SDN-Enabled Industrial Internet of Things

With the rapid advance of industrial Internet of Things (IIoT), to provide flexible access for various infrastructures and applications, software-defined networks (SDNs) have been involved in constructing current IIoT networks. To improve the quality of services of industrial applications, network traffic prediction has become an important research direction, which is beneficial for network management and security. Unfortunately, the traffic flows of the SDN-enabled IIoT network contain a large number of irregular fluctuations, which makes network traffic prediction difficult. In this article, we propose an algorithm based on multitask learning to predict network traffic according to the spatial and temporal features of network traffic. Our proposed approach can effectively obtain network traffic predictors according to the evaluations by implementing it on real networks.

Automated summary

With the rapid advancement of IIoT and the involvement of SDNs, network traffic prediction has become an important research direction. This article proposes an algorithm based on multitask learning to predict network traffic by considering the spatial and temporal features of the traffic. The proposed approach effectively obtains network traffic predictors through evaluations on real networks.