Traffic-Associated Link Delay Learning for Industrial Internet of Things

Abstract

Link delay is a key factor to evaluate and ensure the stringent network service quality required by the Industrial Internet of Things (IIoT). Because link delay is seriously affected by traffic, obtaining link delay features associated with network traffic is important. This article presents a traffic-associated link delay learning solution for the IIoT. In our solution, the network of the IIoT is divided into many local networks and a software-defined network (SDN). Our solution uses low-loaded methods to collect traffic-delay samples, and uses a traffic-interval-based mechanism to solve the traffic-associated delay statistics problem. We present a link traffic-delay model learning method for local networks of the IIoT. This method uses path traffic-delay samples, independent from specific network paradigms. Our solution uses a particular deep neural network structure to explore the information implied in path traffic-delay samples. We also propose a link traffic-delay model learning method for the SDN, which selects source links by a feature-similarity-based method and generates link traffic-delay models based on transfer learning. Our solution evaluates the accuracy of link traffic-delay models, and further improves the models with low accuracy.