Network Traffic Prediction in Industrial Internet of Things Backbone Networks: A Multitask Learning Mechanism

Industrial Internet of Things (IIoT), as a common industrial application of Internet of Things, has been widely deployed in recent years. End-to-end network traffic is an essential information for many network security and management functions. This article investigates the issues of IIoT-oriented backbone network traffic prediction. Predicting the traffic of IIoT backbone networks is intractable because of the large number of prior network traffic information, which needs to consume expensive network resources for sampling. Motivated by that, we propose an effective prediction mechanism using multitask learning (MTL), which is a special paradigm of transfer learning. A deep learning architecture constructed by MTL and long short-term memory is designed. This deep architecture takes advantage of link loads as additional information to improve prediction accuracy. We provide a theoretical analysis for the MTL mechanism. The effectiveness is evaluated by implementing our mechanism on real network.

I. Introduction

Ubiquitous 5G-enable Internet of Things (IoT) based on has become a prevalent technique for industrial field to implement intelligent production [1]–[3]. The 5G communication technique provides coinstantaneous broadband access for a mass of users. Currently, a great number of intelligent sensors, radio frequency identification infrastructures, and other networks (e.g., wireless sensor networks) have been connected to 5G-enable Industrial IoT (IIoT), which generates a lot of communication traffic. In this case, the 5G-enable IIoT has been a large-scale complex and heterogeneous network, which provides reliable communication for various wireless and wired terminals. Network security and management functions can provide reliable IIoTs for production automation. End-to-end network traffic is a fundamental input for many network management and security functions. For instance, network operators need to know network traffic to carry out a reasonable routing algorithm. Besides, some anomaly and intrusion detection algorithms are realized according to network traffic information, such as identifying mechanism for distributed denial of service attacks [4]–[10]. The IIoT backbone network aggregated the network traffic from edge nodes is a large-scale network, and predictive routing algorithms are necessary for the backbone network to carry out effective resource allocation. Thus, excellent network traffic prediction techniques can improve the effectiveness of IIoT backbone networks [11].