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.

中文翻译：

---

工业物联网骨干网络中的网络流量预测：一种多任务学习机制


工业物联网（IIoT）作为物联网常见的工业应用，近年来得到了广泛的部署。端到端网络流量是许多网络安全和管理功能的重要信息。本文研究了面向工业物联网的骨干网络流量预测问题。由于事先有大量的网络流量信息，需要消耗昂贵的网络资源进行采样，因此预测工业物联网骨干网络的流量非常困难。受此启发，我们提出了一种使用多任务学习（MTL）的有效预测机制，这是迁移学习的一种特殊范式。设计了由MTL和长短期记忆构建的深度学习架构。这种深层架构利用链接负载作为附加信息来提高预测准确性。我们对 MTL 机制进行了理论分析。通过在真实网络上实施我们的机制来评估有效性。