Introduction to the Special Section on Learning-Based Modeling, Management, and Control for Computer and Communication Networks

The papers in this special section examine the market for learning-based modeling, management, and control for computer and communication networks. Computer and communication networks are becoming larger and more complicated, generating a huge amount of runtime statistics data (such as traffic load, resource usages, etc.) every second. Meanwhile, emerging machine learning models and techniques, such as active learning, Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL), have been shown to dramatically improve the state-of-the-art of many applications, including video/image processing, natural language processing, game playing, etc. This special issue aims to exploit how these emerging and powerful techniques can be leveraged to grasp the exciting opportunities provided by pervasive availability of voluminous data to model, manage and control computer and communication networks. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section range from modeling, analysis, demonstration of various networks with emerging machine learning techniques.