IEEE Network covers topics which include: network protocols and architecture; protocol design and validation; communications software; network control, signaling and management; network implementation (LAN, MAN, WAN); and micro-to-host communications.

Special Issue on Interplay Between Machine Learning and Networking Systems
Submission Date: 2021-04-15

Recently, the advancement of machine learning (ML) techniques, especially deep learning, reinforcement learning, and federated learning, has led to remarkable breakthroughs in a variety of application domains. The success of ML benefits from the advancement of the Internet, mobile networks, data center networks, and IoT that facilitate data creation and sharing. On the other hand, we have also witnessed a fast growing trend in the networking community toward using ML to tackle challenging problems in network design, management, and optimization, which are traditionally addressed using mathematical optimization theory or human-generated heuristics. ML is also an essential ingredient in the realization of autonomous or self-driving networks.

Despite the wide successes of ML-related research in networking systems, there remain many challenges, such as the lack of open datasets, open-source toolkits and benchmark suites, reproducibility of the experiments, interpretability and robustness of the ML models, communication bottlenecks in distributed ML systems, etc. The objective of this Special Issue is to bring together the state-of-the-art research results of ML technology and its applications in networking systems. We welcome submissions from both academia and industry that address the fundamental challenges and opportunities in the interplay between ML and networking systems. The topics of interest of this special issue include, but are not limited to:

 - Open datasets of networking systems for ML research
 - Open-source ML software for networking systems
 - Benchmark suites for ML research in networking systems
 - ML for traffic prediction and classification
 - ML for routing
 - ML for congestion control
 - ML for data center networks
 - ML for network management
 - ML for network security, including anomaly detection, intrusion detection, etc.
 - ML for software-defined networks
 - ML for autonomous and self-driving networks
 - Big data analytics frameworks for networking data
 - Network theory inspired by ML
 - Interpretability and robustness of ML for networking systems
 - Reinforcement learning for networking systems
 - Federated learning for networking systems
 - Networking performance optimization for ML applications and systems
 - Reproducibility of ML research in networking systems