CFP: IEEE Network Magazine Special Issue on Interplay Between Machine Learning and Networking Systems

[Xiaoming Fu]

 [Apologies for cross-posting]

IEEE Network Magazine

Special Issue on Interplay between Machine Learning and Networking Systems

Please kindly note the submission deadline April 15, 2021 (firm) is approaching!

https://www.comsoc.org/publications/magazines/ieee-network/cfp/interplay-between-machine-learning-and-networking-systems

Call for Papers

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

Important Dates
Manuscript Submission Deadline: 15 April 2021
Initial Decision Notification: 1 June 2021
Revised Manuscript Due: 1 July 2021
Final Decision Notification: 1 August 2021
Final Manuscript Due: 1 September 2021
Publication Date: November 2021

Submission Guidelines
Manuscripts should conform to the standard format as indicated in the
"Information for Authors" section of the Paper Submission Guidelines.

All manuscripts to be considered for publication must be submitted by the
deadline through Manuscript Central. Select "November 2021/ Interplay
between Machine Learning and Networking Systems" from the drop-down menu of
Topic titles.

Guest Editors

Xiaowen Chu, Hong Kong Baptist University, Hong Kong, China
Xiaoming Fu, University of Goettingen, Germany
Baochun Li, Toronto University, Canada
Wei Wang, The Hong Kong University of Science and Technology, Hong Kong, China
Hui Zang, Google, USA
Albert Zomaya, The University of Sydney, Australia