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Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of data science and connected devices of all varieties, Intelligence have found ways to improve operations and management of next generation networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, system, and service operations and management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.

IEEE Transactions on Network and Service Management (IEEE TNSM) is a premier journal for timely publication of archival research on the management of networks, systems, and services. Following the success of five recent TNSM special issues on Data Analytics for Network and Service Management in 2016, 2018, 2019, 2020, 2021, and 2022, this special issue will also focus on recent, emerging approaches and technical models that exploits intelligence in network and service management solutions. We welcome submissions addressing the underlying challenges and opportunities, presenting novel techniques, experimental results, or theoretical approaches motivated by network/service management problems. Survey papers that offer a perspective on related work and identify key opportunities and challenges for future research are also in the scope of the special issue. We look forward to your submissions!

Topics of Interest

Topics of interest for this special issue include, but are not limited, to the following:

ML/AI Techniques for Network and Service Management

  • Analysis, modelling and visualization
  • Operational analytics and intelligence
  • Event and log analytics, text mining
  • Outlier / Anomaly detection and prediction
  • Predictive analytics and real-time analytics
  • ML/AI, neural networks, and deep learning for management
  • Data mining, statistical modeling, and machine learning for management

Application Domains and Management Paradigms

  • Social and communication networks analysis
  • ML/AI management of virtualized infrastructure, clouds, and edge nodes
  • ML/AI management of software defined networks
  • ML/AI management of storage resources
  • ML/AI management of Internet of Things and cyber-physical systems
  • ML/AI management of 5G, 6G and beyond
  • ML/AI management of zero touch and driverless networks
  • Applications of AI to traffic classification, root-cause analysis, service quality assurance, IT service and resource management
  • Novel approaches to cyber-security, intrusion detection, threat analysis, and failure detection based on ML/AI
  • Platforms for monitoring and measurements to support management for ML/AI
  • Platforms for analyzing and storing logs and operational data for management tasks
  • Platforms for collaborative learning from multiple distributed network elements
  • Platforms for anonymizing operation data
  • AI/ML testbeds and experimental evaluations
  • Abstractions and knowledge representation/data models needed for deploying ML/AI for network management and orchestration

Submission Guidelines

All papers should be submitted through the IEEE Transactions on Network and Service Management manuscript submission site. Authors must indicate in the submission cover letter that their manuscript is intended for the "Machine Learning and Artificial Intelligence for Managing Networks, Systems and Services” special issue. View detailed author guidelines.

Important Dates

Paper Submission: 20 June 2023 (Extended Deadline)
Publication Date: March 2024 (Tentative)

Guest Editors

Nur Zincir-Heywood (Lead)
Dalhousie University, Canada

Robert Birke
University of Turin, Italy

Elias Bou-Harb
The University of Texas at San Antonio, USA

Hossam Hassanein
Queen's University, Canada

Takeru Inoue
NTT Laboratories, Japan

Neeraj Kumar
CSED Thapar Institute of Engineering and Technology, India

Alberto Leon-Garcia
University of Toronto, Canada

Hanan Lutfiyya
The University of Western Ontario, Canada

Deepak Puthal
Newcastle University, UK

Abdallah Shami
The University of Western Ontario, Canada

Natalia Stakhanova
University of Saskatchewan, Canada

For more information, please contact Professor Nur Zincir-Heywood.