Artificial intelligence (AI) enables the simulation of human intelligence in computing machines that can be programmed to behave like humans and imitates their actions. The term can be associated with any machine that shows human traits such as learning new patterns and problem-solving. The main characteristic of artificial intelligence is its power to think rationally and take appropriate action to achieve a specific goal. Machine learning, which is a subset of artificial intelligence, refers to a computing paradigm that can automatically learn from a set of data with minimal human intervention. After that, it can take any input and classify or predict the outcomes.

Network technologies such as Software Defined Networking (SDN), Network Function Virtualization (NFV), and 5G / 6G, are continuously evolving to support the exponential growth of connected devices and unique performance expectations such as reliability, dependability, and scalability. The downside of those technologies is that they are changing faster than we can manage them. To address this problem, cognitive network (CN) is increasingly used, which refers to a network as a cognitive process that can take input as real-time network conditions, process them using artificial intelligence and act on those network conditions.

Therefore, research is required to understand and improve the potential and suitability of artificial intelligence in the context of network management. This will provide a deeper understanding and better decision-making based on largely collected and available network data. It will also present opportunities for improving artificial intelligence algorithms on aspects such as reliability, dependability, and scalability and demonstrate the benefits of these methods in management and control systems. This workshop aims at gathering the recent advances in AI for Intelligent network management. We hope this workshop will inspire new thoughts and contributions to this specific topic. Topics include but are not limited to the following:

• Deep and Reinforcement learning for networking and communications in 5G networks

• Data mining and big data analytics in 5G networking

• Protocol design and optimization using AI/ML in 5G

• Self-learning and adaptive networking protocols and algorithms for 5G

• Intent & Policy-based management for intelligent networks

• Innovative architectures and infrastructures for intelligent networks

• AI/ML for network management and orchestration in 5G systems

• AI/ML for network slicing optimization in 5G systems

• AI/ML for service placement and dynamic Service Function Chaining in 5G systems

• AI/ML for C-RAN resource management and medium access control

• Decision making mechanisms

• Routing optimization based on flow prediction in 5G systems

• Data-driven management of software defined networks for 5G networks

• Methodologies for network problem diagnosis, anomaly detection and prediction

• Reliability, robustness and safety based on AI/ML techniques

• Network Security based on AI/ML techniques in 5G

• AI/ML for IoT

• Open-source networking optimization tools for AI/ML applications

• Experiences and best-practices using machine learning in operational networks

• Novel context-aware, emotion-aware networking services

• Machine learning for user behavior prediction

• Modeling and performance evaluation for Intelligent Network

• Intelligent energy-aware/green communications

Award

• There will be a best paper award for this workshop. The workshop chairs will thoroughly evaluate all the submissions to this workshop to identify a paper for the best paper award.

• Selected papers from this workshop will be invited to submit to special issues of reputed journals.

Important Dates

• Submission deadline: June 1, 2021 June 15, 2021 June 30, 2021 (FIRM)

• Authors notification: July 15, 2021 July 20, 2021

• Camera-ready due: August 1, 2021

• Registration: August 1, 2021

Contact

Please contact committee chairs for your queries.