Chair: Prof. Wang-Cheol Song (Jeju National University/KOREN Forum) [ philo AT jejunu.ac.kr ]
Co-Chair: Dr. Hidehisa Nagano (NICT) [ hidehisa.nagano AT nict.go.jp ]
Co-Chair: Prof. Xiaohong HUANG from BUPT (China), [huangxh AT bupt.edu.cn]
Mailing List: ainwg AT apan.net
The APAN AI driven Networks Working Group (AINWG) has started at the APAN 50 meeting held in 2020 by having BoF (Birds of Feather) sessions. The aim of AINWG is to share experience to design, manage, maintain, and protect the network using artificial intelligence (AI) and escalate collaboration for AI driven networks in APAN community.
Combining human intellect and creativity with the massive computing power of AI will create situations in which new design and management techniques may be created that humans could not build on their own, but self-improving intelligent algorithms will harness over time.
Beyond simply designing the network, AI will help manage, maintain and protect it. As the AI powering traditional algorithms becomes more intelligent, it will find faster and more foolproof methods of anticipating threats and cleaning the network. AI will be able to better predict traffic as it collects and analyzes data in real-time, so that network managers are better prepared for big events.
Next, with the combination of AI-designed underlying network topologies and AI-driven software-defined networking, we will eventually see more profound evolutions of what networks are capable of, across all industries.
With all these advantages of AI-driven networks, AINWG intends to provide all APAN members with the opportunity to efficiently design, manage, maintain, and protect their respective national and research networks for the mutual benefits of all participants.
Goal and Objectives
The ultimate goal of AINWG is to encourage the collaboration of technical experiences and knowledge regarding AI-enabled networks. This will lead to the development of an intelligent, scalable, sustainable, and easy-to-deploy technical platform for each APAN member country to manage its respective national research and education networks.
In addition, automated processing of incidents in a data-driven domain agnostic manner without the need for expert rules would help significantly enhance automation in Network Operations Centers (NOCs). AI techniques enable us to discover co-occurring patterns in such a stream of alarms, and other events, which helps to quickly identify the root cause in most fault scenarios, eventually transforming a traditional NOC into an iNOC (Intelligent Network Operations Center). To this end, the AI-driven Networks Working Group (AINWG) in APAN will target:
- Sharing experience for developing Intelligence in NREN
- Intelligent automation in APAN networks
- Intelligent operation automation in NOCs
- Collaboration to integrate intelligent networks in APAN community
2021.02.- The 2nd BoF was held in APAN 51 as two sessions.