While network technologies and architectures evolve rapidly, the complexity of network operation management has grown significantly over the past 20 years. The network operation industry faces numerous challenges, including:
- Lack of collaboration and coordination across teams
- Rapidly changing cloud environments and dynamic resource orchestration, where knowledge and information often lag behind, making troubleshooting problems more difficult
- Time-consuming troubleshooting processes that require correlating data across multiple devices and toolsets, heavily relying on manual processes to achieve accurate diagnoses
- Increasing cyber-attack threats that place additional pressure on network operations
Following research into AI models for cyber-attack detection, the TeleMARS team shifted its focus to supporting the practical implementation of data-driven technologies in network operation management. Building on previous project experience and discussions with experts from RMIT University, the team recognized the critical importance of knowledge sharing and skill development in key areas such as diagnosis, monitoring, measurement, and the analysis of live or historical network datasets. This approach specifically addressed emerging cyber-attack challenges and guided efforts to develop effective AI solutions while equipping network professionals with essential knowledge.
Motivated by these challenges, the TeleMARS team sought to create innovative training approaches and facilities designed to enable knowledge sharing and skill improvement in network data analysis. These efforts aimed to support AIOps transformation, fostering advancements that benefit Internet development.
Key project initiatives included:
- Developing a Collaboration Framework: The team designed a novel collaboration approach between research institutions and industry professionals. This framework facilitated the transfer of research outcomes, industry expertise, and practical knowledge. It was specifically tailored for the network operation community and was also applicable for enhancing skills in Internet performance and security.
- Creating a Network Analysis Platform: This platform provided participants with hands-on opportunities to learn and practice AI analytics in a controlled environment. The diverse project team, which included subject matter experts, data engineers, and scientists, collaborated on implementing practical examples. This hands-on experience significantly enhanced learning outcomes.
Through these efforts, TeleMARS demonstrated the value of collaboration, hands-on learning, and innovative approaches in advancing AI analytics capabilities in the network operation industry. By integrating industry requirements with research insights, the project successfully addressed skill gaps and prepared professionals for the evolving challenges of network operations.
