AI-driven network automation for traffic and service resilience - Phase II
Undersea cable incidents frequently disrupt global telecommunications,
with a recent major cut between Asia and Europe significantly
impacting network quality and customer experience. Network engineers
often navigate diverse routing paths, including IX, Peering, and
Transit services, but improper handling can exacerbate issues. Even
after optimal rerouting, persistent service degradation, particularly
at the application layer makes problem identification challenging.
Manual performance analysis across platforms is labor-intensive and
inefficient.
AI in the network operations space offers a transformative solution.
The AI continuously learns traffic behaviors, optimizes resource
allocation, and prioritizes traffic based on Quality of Service (QoS)
metrics to achieve better performance & resilience. GenAI is also
introduced in the grand design, enabling intent-based interactions
with the network.
AI-driven innovations ensure consistent service quality, optimised
operational costs, stronger customer trust, and more advanced
automated network management. This approach addresses evolving
telecommunications challenges, ensuring robust and reliable service
delivery.
Our catalyst explores AI-driven solutions to advance towards higher
level of autonomous networks (AN Level-4/5). In the previous phase, it
demonstrated three aspects: intent input via chatbot and interpreter,
service decomposition and instantiation, service-to-resource
management. The previous phase (Stage 1) focused on developing a
reference architecture and realisation of intent-based
Connectivity-as-a-Service (CaaS) orchestration. Stage 2, will focus on
the full implementation of the design to achieve the endgame of
AI-driven network operations for enhanced efficiency and automation.