End-to-end transport network digital twins with cloud-native SDN controllers and generative AI [Invited]

Abstract

This paper explores the potential of network digital twins (NDTs) in networking (both IP Ethernet networks and optical transport networks), highlighting their integration with cloud-native software-defined networking (SDN) controllers and intent-based networking enabled by generative artificial intelligence (GenAI). The proposed framework represents an approach that combines advanced virtualization, real-time analytics, and GenAI. The use of NDTs enables a comprehensive and dynamic digital representation of the physical network, capturing critical aspects, such as topology, traffic patterns, and performance metrics, which permits data-driven decision-making to lead to more efficient networking operations. The incorporation of cloud-native SDN controllers along with an NDT ensures that the system remains scalable, flexible, and responsive to dynamic network conditions. Intent-based networking, powered by GenAI, allows the network to interpret high-level objectives from operators and autonomously translate them into actionable configurations that are enforced by orchestrators and SDN controllers. This eliminates manual intervention, minimizes errors, accelerates the deployment of network services, and provides a means for easier network management. The presented framework significantly enhances automation, enabling predictive maintenance by identifying potential issues before they impact network performance. It optimizes network design by simulating various configurations and testing their feasibility in a risk-free environment. These capabilities collectively improve operational efficiency, reduce downtime, and ensure optimal resource utilization.