Building operator-centric digital twins for optical transport networks: from data models to real applications

Abstract

The complexity and heterogeneity of modern optical transport networks (OTNs) demand advanced solutions to enhance their operation and maintenance. This paper presents lessons learned from the design and implementation of a digital twin network (DTN) tailored to network operators’ requirements, integrating hybrid models that combine physical representations with data-driven analysis. By leveraging telemetry data, the DTN can facilitate network operators to perform predictive maintenance, resource optimization, and risk analysis. Through a case study on soft failure detection across 454 optical sections, we analyze the value of transforming theoretical concepts coupled with collected data into actionable insights, reducing operational overhead and enhancing network resilience. This work underscores the potential of hybrid modeling in the context of DTN to create operator-centric solutions for optical transport networks.