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The Optical Network Emulation (ONE) engine is a software tool that offers students the opportunity to learn how to control and operate open optical (wavelength division multiplexing) transport networks, such as those based on the Open ROADM MSA standards. This paper describes multiple modelling techniques that are implemented in the ONE engine to represent the signal power spectral density at any link/fiber section of the emulated transport network. These techniques make use of polynomial fitting and deconvolution computation methods. 

Advanced applications emerging from distributed cloud and edge computing and Fifth-generation and Beyond (B5G) networks increasingly require highcapacity, low-latency, and highly reliable optical transport networks. Accurate fault localization and isolation are therefore of the essence in optical transport networks, including timely identification of both correlated alarms and the identification of the root alarm, triggering further fault propagation. In this paper, a Graph Neural Network with Multi-Head Attention (GNN-MHA) architecture is applied to jointly address alarm correlation and rootalarm detection within a unified framework. To support realistic training and evaluation, we develop a configurable Reconfigurable Optical Add-Drop Multiplexerbased (ROADM) simulator that generates timestampaware alarm propagation graphs under diverse fault scenarios. This simulator enables a comprehensive assessment of both alarm correlation and root cause detection capabilities. Simulation based on synthetic datasets shows that the proposed GNN-MHA model achieves an F1 score of 0.9202 for alarm correlation and an accuracy of 97.34% for root alarm detection, outperforming all baseline methods in overall performance. These results corroborate the effectiveness of the proposed model for topology-aware and time-sensitive fault analysis in optical transport networks. 

Advanced applications emerging from distributed cloud and edge computing and Fifth-generation and Beyond (B5G) networks increasingly require highcapacity, low-latency, and highly reliable optical transport networks. Accurate fault localization and isolation are therefore of the essence in optical transport networks, including timely identification of both correlated alarms and the identification of the root alarm triggering further fault propagation. In this paper, a Graph Neural Network with Multi-Head Attention (GNN-MHA) architecture is applied to jointly address alarm correlation and root alarm detection within a unified framework. To support realistic training and evaluation, we develop a configurable Reconfigurable Optical Add-Drop Multiplexerbased (ROADM) simulator that generates timestampaware alarm propagation graphs under diverse fault scenarios. This simulator enables a comprehensive assessment of both alarm correlation and root cause detection capabilities. Simulation based on synthetic datasets shows that the proposed GNN-MHA model achieves an F1 score of 0.9202 for alarm correlation and an accuracy of 97.34% for root alarm detection, outperforming all baseline methods in overall performance. These results corroborate the effectiveness of the proposed model for topology-aware and time-sensitive fault analysis in optical transport networks. 

This demo presents a cyber-resilient fifthgeneration and beyond (B5G) framework that activates dynamic live-migration of the Centralized Unit–User Plane (CU-UP) container in response to a cyber threat. Deployed within the near-real-time RAN Intelligent Controller (Near-RT RIC) an Adaptive Cyber Threat (ACT) xApp detects cyber threats adversely affecting the CU-UP container and triggers its migration through an Optical Transport Network (OTN). Implemented using OpenAirInterface (OAI) and FlexRIC, the demo can respond to cyber threats in around 10 seconds. 

The Optical Network Emulation (ONE) engine is a real-time, multi-container software platform designed to model and emulate open optical transport networks with realistic fidelity. This paper introduces an enhanced version of the ONE engine that integrates a distributed implementation of a Gaussian Noise (GN) model for estimating nonlinear interference (NLI) in wavelength division multiplexing (WDM) systems. The inclusion of the GN model enables more realistic emulation of nonlinear signal degradation across diverse link configurations and operating conditions. The enhanced ONE package is then used to document the model’s impact on system performance under varying transmission conditions, including signal launched power and increased spectral loading. With this upgrade, the ONE engine expands its utility for research, development, and education, providing a scalable and flexible environment for testing physical-layer impairments and control strategies in software-defined optical networks. 

This paper presents a proactive live migration of the gNodeB Central Unit User Plane (gNB-CU-UP) container in a 5G virtualized Radio Access Network (vRAN) testbed across a multi-vendor OpenROADM optical transport network (OTN). An automated mechanism monitors the CPU usage and dynamically reconfigures midhaul and backhaul wavelength services to maintain low-latency transport. Leveraging Pacemaker and Corosync for seamless Virtual IP (VIP) handover, the proposed mechanism achieves a UE Service Recovery Time of 1.04 sec ensuring minimal disruption. 

Network automation is essential for accelerating service deployment and optimizing operations, especially in multivendor optical transport networks. However, unique interoperability challenges arising from vendor-specific implementations require standardized frameworks such as those provided by OpenROADM MSA. This work introduces the Optical Network Robotic Automation Platform (ON-RAP), a fully automated, vendor-agnostic solution that integrates system-level automation with robotic process automation, streamlining OpenROADMcompliant network operations and enabling comprehensive test automation. ON-RAP automates compliance validation, service provisioning, troubleshooting, and multi-vendor simulation, achieving precise configuration, real-time monitoring, and timeefficient fault resolution. ON-RAP enables seamless validation across simulated and real environments, enhancing test efficiency and scalability for network devices and controllers. In addition, it automates report generation to facilitate structured analysis and reduce manual effort. 

Network operators rely on the fault, configuration, accounting, performance, and security (FCAPS) model for efficient network management using traditional monitoring solutions that are often costly and proprietary. This paper introduces OpenNOP, an open-source, multi-layer, and multi-vendor network observability platform designed for fault detection, configuration tracking, and performance monitoring. OpenNOP collects and processes network metrics in a time-series database, enabling real-time visualization and AI-driven predictive analytics. Deployed in a multi-vendor optical transport testbed, it facilitates ML-based inference of network disturbances. OpenNOP uses scripted automation to control the generation of network disturbances and the collection of L1/L2/L3 metrics and then train and test ML models to infer the noise profile based on those metrics. By providing a scalable and extensible alternative to proprietary tools, OpenNOP advances network monitoring, predictive maintenance, and AI explainability. 

Fifth-generation and beyond (B5G) networks must handle stringent requirements for ultra-low latency, high reliability, and dynamic service provisioning across decentralized environments. While container-based live migration has emerged as a flexible mechanism to ensure service continuity during failures and overload scenarios, most proposed approaches are reactive and lack integration with the transport layer and automation through real-time resource orchestration. This work presents a proactive migration framework that tightly couples a 5G radio access network (RAN) architecture with an OpenROADM-compliant optical transport network (OTN) testbed. By leveraging dynamic optical wavelength service creation and container-based network function virtualization, the presented framework enables seamless live migration of the gNB (next generation) central unit–user plane (CU-UP) between two remote locations without disconnecting the supported mobile services. A custom xApp within the near-real-time RAN intelligent controller (Near-RT RIC) monitors system performance metrics and employs predictive analytics to trigger the proactive CU-UP container migration ahead of a probable server overload scenario. A robot framework-based automation platform ensures coordinated orchestration between the compute and transport layer resource allocation to achieve a successful live migration of the container running the CU-UP. Experimental results confirm that the proposed approach achieves near-zero mobile user service downtime, demonstrating its effectiveness in meeting the end-to-end quality of service (QoS) requirements of B5G applications.