Diagnosing performance degradations in 5G networks remains challenging due to the limited visibility of internal RAN behavior in open-source deployments, where radio impairments, such as interference, directly affect performance and are difficult to observe. This demo presents a real-time RAN observability framework for open-source 5 G systems and demonstrates its effectiveness under controlled radio interference. The solution extends Monarch's existing cloud-native monitoring stack with a non-intrusive, Prometheus-compatible telemetry pipeline for the OAI gNB, exposing fine-grained, per-UE metrics from the physical, MAC, and RRC layers and visualizing them live in Grafana. The system is deployed automatically using an Ansible-based framework and validated on a controlled testbed with real radio hardware, where interference-induced effects can be observed and correlated in real time, enabling rapid and practical diagnosis of RAN performance degradation.
Remote operation and infrastructure-supported driving expand vehicular perception and control through distributed communication between vehicles, infrastructure, and control centres. However, this also creates the need for a cybersecurity management framework that can satisfy the requirements of the cloud and edge environment, as well as rapidly emerging adversarial AI attacks. Since communications between participants in both scenarios can be modulated as data streams, a hybrid data stream-based cybersecurity management framework extending the GALOIS architecture that satisfies requirements is proposed. So far, no concept has been proposed to model a cybersecurity management framework based on a distributed data stream processing architecture that takes into account the interoperability of the different devices in particular. Our results show that real-time analysis of adversarial attacks is feasible but needs a redesign. Our introduced online detection operator outperformed the classical offline detection by 584.3% in latency time.
Diagnosing performance issues in 5G and future disaggregated networks remains challenging due to the complexity of the protocol stack and the multitude of interdependent metrics. This is especially true in experimental environments using open-source 5 G software, where logs are often verbose, fragmented, and difficult to interpret. Real-time visual observability, especially at the Radio Access Network (RAN) level, is therefore essential for effective troubleshooting. However, tools like 5GC-Observer and Monarch provide only partial support, focusing mainly on the core network and lacking visibility into the RAN, where many performance bottlenecks originate. To tackle this, we present a Prometheus-compatible telemetry pipeline for the OpenAirInterface (OAI) gNB that extracts RAN metrics, adjusts acquisition intervals via a custom FlexRIC model, and visualizes the data in real time with Grafana. The pipeline is also compatible with SRSRAN through a lightweight Telegraf-Prometheus integration, extending its use to multiple open-source stacks. We integrate our solution into the Monarch monitoring architecture for cloud-native 5G, enabling end-to-end observability. An Ansible-based automation simplifies testbed setup and ensures reproducible experimentation. Validation on a realistic testbed shows real-time RAN metrics exposure with negligible overhead.
Enhancing the user experience for students and researchers on university networks is a top priority of network administrators, as it directly impacts academic performance in classrooms and laboratories. The goal is to ensure that the online experience is so seamless that students and faculty forget they are on a managed network at all. A key determinant of network performance is the efficiency of traffic routing. Optimizing routing mechanisms can significantly improve user experience by alleviating issues, such as congestion, as well as minimizing packet loss. In this paper, we propose the primary steps for exploiting network digital twins as a powerful tool to fine-tune routing schemes by optimizing routes at the flow level. The proposed framework establishes a virtual environment where routing metrics defined by the administrator's Service Level Agreement (SLA), such as flow-level round-trip time, can be tested and analyzed before deployment, enabling network optimization tailored to specific performance requirements. Furthermore, we demonstrate how deploying a digital twin to enhance flow-level routing can address the limitations of traditional approaches, such as Open Shortest Path First (OSPF), by providing greater flexibility and adaptability to dynamic network conditions.
Cloud, Internet, and network infrastructures are now tightly integrated, making experimental research increasingly dependent on advanced technical expertise and real-system deployments. As a result, purely theoretical evaluations are often insufficient to capture real-world behavior. This shift has driven the rise of data-driven research, where hypotheses are validated using high-quality experimental data. However, such data are frequently retained by their producers, limiting reproducibility and reuse. The EU SLICES-RI project addresses this challenge by providing a scientific platform that supports reproducible, large-scale experiments on state-of-the-art hardware and software and enables the systematic sharing of experimental data and metadata across the research community. This demo presents the SLICES-RI Data Management Infrastructure and its Metadata Registry System, demonstrating how lightweight tools can be used to efficiently publish and share experimental results. This Metadata Registry System has already been adopted by other European projects, such as Converge, 6GXCEL, and 6G-Sunrise.
