6G networks are expected to deliver ultra-low latency, high reliability, and real-time intelligence for emerging services such as interactive Augmented Reality (AR), autonomous robotics, and digital twins. Achieving these requirements in practice demands tight coordination between networking, computing, and control domains, spanning RAN, transport, edge, and cloud. However, current 5G deployments lack pervasive telemetry, fine-grained observability, and automated control mechanisms capable of reacting at the time scales required by latency-sensitive applications. This paper presents a full integrated demonstration of DESIRE6G, a cloud-native 6G-ready architecture that leverages programmable data planes with P4 for flexible routing and telemetry using an implementation of novel data plane protocols, achieves distributed optimization of service deployment and runtime monitoring and reconfiguration via secure multi-agent systems (MAS), combined with intent-based orchestration layer for end-to-end service assurance. The system is validated on the federated ARNO testbed using a real distributed AR application involving a remotely-controlled drone as a User-Equipment that is equipped with a camera streaming a live video through the DESIRE6G network to a Kubernetes edge cluster that executes serverless inference functions for object detection and recognition, the video is then augmented with object information and shown on a Quest 3 AR headset. The MAS monitors the end-to-end latency in real time through P4 Telemetry and responds to changes in network conditions by reconfiguring the affected segments, while the Kubernetes monitoring provides real-time visibility and scalability across different segments. Overall, three hierarchical service assurance loops are demonstrated: (i) In-Network Control (INC) executing microsecond-scale congestion recovery in the P4 data plane, (ii) Infrastructure Management Layer (IML) performing millisecond-scale function migration and scaling, and (iii) MAS-driven cross-domain optimization operating at sub-second time scales to resolve RAN latency anomalies. Evaluation results show stable end-to-end latency in the 15–25 ms range in steady-state conditions, with fast recovery during induced congestion ≤ 1 . 5 ms data plane reroute via P4 INC.

This demonstration showcases the autonomous creation of optical lightpaths across two geographically optical network testbeds, using TeraFlowSDN (TFS) as a high-level intent-based orchestrator for optical service provisioning. Unlike existing approaches that create lightpaths independently within a single domain, our system highlights a hierarchical control model in which a centralized TFS instance coordinates two heterogeneous domain controllers: a vendor-specific controller at Politecnico di Milano (Italy) and a local TFS instance at Sant’Anna School of Advanced Studies (Italy). Southbound adapters enable the translation of high-level service intents into device-specific configurations, making it possible to integrate different controllers and vendors without modifying the underlying infrastructure. The live demo demonstrates automated provisioning of optical lightpaths triggered via a user-friendly graphical interface. The process includes endpoint discovery, transceiver selection, and lightpath establishment, all performed autonomously across multiple domains to support a video streaming service. This work demonstrates the novelty of hierarchical cross-domain orchestration, showing how TFS can unify multivendor environments under a single platform with minimal configuration overhead. This lays the groundwork for future developments in automated service provisioning, closed-loop control, and scalable cross-domain networking.

Traditional 5G user plane function (UPF) architectures are software-based implementations that struggle to maintain performance. To meet the stringent quality of service (QoS) and scalability requirements of 5G under high session and data plane loads, a highly efficient next-generation UPF is required. In this paper, we present a next-generation 6GUPF that fully leverages the hardware acceleration of SmartNICs/DPUs. The 6GUPF is developed using DOCA Flow API, which enables fast, programmable packet processing directly in the data path, specifically for the N3 gNB network and N6 interfaces of datanet. The architecture integrates flow-based acceleration for stateful flow tracking and symmetric Receive Side Scaling (RSS) to utilize cores and manage non-blocking states efficiently. This will help minimize latency and avoid contention in multi-core environments. Our experiments show that hardware-offloaded UPF achieves an aggregate line rate of 400 Gbit/s, supports 1 million concurrent data streams, and scales up to 500,000 active User Equipment (UE) instances while maintaining QoS and session isolation. It also enables 40% lower latency compared to traditional software UPFs. Unlike Tofino-based P4 switch UPF designs, which are limited by SRAM and TCAM constraints when storing large-scale data streams, our DPU-based solution is ideal for high-density Protocol Data Unit (PDU) deployments without sacrificing programmability and performance. It creates a path for cloud-native, 6G UPF deployments that can scale to a wide range of workloads, from ultra-low-latency applications to large-scale IoT backhaul.

This paper introduces the issue of highly available Software Defined Flying ad-hoc Network for critical real-time applications. FANETs are a particular implementation of VANET (Vehicle Ad-hoc NETworks) where the vehicles, in the specific case, are Unmanned Aerial Vehicles (UAV), or drones. VANETs in general suffer from the mobility of the nodes, which can lead to continuous disconnections and topology changes. This issue becomes particularly troublesome for Flying ad-hoc networks (FANET), which are characterized by flying nodes, with low node density and a high rate of topology changes. On the other hand, nowadays, drones are widely used both in civil and industrial environments, and thus they may need to satisfy certain requirements in terms of the availability of communication. In the traditional distributed networks, the propagation of the information when there is a change in the topology, and the consequential update of the information in each switch, can introduce long periods of unavailability, which cannot be tolerated in certain contexts. For this reason, we decided to introduce an approach based on SND to manage FANET in an efficient and reliable way. The SDN architecture relies on a centralized controller to configure the network devices by exploiting a control plane channel. However, having a centralized controller implies a single point of failure. Moreover, control plane links are critical in SDN, since the controller uses those links to update the configuration of the switches. In order to efficiently introduce the SDN paradigm in a dependable FANET it is worth considering a novel approach, where the controller is responsible for the main routing decisions, but the network is able to operate autonomously and tolerate topology changes, within a certain degree, even when the SDN controller is not connected. The proposed architecture is a P4-SDN, that is capable of automatically rerouting traffic even when the controller can not communicate with the involved devices, providing then an additional level of redundancy. The programmable switches are instructed to take countermeasures when an unexpected link fault occurs and the controller is unreachable. In our solution, the controller configures both primary and backup actions for each expected flow, leveraging the programmability of P4-enabled devices. Thus, each switch can choose to forward the packets over the primary path or follow the backup action. Since these countermeasures are taken locally and independently from the controller, this approach allows the network to operate even when the control-plane link is unusable. It also improves the handover of the fault when the controller is reachable, minimizing critical application traffic disruptions.

The fast-paced growth of 5G networks, along with the emergence of 6G technology, has emphasized the crucial importance of strong security measures to safeguard communication infrastructures. A key security issue in 5G data networks is Distributed Denial-of-Service (DDoS) at tacks, which specifically target the GTP-based protocol which is a significant threat. However, network telemetry data provides a rich source of information about the nature of network traffic, which can be used to detect and predict DDoS attacks. We propose a novel framework for collecting and processing large amounts of telemetry data in 5G networks leveraging state-of-the-art technologies, including data-plane programmability in P4-based User-Plane Function (UPF) and Data Processing Unit (DPU). Furthermore, we propose an anomaly-detection method for performing live deep learning analysis on network traffic using a Convolutional Neural Network (CNN) to detect DDoS attacks. Our results demonstrate the effectiveness of our framework, achieving an impressive 98.6% accuracy and 98% F1-score.

The implementation of zero-touch network and service management solutions in software defined optical networks requires the elaboration of detailed optical components’ information. However, such data can be elaborated by third parties. Thus, confidentiality issues may arise because providers are not willing to unveil their detailed information. This study proposes schemes based on dataset scrambling and unsupervised machine learning algorithms for soft failures detection in optical networks. A key aspect of the proposed scheme is the preservation of data confidentiality, that refers, in this context, to safeguard the detailed information of optical components while still enabling effective failure detection. The performance of six different clustering algorithms have been experimentally evaluated in a laboratory testbed. The results reveal that certain algorithms, while working in a confidentiality preserving scheme, perform very well in clustering different states (i.e., working and faulty states) of the network.

This demonstration shows the effectiveness of a hierarchical Software-Defined approach in coordinating Radio Access Network (RAN) and Passive Optical Network (PON)-based RAN transport to proactively scale virtual Distributed Units (vDUs) / Radio Units (RUs) and to jointly reconfigure the mid-haul transport.

This paper introduces a novel framework for enhancing quality of service predictability in Flying ad hoc Networks (FANET) by leveraging P4 data-plane programmability. The proposed solution, P4 FANET In-Band Telemetry (FINT), is specifically designed to tailor the limited resources in wireless networks and is extended to collect not only the standard INT metadata but also novel essential Unmanned Aerial Vehicles (UAV) real-time metrics, including Received Signal Strength Indicator (RSSI), geolocation information and CPU load. These parameters are then fed into an artificial intelligence (AI) system, enabling proactive prediction of FANET link failures. By integrating P4 FINT and AI, our framework aims to improve the availability and overall performance of UAV-based networks through advanced link failure forecasting.

The widespread connectivity provided by fixed and mobile communication technologies can facilitate the utilization of e-health and mobile-health services that are radically changing the way healthcare may be provided. Such services are particularly important for inner areas, where it is difficult to guarantee physical availability and proximity of services while ensuring the sustainability of the system. However, e-health and m-health service deployment requires careful planning, as connectivity in inner areas can be spotty. This paper reports how multiple e/m-health solutions have been planned in some inner areas of Tuscany within the PROXIMITY-CARE project. In particular, it introduces a newly developed QGIS-based analysis tool, which allows correlating connectivity data with patient needs and healthcare facilities location. Moreover, the paper presents a tele-tutoring system for the emergency service and a mobile application to monitor patient vital parameters. Such tools facilitate the maximization of the reached population, thus improving benefits for the patients.

This research aims to propose an approach to address the unpredictability topology state issue of FANET. The mobility of the network can lead to frequent link disruptions, causing communication unavailability. To mitigate this, our goal is to implement an AI algorithm that can identify patterns in UAV mobility, predict potential disconnections, and trigger rerouting/forwarding algorithms in advance. This paper presents an example of an SD-FANET able to provide wireless in-band telemetry to the AI-equipped edge node placed at the ground station, discusses the design of subsystems hosting the AI process, and demonstrates how a machine learning model can recognize critical network situations without relying on complex neural networks.

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models.

The deployment of 5G and network slicing has challenged the current network management requirements, triggering the need for programmable and software-driven architectures. Thus, automated real-time fault management for self-managed networks with machine learning and artificial intelligence at the forefront has become necessary. This is especially the case of optical communication systems, accountable for most of the data traffic worldwide. This study introduces the application of a novel failure detection and localization framework capable of forecasting failures in optical systems based on an unsupervised learning strategy. In this approach, the Long- and Short-term Time-series Network (LSTNet) is exploited for modeling the normal behavior of optical systems. Then, failure conditions are properly forecast without explicitly training the model for such cases, easing the data acquisition process. Later, forecast values and actual measurements from optical equipment are used to derive an outlier detection method to detect and locate failures to improve the decision-making process at the network orchestrator. Laboratory experiments comparing the proposed approach with the Recurrent and Long Short-Term Memory models in terms of failure detection and forecasting performance show that using the LSTNet reduces the mean squared errors in 95% for unseen data, indicating robustness and suitability for real-world environments.

Network programmability based on the P4 language is gaining consensus in multiple scenarios, including edge computing. In this work, we present the P4-based edge networking solutions adopted in the framework of the EU-funded BRAINE Project. The project targets the design and development of a powerful edge micro data center (EMDC) aiming at boosting artificial intelligence (AI) at the network edge. The EMDC will encompass an embedded programmable P4 ASIC supporting unprecedented intra- and inter-edge/fog interconnection. In this paper, a selection of the solutions to be supported by the P4 switch embedded within the BRAINE EMDC is presented. They include decentralized traffic engineering solutions driven in-band telemetry (INT), quality of service enforcement and verification, 5G network function virtualization, and decentralized cyber-security at the edge.

The performance of optical devices can degrade because of aging and external causes like, for example, temperature variations. Such degradation might start with a low impact on the Quality of Transmission (QoT) of the supported lightpaths (soft-failure). However, it can degenerate into a hard-failure if the device itself is not repaired or replaced, or if an external cause responsible for the degradation is not properly addressed. In this work, we propose comparing the QoT measured in the transponders with the one estimated using a QoT tool. Those deviations can be explained by changes in the value of input parameters of the QoT model representing the optical devices, like noise figure in optical amplifiers and reduced Optical Signal to Noise Ratio in the Wavelength Selective Switches. By applying reverse engineering, the value of those modeling parameters can be estimated as a function of the observed QoT of the lightpaths. Experiments reveal high accuracy estimation of modeling parameters, and results obtained by simulation show large anticipation of soft-failure detection and localization, as well as accurate identification of degradations before they have a major impact on the network.

Being able to account for the value of latency introduced by the transport infrastructure is considered a crucial feature for any 5G service provisioning strategy. This paper proposes a methodology for the automatic monitoring of the link latency in networks with a distributed SDN-based control plane architecture. The proposed link monitoring solution does not introduce any overhead in the OpenFlow channel and in the data plane. Experimental tests demonstrate the accuracy of the proposed methodology and its ability to scale to large network instances.

Supporting end-to-end network slices and services across operators has become an important use case of study for 5G networks as can be seen by 5G use cases published in 3GPP, ETSI as well as NGMN. This paper presents the in- depth architecture, implementation and experiment on a multi-domain orchestration framework that is ab le to deploy such multi-operator service as well as monitor the service for SLA compliance. Our implemented architecture allows operators to abstract their sensitive details while exposing the relevant amount of information to support inter-operator slice creation. Our experiment shows that the implemented framework is capable of creating services across operators while fulfilling the respective service requirements.

Elastic Optical Networks will drive a high degree of flexibility enabling dynamic configurable lightpaths provisioning and re- optimization due to next generation bandwidth variable transponders and switches. In order to guarantee quality of transmission (QoT), novel Operation Administration and Maintenance (OAM) solutions are necessary with respect to existing standard management protocols. For optical networks, scalable mechanisms providing fast and effective QoT alarm information, including localization and, possibly, forecasting critical events, are needed. The introduction of the Application Based Network Operation (ABNO) architecture is pushing towards a dedicated OAM Handler, in charge of collecting OAM information from the network, performing correlations and triggering control plane reaction. However, serious scalability issues may arise since a centralized element would have to elaborate a potentially huge amount of data. In this paper, a novel hierarchical OAM architecture is proposed, that enables multi- level OAM entities to provide OAM Handler with effective information, obtained by filtering several OAM messages at each layer, so that the overload of OAM Handler is avoided. Moreover, the NETCONF protocol, typically used for SDN- based node configuration purposes, is proposed and utilized as OAM protocol, in order to achieve high degree of convergence and limit the number of utilized protocols. The proposed OAM architecture is implemented and experimentally evaluated in a QoT degradation use case, showing that multi-level localization and local correlation of events allow aggregated, fast and scalable OAM information set provided to the OAM Handler.

Recent advances in network control and management technologies, such as software defined networking (SDN) and network function virtualization (NFV), are expected to make the network effectively able to cope with application service requirements in a more flexible and timely manner. We argue that SDN and NFV capabilities can be exploited in Next Generation Service Overlay Networks (NGSONs) for addressing contex-aware end-to-end data delivery services with adaptive composition of middlebox services when dynamically provisioned as virtual functions. In this paper we present a Service-Oriented SDN controller that allows for the programmable provision of data delivery paths through a dynamically established sequence of virtual middlebox functions, thereby accomplishing the deployment of adaptive network service chains in NGSON. In this way, an extended set of QoS requirements can be addressed that include specifications on processing functions to be traversed by service data while taking advantages from context-awareness and self-adaptation capabilities of NGSON. A use case analyis have been carried out on an experimental testbed about the effectiveness of the proposed approach in differentiating service data processing while optimizing the use of forwarding tables in the switches.

Nowadays, Data Centers are the primary infrastructures for Cloud Computing services provisioning. In this challenging scenario, Data Centers have to deal with highly dynamic workloads, thus not only should they adopt advanced software virtualization solutions, but also network control capabilities. Elastic, agile network connectivity control should be integrated with the computing management infrastructure to make it achieve its goals. This paper presents an OF-based controller, called OFVN, that enables novel virtualization-aware networking functions in Cloud DCs. Virtual Machines allocations are performed considering the availability of computational resources on physical server, but also selecting a forwarding path for VMs data flows based on network links utilization. The focus of the paper is on the implementation and assessment of the controller in an experimental testbed. The behaviour of different allocation strategies is also investigated.

This demo deals with a new orchestrator, the OFVN controller, specifically designed, according to the SDN principles, to deliver Cloud services with IT and network resources requirements.