Traditional rule-based or static management approaches struggle to cope with the dynamic, multi-layered nature of 5G/6G networks, creating a strong motivation for AI-native solutions – management systems built from the ground up with artificial intelligence – to enable autonomous, real-time network control. In this work, we introduce GNN-ATIVE, an AI-native orchestration framework that leverages Graph Neural Networks (GNNs) and knowledge graphs (KGs) in a unified graph-based paradigm for network management. GNN-ATIVE uses a semantic knowledge graph to represent the network’s state and context, employing standard ontologies to ensure consistency and interoperability. Building on this foundation, we design Knowledge Graph enabled Generative Pretrained Transformer (KG-GPT), a novel graph-to-graph Transformer model that performs knowledge-driven reasoning on the KG. KG ingests the structured network state (nodes, links, and attributes) and infers optimal configurations or management actions, serving as a high-level decision engine for the orchestrator. We implement and evaluate GNN-ATIVE on an Optical Transport Network (OTN) testbed using real network components. The results demonstrate that GNN-ATIVE can effectively manage OTN resources and adapt to network changes while achieving low-latency inference for decision making.

The growth of telecommunication networks towards 6 G, owing to the complexity and heterogeneity, mandates a paradigm shift in network management. Due to the nature of the traditional Policy-based Management and Control (PBMC), aspects of network management have to be well thought out during the design phase making the system inflexible. Artificial Intelligence (AI) is expected to augment PBMC by introducing run-time flexibility and adaptability. The use of AI becomes more pressing, when 6 G systems are considered to host more diverse technologies catering to specific use cases thus increasing the burden on operators. This article presents “GraphGPT”, a transformer-based Knowledge Graph (KG) reasoning model prepared using light-weight and tailored datasets for 6 G. The implementation and evaluation details present the first look of the model.

Network management systems for beyond 5G (B5G) and 6G networks today require efficient approaches for handling increased heterogeneity, network-function dis-aggregation, performance requirements, and optimizing networks to support highly diverse use cases. While the Open-Radio Access Network (O-RAN) Service Management and Orchestration (SMO) frameworks efficiently manage RAN and cloud infrastructure, the fragmentation of management platforms across RAN, Core Network (CN), and Transport Network (TN) introduces operational inefficiencies, particularly in Non-Public Networks (NPNs) deployments. This paper proposes a novel extension to the O-RAN SMO architecture, integrating CN and TN management functionalities into a unified control framework. By exploiting AI/ML-driven$\mathrm{x} / \text{rApps}$and a converged data analytics pipeline, the proposed architecture enhances SMO's fault management, resource optimization, and service continuity capabilities. Our implementation validates the feasibility of the proposed SMO extension, demonstrating subscriber-specific QoS assurance through O-RAN-based mobility management mechanisms. The proposed approach successfully shows how RAN-/Core-converged SMOs enable significant enhancements to O-RAN's x/rApps, allowing for subscriber-specific as well as application-specific differentiated QoS assurance.

The distributed Cloud Computing paradigm is continuously being adopted within the industrial automation domain. The most distinguishing feature of these Edge Clouds relates to their ability to provide low-latency and even hard realtime services. As infrastructure deployments can be rather heterogeneous in their nature, service providers require precise means for estimating end-to-end application latency behavior, in order to know performance boundaries that can be met for defining certain Service Level Agreements (SLAs). Although network performance tools exist for many years, mechanisms for assessing hard real-time performance of applications in distributed Edge Cloud environments have not been considered extensively yet. Therefore, we use a built-in feature of the Linux Kernel, the extended Berkeley Packet Filter (eBPF), to measure delays between targeted endpoints in the kernel stack, that enable the user to gain deeper and more accurate measurements of events as compared to generalized approaches (as accurate as eBPF / the time-stamping facility from the kernel). As a result, the real-time behavior of particular Edge Cloud deployments, including its hosted applications, can be profiled in detail by end-users as well as service-providers. Within our evaluation we have monitored a cyclic transmission of packets with a scheduled delay of under 190 μs and measured a round trip time under 2 ms. Future work include profiling the real-time behavior of potentially hosted time-critical applications, such as virtual Programmable Logic Controllers (vPLCs), over real-time networks, such Time Sensitive Networking (TSN); the extension towards dynamically configured real-time networks; and finally its application to future organic, self-optimizing Ultra-Reliable Low-Latency Communication 6G core networks.

The benefits of the currently evolving IEEE Time-Sensitive Networking (TSN) standard have already been globally recognized. Whereas the application of TSN in a LAN is currently widely and globally tested, TSN in a Metropolitan Area Network (MAN) has not been a major focus until now. The possible benefits of utilizing co-located Edge Clouds in order to support multiple urban production sites with industrial realtime applications open a wide range of new business models. Therefore, we have analyzed the feasibility of transparently using PROFINET over TSN via a Dense Wavelength Division Multiplex (DWDM) link, where a machine park is controlled remotely by an Edge-based virtual Programmable Logic Controller (vPLC). As a result, we are able to setup a TSN connection over a MAN with a one-way delay of about 156.5 J.μs and a jitter of about 12 ns. This work can be extended to allow for dynamically provisioned TSN flows and multi-path Frame Replication and Elimination (FRER) for distributed hard real-time machine control and adoption to Ultra-Reliable Low-Latency Communication (URLLC) 5G campus networks.

Technical and economic opportunities of cloud computing become the focus for Internet applications and at the same time also for telco support infrastructures and network services. In fact, many telco providers are consolidating their service infrastructures towards converged and all-IP next generation networks providing typical telco services within LTE (and soon 5G) and also fixed network environments, e.g., often still adopting IP Multimedia Subsystem (IMS) architecture solutions. This telco service infrastructure evolution requires a significant upfront investment in the necessary hardware and software, thereby slowing down the adoption process significantly more than any other Internet application. Cloud computing applied to telco infrastructures can allow pay-per-use business models and significantly lower investment risks by providing telco infrastructure functionality as Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) platform. For this purpose we propose a quality audit and resource brokering framework that is fully NFV-compliant. Its current and reported implementation specifically targets IMS services because of the still relevant role played by IMS in converged provisioning and the wide availability of IMS deployment testbeds to validate the proposal. In particular, the proposed solution can monitor the quality offered by VNFs and scale in/out depending on dynamic requirements; it is fully based on industrial standards and open-source reference implementations, thus enabling rapid adoption in real industrial environments.

The global trend of Future Internet related experimentally driven research has gained a strong momentum. In this context sustainability and the best use of developed infrastructures are highly critical. While many facilities have reached a level of maturity that allows them to be opened up to a wider use, mainly the academic sectors have been targeted. The Fanning out Testbeds-as-a-Service for the EIT ICT1 (FanTaaStic) project explores best practices, has performed a gap analysis, investigates applicable business models and works out a concept for the operation of a self-sustainable commercial testbed service offering. For defining the operational model, an analysis of available tools and frameworks from past and current Future Internet testbed projects is being carried out, based on a standard framework that defines the required processes and applications for the carrier grade operation of telecommunication infrastructures and provisioning of services. The business model is defined as a gradually growing service offering, based on categories of a framework for IT Service Management in federated e-infrastructures. The applicability of both, the developed business and operational model for such a sustainable federation will initially be implemented and evaluated in 2014.

Cloud computing management supports are becoming more and more important not only in the fields of IT infrastructures for Internet applications and services, but steadily also in the field of telecommunication services and infrastructures. More and more telecommunication service providers have adopted IP Multimedia Subsystems (IMS) to consolidate their service infrastructures towards converged, all-IP, access network independent Next Generation Networks (NGNs). Although modern NGN service environments have the potential to greatly reduce new telecommunication services time-to-market, until now significant upfront investments into computational resources are required, that is still often a risk for the enterprise, with unsure return on invest. The application of cloud computing technologies to IMS-based service infrastructures enables new pay-per-use cost models, so IMS service providers may be charged for what they use only, significantly lowering the risk of bad investments. This work presents the design and implementation of a cloud brokering system for IMS services, capable of simultaneously interworking with multiple cloud infrastructures. We developed a Cloud Broker Engine (CBE) able to dynamically up/down-scale cloud resources across multiple cloud platforms. Our CBE is capable of coping with dynamic load situations and QoS requirements optimizing resource utilization across different cloud infrastructures: it enables QoS assurance and optimizes resource consumption across multiple cloud providers.

Next generation telecommunication network operators securely opening up their network capabilities and services to third party service providers require flexible service delivery platforms. Policy based service exposure, service discovery and service composition mechanisms are required to offer chargeable services and service building blocks to external entities in a customizable way. Based on research and developments conducted while prototyping solutions for the Open SOA Telco Playground, a unique IMS based NGN testbed for realizing SOA based NGN service delivery platforms, this work proposes the extended policy based, semantically enabled service broker (XPOSER). XPOSER provides novel NGN service exposition mechanisms. It enables intent-based NGN service discovery and allows for user-centric, automated service composition. Utilizing SOA based operation support systems this work explains requirements and solutions for dynamically managing NGN service compositions by tightly linking service creation, service fulfillment and service assurance mechanisms already at service composition time.

The wide adoption of web-based distributed systems and consequently their complexity leads to an increasing demand for performance and QoS testing tools for these systems. Pushing forward open NGN protocols has resulted in a proliferation of benchmarking, interoperability and prototyping tools, yet there is still no solid strategy for integrating these efforts, collecting test information and processing it at higher levels. We propose a thin management layer able to consume real-time test data from existing tools, using it for statistics collection and fault management based on an extensible policy system. The results of validating the proposed system in connection with a unified web service and NGN performance testing tool are discussed.