The automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service autoscaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution.Our evaluation shows that AI/ML-related service handling operations (1-2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).

5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.

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There is a growing interest of verticals (in this case, the automotive industry) to reap the benefits of 5G networks. At the same time, there is a clear trend of the telco industry to understand their needs. These are also some of the main goals of the EU 5G-TRANSFORMER (5GT) project. This demo focuses on the need of verticals to dynamically deploy services at the edge and to adapt the vertical service to network operational conditions. In particular, it is presented the extended virtual sensing (EVS) service, which deployed on demand at the distributed computing infrastructure (i.e. in the network), complements sensing and processing functions running in the car to detect the risk of collisions and take appropriate action, even if there is no direct communication between cars. The stringent latency constraints imposed by the EVS network service leave a limited processing budget at the vertical service level. Since such processing time is correlated with the CPU consumption of a virtual machine running a VNF of the EVS network service, in this demo we also show how the vertical service exploits the automated scaling capabilities offered by the 5GT service orchestrator to deploy a new instance of the EVS VNF upon reception of a CPU consumption alert generated by the available 5GT monitoring platform.

5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive "greedy" approach in a realistic scenario.