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.

Network Virtualization (NV) has proved a promising technology that allows multiple heterogeneous Virtual Networks (VNs) to operate simultaneously on the same infrastructure. Dynamic Virtual Network Embedding (NVE) has emerged as an enabler of elasticity and scalability in the VN deployment and resource allocation of the physical infrastructure. However, the key challenge in realizing NV in a sustainable way is how to dynamically embed VNs efficiently into physical network, which is defined as the VNE problem. To address this challenge, this paper proposes an approach that leverages Multi-Agent Reinforcement Learning (MARL) to solve the dynamic VNE of VNs for 5G/6G communication systems. The proposed framework consists of multiple horizontally distributed RL agents that co-operate to devise temporally dynamic VNE placements. The key contributions of this work are introducing a novel dynamic VNE orchestration framework for multi-domain networks based on Distributed RL, providing a scalable VNE framework targeted to Ultra-Reliable Low-Latency Communication (URLLC) services, evaluating and comparing the proposed algorithm with existing solutions in the state of the art. The paper concludes that there is a significant improvement in latency of 144.191% when compared to the baselines.

As commercial 5G networks become commercially available and 6G looms in the horizon, the adoption of this new technologies depends on the way current and yet-to-come vertical industries put them to use. This accelerates the process of adoption by the end users, which are the final consumers of these technologies. 5G networks have multiple use cases associated with its new features, being the Ultra-Reliable Low Latency Communications (URLLC) use case one of the most instrumental for verticals such as remote teleoperation, factory automation and autonomous driving. In this paper, we design a general purpose and low-cost end-to-end (E2E) 5G/Beyond-5G Experimental Platform for URLLC applications, supporting Platform-as-a-Service (PaaS) with Artificial Intelligence (AI) and Machine Learning (ML) capabilities for online data analytics and automated decision making. The experimental evaluation of our platform demonstrates an average one-way latency on the DL and UL as low as 4.1 ms and 6.60 ms, respectively, 13.8 ms of end-to-end latency (E2EL), and a 31.7 ms E2EL between UEs for data frames of a teleoperation Web App with video feedback, demonstrating the capability of our platform in relation to other state-of-the-art testbeds for URLLC applications.

5G and Beyond 5G systems are built with unprecedented scale, heterogeneity and programmability in mind, and come with features such as increased technological complexity (due to increased heterogeneity and abstraction); dynamic behavior of the network prompted by dynamic demand for new functionality and capacity extensions; variability and volume of network access (e.g., type and number of connections, diverse types of services and traffic patterns), etc. Therefore, one of the major network management requirements is automation. Rather than having only local, segregated automation mechanisms (e.g., node self-configuration, SON), it is now agreed that end-to-end proactive automation is a must. In this paper, we present a novel solution for supporting automated and proactive decision-making at the network slice level in 5G/ Beyond 5G telecommunication systems. We propose a Graph-based Interpretable Anomaly Detection (G5IAD) framework that combines the use of Graph Convolutional Neural Networks with model correlations between multivariate network slice Key Performance Indicators (KPIs) and forecasts individual time series using Recurrent Neural Networks/GRUs. The architecture also includes an anomaly detection mechanism based on probabilistic classification. This unsupervised approach helps network operators identify & explain features of the KPIs, leading to faster root cause analysis and enabling self-managed network slices. Our model was tested in a realistic 5G simulation and demonstrated 45.9% better accuracy when trained with a Graph CNN and Recurrent Neural Network compared to the GRU model.

Managing network slices in 5G networks and in communication technologies Beyond-5G (B5G) requires intelligent mechanisms to ensure users’ service access and to maximize the utility and efficiency of the network’s physical resources. To achieve this, we propose a mechanism based on Reinforcement Learning (RL) for the Admission Control (AC) of User Service Requests (USRs) into network slices through dynamic bandwidth (BW) reallocation. Our approach admits, delays or rejects USRs into service depending on the BW of the slice and the utility this generates for the infrastructure provider (InP). This approach achieves very low USR rejection rates (RRs) with very high resource efficiency, even when peak traffic loads considerably exceed the BW capacity causing resource scarcity scenarios. When compared against a static BW allocation mechanism, our approach achieves RRs that are a fraction (0,33) of those achieved by static (smaller RRs are better), with 33,2x less resource overallocation, significantly achieving a very high resource efficiency.

The evolution of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in the telecommunications industry have intensified the issues of network management at large scales. Dynamic service orchestration and adaptive resource allocation became a necessity for network operators to manage the rapid growth of users and data-intensive applications. The impact of network automation on energy consumption and overall operating costs is often overlooked. Guaranteeing strict performance constraints of Ultra-Reliable Low Latency Communication (URLLC) services while enhancing energy efficiency is one of the major critical problems of future communication networks, given the urgency to reduce carbon emissions and energy consumption. In this work, we study the problem of zero-touch Service Function Chain (SFC) orchestration for multi-domain networks, targeting the latency reduction of URLLC services while improving energy efficiency for beyond-5G networks. Specifically, we propose SCHE2MA, a Service CHain Energy-Efficient Management framework based on distributed Reinforcement Learning (RL), that can intelligently deploy SFCs with shared VNFs per se into a multi-domain network. Finally, we evaluate SCHE2MA through model validation and simulation while demonstrating its ability to jointly reduce average service latency by 103.4% and energy consumption by 17.1% compared to a centralized RL solution.

The success of Software-Defined Networking and Network Function Virtualization enabled providers to partially automate the decision-making of various network operating workflows. Network slicing is an innate feature of the Fifth Generation (5G) and Beyond-5G (B5G) networks, bringing many advantages but also increasing the complexity of managing such networks. In this context, automation tools based on Artificial Intelligence (AI) have become a preeminent instrument of automation used by the industry, as it would be impossible to manually provision and manage such a vast and dynamic infrastructure. Safe interaction of the AI agents with the network is one of the predominant challenges, especially when Reinforcement Learning (RL) is used in critical environments. This is particularly important when the RL agent actions have a high or irreversible impact on the network or service. Slice management is one of the major features that operators want to automate, to offer future services at large scales with manageable complexity to the operator. However, during the exploration phase, RL agents can cause significant performance degradation during operation and possibly introduce irreversible damage to the service being offered. To address this major challenge, we propose a multi-agent, modular, SafeRL architecture for distributed slice orchestration. We study the problem of zero-touch slice management and orchestration, in the context of Ultra-Reliable Low Latency Communication services for B5G networks. Our results demonstrate improved performance over competing solutions, while ensuring the safety of the performed actions during real-time slice orchestration.

As the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.

The standard for 5G communication exploits the concept of a network slice, defined as a virtualized subset of the physical resources of the 5G communication infrastructure. As a large number of network slices is deployed over a 5G network, it is necessary to determine the physical resource demand of each network slice, and how it varies over time. This serves to increase the resource efficiency of the infrastructure without degrading network slice performance. Traffic prediction is a common approach to determine this resource demand.State-of-the-art research has demonstrated the effectiveness of machine learning (ML) predictors for traffic prediction in 5G networks. In this context, however, the problem is not only the accuracy of the predictor, but also the usability of the predicted values to drive resource orchestration and scheduling mechanisms, used for resource utilization optimization while ensuring performance. In this paper, we introduce a new approach that consists on including problem domain knowledge relevant to 5G as regularization terms in the loss function used to train different state-of-the-art deep neural network (DNN) architectures for traffic prediction. Our formulation is agnostic to the technological domain, and it can obtain an improvement of up to 61,3% for traffic prediction at the base station level with respect to other widely used loss functions (MSE).

As technology progresses, cars can not only be considered as a transportation medium but also as an intelligent part of the cellular network that generates highly valuable data and offers both entertainment and security services to the passengers. Therefore, forthcoming 5G networks are said to enhance Ultra-Reliable Ultra-Low-Latency that will allow for a new breed of services that will disrupt the industry as we know it today. In this work, we devise a unique fusion of Deep Learning based mobility prediction and Genetic Algorithm assisted service orchestration to retain the average service latency minimal by offering personalized service migration, while tightly packing as many services as possible in the edge of the network, for maximizing resource utilization. Through an extensive simulation based on real data, we evaluate the proposed mobility orchestration combination and we find gains in low latency in all examined scenarios.

The increasing demand for fast, reliable, and robust network services has driven the telecommunications industry to design novel network architectures that employ Network Functions Virtualization and Software Defined Networking. Despite the advancements in cellular networks, there is a need for an automatic, self-adapting orchestrating mechanism that can manage the placement of resources. Deep Reinforcement Learning can perform such tasks dynamically, without any prior knowledge. In this work, we leverage a Deep Deterministic Policy Gradient Reinforcement Learning algorithm, to fully automate the Virtual Network Functions deployment process between edge and cloud network nodes. We evaluate the performance of our implementation and compare it with alternative solutions to prove its superiority while demonstrating results that pave the way for Experiential Network Intelligence and fully automated, Zero touch network Service Management.