Network Function Virtualization (NFV)-based 5G networks deliver specific services to a end-users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the Admission Ratio (AR) calculation in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices form end-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analysis, we not only take into account the occupancy distribution in both the network's computational and communicational domains (servers and fiber links), but we also consider the SFC establishment AR by taking into account different sub-service-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the proposed model is employed for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks), in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas, which have a low computational complexity, as opposed to time-consuming simulations, without requiring the application of complex optimization algorithms.

This paper proposes a methodology for the efficient allocation of both optical and wireless resources in an SDN/NFV-based converged optical-wireless network architecture. Our approach considers a network slicing architecture where different network slices form end-to-end logically isolated networks, each one dedicated to a different type of service with diverse requirements. The target of the proposed approach is to optimally determine the network slices so that the specific delay and bandwidth requirements of the multiple services are met, by considering both the optical and wireless network resources.

The integration of Terrestrial and Non-Terrestrial Networks (TNs-NTNs), known as Space-Air-Ground Integrated Networks (SAGINs), is a foundational element of future sixth Generation (6G) systems promising ubiquitous connectivity. However, the high mobility of Low Earth Orbit (LEO) satellite constellations presents significant challenges, rendering traditional static routing paradigms and network management solutions ineffective. The frequent topological changes inherent in these environments necessitate the development of intelligent and adaptive network management frameworks. This paper introduces a Software Defined Networking (SDN)-based architectural framework designed to address this challenge. The proposed framework aligns with key principles from 3GPP and ETSI Network Function Virtualization (NFV) standards to enable the creation of a unified management plane. This plane facilitates the End-to-End (E2E) orchestration of services, encompassing both traditional mobile core elements and Virtualized Network Functions (VNFs) across the SAGIN infrastructure. The proposed solution utilizes a centralized control system, wherein a custom SDN application leverages externally generated topological data to proactively manage data paths. This management is facilitated by an extensible satellite network simulator, the LEO Routing and Simulation Framework (LRSIM), which decouples complex path computation from real-time control. This separation allows the system to react rapidly to network state changes. The architecture, its operational workflows, and the control mechanism are demonstrated through an empirical evaluation on a scalable virtualized testbed capable of emulating realistic, multi-orbit LEO constellations. Performance is assessed based on Round Trip Time (RTT), jitter, throughput, and controller reaction time, where the results validate the framework’s capability to maintain resilient E2E connectivity and high performance.

As network slicing gains traction in cloud computing environments, efficient management and orchestration systems are required to realize the benefits of this technology. These systems must enable dynamic provisioning and resource optimization of virtualized services spanning multiple network slices. Nevertheless, the common resource overprovisioning practice implemented by service providers leads to the inefficient use of resources, limiting the ability of Mobile Network Operators (MNOs) to rent new network slices to more vertical customers. Hence, efficient resource allocation mechanisms are essential to achieve optimal network performance and cost-effectiveness. This paper proposes a predictive model for network slice resource optimization based on resource sharing between Virtualized Network Functions (VNFs). The model employs deep learning models based on Long Short-Term Memory (LSTM) and Transformers for CPU resource usage prediction and a reactive algorithm for resource sharing between VNFs. The model is powered by a telemetry system proposed as an extension of the 3GPP network slice management architectural framework. The extended architectural framework enhances the automation and optimization of the network slice lifecycle management. The model is validated through a practical use case, demonstrating the effectiveness of the resource sharing algorithm in preventing VNF overload and predicting resource usage accurately. The findings demonstrate that the sharing mechanism enhances resource optimization and ensures compliance with service level agreements, mitigating service degradation. This work contributes to the efficient management and utilization of network resources in 5G networks and provides a basis for further research in network slice resource optimization.

AI/ML techniques play a key role in 5G/6G networks providing connectivity to IoT devices. In such scenarios, not only is it necessary to run time-sensitive applications with strict latency requirements without human intervention, but it is also key to apply automation techniques at both the application and the network levels. The chapter is composed of three sections. In the first section, we present different cloud native (CN) technologies enabling scalable, costefficient, and reliable IoT solutions. The second section details different distributed and hierarchical monitoring frameworks and metrics collection schemes as inputs to AI engines. In the last section, application placement problems focused on delay minimization in geographically distributed singlecluster environments arc first discussed. Afterwards, application placement issues ensuring latency requirements for the applications and energy consumption in distributed multi-access edge computing (MEC) systems using AI pipelines arc presented. <!----> <!----> <!----> <!---->