Self-Modeling Based Diagnosis of Software-Defined Networks

Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-time and the building of the diagnosis model of any SDN infrastructure using our templates

Engineering software for next-generation networks in a sustainable way.

The virtualization and softwarization of network functions is the networking industry's latest achievement. Software-Defined Networks (SDN) and Network Function Virtualization (NFV) propose novel software architectures and development process adapted to for instance mobile networks (e.g., 6G). However, these architectures and processes are mainly defined by the telecommunications community, without much regard for the contributions of software engineering to generic software processes. This paper explores how the fields of software engineering (SE) and telecommunications can work together to improve service virtualization, cloud computing, and edge computing in the context of next-generation networks. It also highlights the potential of SE fields like software architecture, variability, and configuration to greatly enhance the development of virtual network functions (VNFs). On the other hand, the new contributions should be energy efficient, since this is a primary goal in next-gen networks. Finally, current software processes should consider the impact of communication networks on the correct functioning of software products, since network functioning can affect the QoE of users.Work supported by the projects \emph{IRIS} PID2021-122812OB-I00 (co-financed by FEDER funds), and \emph{DAEMON} H2020-101017109; and by Universidad de Málaga

Towards 5G Software-Defined Ecosystems: Technical Challenges, Business Sustainability and Policy Issues

Techno-economic drivers are creating the conditions for a radical change of paradigm in the design and operation of future telecommunications infrastructures. In fact, SDN, NFV, Cloud and Edge-Fog Computing are converging together into a single systemic transformation termed “Softwarization” that will find concrete exploitations in 5G systems. The IEEE SDN Initiative1 has elaborated a vision, an evolutionary path and some techno-economic scenarios of this transformation: specifically, the major technical challenges, business sustainability and policy issues have been investigated. This white paper presents: 1) an overview on the main techno-economic drivers steering the “Softwarization” of telecommunications; 2) an introduction to the Open Mobile Edge Cloud vision (covered in a companion white paper); 3) the main technical challenges in terms of operations, security and policy; 4) an analysis of the potential role of open source software; 5) some use case proposals for proof-of-concepts; and 6) a short description of the main socio-economic impacts being produced by “Softwarization”. Along these directions, IEEE SDN is also developing of an open catalogue of software platforms, toolkits, and functionalities aiming at a step-by-step development and aggregation of test-beds/field-trials on SDNNFV- 5G

Multicasting Over 6G Non-Terrestrial Networks: A Softwarization-Based Approach

Multicast/broadcast delivery is a critical challenge of future 6G mobile networks where massive Internet of Things (IoT) deployment and extended reality multimedia such as teleportation are target application scenarios. Non-terrestrial networks (NTNs) are considered essential for the success of 6G, which aims to provide true 'global' services by extending mobile access worldwide, thus overcoming the coverage limit of current terrestrial networks (TNs). This article discusses how the main distinguishing features of NTNs can be effectively exploited for 6G multicasting. Furthermore, in line with the evolution of future 6G networks toward softwarized systems, we evaluate the potential of using the softwarization paradigm in the heterogeneous TN-NTN architecture to deliver multicast services

Softwarized resource allocation in digital twins-empowered networks for future quantum-enabled consumer applications

Network softwarization (NetSoft), recognized as crucial attribute of 6G networks, promises to provide enhanced and advanced services, including future quantum-enabled consumer applications. Softwarized resource allocation is the core issue in NetSoft concept. Digital twins (DT) guarantees to generate the corresponding digital world that reflects and interacts with the original physical world seamlessly. With DT empowering, the digital replica of softwarized networks can be generated to predict, simulate, analyze the softwarized resource allocation in more economical, convenient and scalable methods.In this paper, we research the softwarized resource allocation of requested services, usually, called as slices, in DT-empowered networks for future quantum-enabled consumer applications. We focus on developing efficient softwarized resource allocation algorithm. At first, we present models of the DT-empowered networks and service requests by using graph theory and hypergraph theory. Then, we design one softwarized resource management framework, labeled as DT-Slice-Soft-6G. This framework has the functions of managing softwarized resources, calculating resource allocation solution in digital replica and sending the calculated solution back to softwarized 6G networks. Thereafter, one efficient and fine-grained softwarized resource allocation algorithm, inserted in DT-Slice-Soft-6G, is detailed. This algorithm is labeled as Heu-DT-Slice-6G and is proposed based on efficient heuristic methods. To validate the highlights of DT-Slice-Soft-6G and Heu-DT-Slice-6G, we conduct the simulation work in our self-developed simulator

Network slice selection in softwarization-based mobile networks

Recently, network slicing (NS) has been introduced as a key enabler to accommodate diversified services in network functions virtualization–enabled software‐defined mobile networks. Although there has been some research work on network slice deployment and configuration, how user equipments select the most appropriate network slice is still an essential yet challenging issue, as slice selection may substantially affect the resource utilization and user Quality of Service (QoS). In this paper, we investigate the optimal selection of end‐to‐end slices with the aim of improving network resources utilization while guaranteeing the QoS of users. We formulate the optimal slice selection problem as maximizing the users satisfaction degree and prove it is NP‐hard. We thus resort to genetic algorithm (GA) to find a suboptimal solution and develop a GA‐based heuristic algorithm. The effectiveness of our proposed NS selection algorithm is validated via simulation experiments

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components