Network slicing plays a crucial role in the progression of 5G and beyond, facilitating dedicated logical networks to meet diverse and specific service requirements. The principle of End-to-End (E2E) slice includes not only a service chain of physical or virtual functions for the radio and core of 5G/6G networks but also the full path to the application servers that might be running at some edge computing or at central cloud. Nonetheless, the development and optimization of E2E network slice management systems necessitate a reliable simulation tool for evaluating different aspects at large-scale network topologies such as resource allocation and function placement models.  This paper introduces Slicenet, a mininet-like simulator crafted for E2E network slicing experimentation at the flow level. Slicenet aims at facilitating the investigation of a wide range of slice optimization techniques, delivering measurable, reproducible results without the need for physical resources or complex integration tools. It provides a well-defined process for conducting experiments, which includes the creation and implementation of policies for various components such as edge and central cloud resources, network functions of multiple slices of different characteristics. Furthermore, Slicenet effortlessly produces meaningful visualizations from simulation results, aiding in comprehensive understanding.  Utilizing Slicenet, service providers can derive invaluable insights into resource optimization, capacity planning, Quality of Service (QoS) assessment, cost optimization, performance comparison, risk mitigation, and Service Level Agreement (SLA) compliance, thereby fortifying network resource management and slice orchestration.

This paper surveys recent research on Federated Learning-based resource allocation for next-generation networks with the aim of identifying research gaps and potential directions for future work. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in literature are then reviewed. The strengths and limitations of these solutions are analyzed and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., Game theory or optimization techniques with Federated Learning) and developing new models and algorithms for AI-based resource allocation that addresses specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field.

5G/6G network slicing is identified as key enabler technology for allowing a diversity of sustainable applications while satisfying user’s requirements. A major problem of the traditional networking technologies is the use of the ”one size fits all” approach that handles all types of services. In this work, we focus on drawing a new architecture born of the need of a multi-level services’ orchestration for seeking flexible intelligent management of the new generation use-cases such as massively deployed cloud and edge-cloud based IoT applications for (i) environment monitoring, (ii) Agriculture development, (iii) and new generation Augmented Reality applications in medicine or e-learning.Thispaperproposesanovelmulti-levelDelegationArchitecture for Network Slicing Orchestration (DANSO). DANSO proposes a multi-level delegation for slices management and optimization framework for resources allocation. Our proposal is based on three pillars: (i) definition of levels to fulfill support of network slicing deployment and management via delegated decisions (ii) split of orchestrator, manager and control roles and (iii) placement-independence of entities in (ii). DANSO has the following advantages. First, it provides an optimization mechanism for the deployment of network slices. second, it takes into consideration a negotiation process for the requested slice. Third, it manages the life-cycle not only for the Virtual Network Function (VNF) level but also for the dynamic deployment and suppression of the whole slices. last, it considers network slicing over several administrative domains.

Recent advances in telecommunication and machine learning (ML) have allowed for new smart and autonomous vehicle applications to improve road safety, environmental conditions, and traffic management through Vehicle to Vehicle (V2V) or Vehicle to Infrastructure (V2I) communication. However, with the rise of advanced cyber-attacks, the authenticity of a message guarantees its source but not its correctness. To mitigate the new sophisticated attacks, new Misbehavior Detection Systems (MDS), that use machine learning algorithms to detect misbehaving vehicles, have been proposed. This work provides first a comprehensive review of recent developments in ML-based MDS technology within a Vehicular Ad-Hoc Network (VANET) context, covering data collection, feature selection, model training, model evaluation and deployment. We survey useful public datasets and summarize recent studies. We report useful pieces of information for every work. In particular, we highlight the considered dataset for ML training, list the selected ML models, indicate the feature selection and dimensionality reduction techniques, recapitulate the main results, report the performance metrics and mention the deployment guidelines when applicable. Then, we compare the surveyed studies discussing not only the strength points but also their limitations. One of the key observations from the surveyed works is the absence of a quantitative analysis of the proposed models’ execution time, which is a crucial performance metric considering the limited on-board and edge computing resources. To develop a feasible ML-based MDS for V2X communication, it is essential to address this issue and propose a deployment strategy that optimizes the allocated resources for this technology. However, achieving this remains a challenge. Moreover, in view of the fact that data generation and analysis are critical phases in this technology. Also, using simulation has many advantages over the real data collection, we provide a tutorial on how realizing a useful dataset collection with popular open-source tools while considering exemplar types of attacks. Last, we demonstrate through the tutorial the use of the collected dataset for ML-based MDS model selection and training. An open source github repository is provided for regenerating the whole explained scenarios and modify according to the given research issue.