Peer–Peer Communication Using Novel Slice Handover Algorithm for 5G Wireless Networks

In this chapter, we present a comprehensive end-to-end (E2E) network slicing framework for fifth-generation (5G) wireless and core networks and provide our vision on network slicing for beyond 5G (B5G) networks using artificial intelligence (AI) technologies. We first give an introduction on the 5G communication networks by specifying distinct 5G networking characteristics. As the key features of 5G pose challenges on quality-of-service (QoS)-oriented resource provisioning, a new concept of network slicing is presented, based on software-defined networking (SDN) and network function virtualization (NFV) technologies, to enhance the overall resource utilization while guaranteeing QoS isolation among services. We focus on developing specific network slicing solutions for both 5G wireless networks and core networks. In addition, the evolution of B5G networks is discussed in terms of a potential networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate the effectiveness of the proposed E2E network slicing solutions.Keywords5G communication networksNetwork slicingWireless networksRadio resource slicingCore networksComputing and transmission resource slicingBeyond 5G networksAI-assisted network slicingQoSE2E delay

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

5G networks represent a paradigm shift from their 4G predecessors, engineered to cater to the burgeoning demands of densely populated areas and the high data throughput requirements of the Internet of Things (IoT) and smart city infrastructures. Beyond enhancing fundamental network attributes such as reliability, availability, and security, 5G introduces architectural innovations to accommodate these advanced needs. This paper delves into the architectural nuances of 5G, focusing primarily on the Standalone (SA) deployment method, distinguished by its utilization of network slicing. This approach leverages cutting-edge virtual network technologies, including Software-Defined Networking (SDN) and Network Function Virtualization (NFV), to forge virtual network slices tailored to diverse consumer needs. Such customization is imperative for meeting the expansive and varied connectivity demands characteristic of 5G networks.To navigate the complexities of 5G architecture, this study advocates for the adoption of Reference Architectures (RAs), which provide a high-level abstraction framework for dissecting and understanding intricate systems. By proposing a model that outlines the standalone architecture of 5G networks, our research lays the groundwork for evolving this model into a comprehensive RA for 5G. This foundational work is instrumental in equipping system designers with the tools necessary for generalizing systems to manage design and deployment challenges effectively, as well as the heterogeneity inherent in system components. Additionally, our proposed model facilitates the establishment of a common vocabulary, promoting a coherent understanding of component interactions and offering a unified system perspective. Ultimately, this paper contributes a pivotal model that not only enhances our comprehension of 5G network architecture but also paves the way for future advancements in network design and deployment, promising to significantly impact the development of smart cities and IoT ecosystems.Keywords: 5G, 5G architecture, Reference architecture, patterns. Network slicing, Network Function Virtualization, Virtual Network Function.

In order to meet the traffic demand from diverse next generation wireless network applications and exponentially increasing mobile subscriptions, various 5G network architectures are proposed by leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. Network slicing will be one of the 5G technologies that would support next-generation wireless applications over a shared network infrastructure. However, improper network slicing may lead to either over-provisioning or under-utilization of the underlying network infrastructure resources, especially the 5G core network. Over-provisioning of data plane components such as Serving Gateway (SGW) and Packet Data Network Gateway (PGW) can lead to higher CAPEX and OPEX to mobile operators. In this paper, we propose a novel auto-scaling approach called Bit rate Aware Auto Scaling (BAAS) that maintains a precise UE bit rate requirement in the network slices without over-provisioning of data plane resources.

Network slicing is a promising approach to meet the diverse requirements of the various use cases in the 5G networks. Hence, the mobile operators are moving forward to leveraging network slicing in order to measure up with the individual service expectations in their networks. Deploying different network slice types requires the global view of the network and the automated orchestration and management of the underlying resources. This is facilitated by utilizing software-defined networking and network function virtualization as the 5G key-enabler technologies. In this paper, we propose a practical network slicing resource management scheme which is comprised of a dynamic, priority-based resource allocation cooperating with an admission control unit. Adopting the proposed dynamic resource allocation would allow the admission control to comply with more NS requests while ensuring the desired requirements of the existing network slices. To validate the effectiveness of such a mechanism in a real environment, we take advantage of the features provided by OpenAirInterface and FlexRAN to efficiently manage multiple isolated network slices. In particular, we evaluate the significance of the network slicing, the isolation degree among created slices, and the effectiveness of the proposed scheme through several practical scenarios.

Network slicing is an architectural solution that enables the future 5G network to offer a high data traffic capacity and efficient network connectivity. Moreover, software defined network (SDN) and network functions virtualization (NFV) empower this architecture to visualize the physical network resources. The network slicing identified as a multiple logical network, where each network slice dedicates as an end-to-end network and works independently with other slices on a common physical network resources. Most user devices have more than one smart wireless interfaces to connect to different radio access technologies (RATs) such as WiFi and LTE, thereby network operators utilize this facility to offload mobile data traffic. Therefore, it is important to enable a network slicing to manage different RATs on the same logical network as a way to mitigate the spectrum scarcity problem and enables a slice to control its users' mobility across different access networks. In this paper, we propose a mobility management architecture based network slicing where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In this architecture, each slice has a different mobility demands and these demands are governed by a network slice configuration and service characteristics. Therefore, our mobility management architecture follows a modular approach where each slice has individual module to handle the mobility demands and enforce the slice policy for mobility management. The advantages of applying our proposed architecture include: i) Sharing network resources between different network slices; ii) creating logical platform to unify different RATs resources and allowing all slices to share them; iii) satisfying slice mobility demands.

Innovative technologies, such as Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), are enabling the possibility of managing network infrastructures and their services for enhancing network flexibility, efficient resource control, and cost-efficiency. How to combine these technologies requires taking into account the requirements established by the scenarios being executed, which can change at any time due to their dynamism. Network Slicing appeared to this end. This new technique tailors the needs of a particular scenario whose requirements are extremely heterogeneous and complex. By using Network Slicing, SDN/NFV-enabled environments can be strengthened to manage network resources and services depending on those requirements. Nevertheless, Network Slicing approaches lack management mechanisms that consider the heterogeneity and dynamism provided by scenarios based on future mobile networks. This article presents a formal definition of the Network Slicing information model, where all elements interacting with the Network Slices life cycle are formally represented. Another important contribution is a mobility-aware architecture that combines NFV/SDN techniques and introduces innovative components responsible for managing and orchestrating the Network Slices. Finally, a systematic comparison is made between the existing architectures and the proposed one.

The fifth generation (5G) networks are expected to support diverse business verticals (i.e., manufacturing, health care, etc.) with varying quality of service requirements. While today’s mobile networks are a one size fits all architecture, tomorrow’s 5G mobile networks are envisioned to encourage agility, programmability and elasticity through enabling a software-based architecture promoted by network slicing. Network slicing is a new paradigm consisting of partitioning the underlying network infrastructure into different logical network slices, each dedicated to address the requirements (i.e., ultra-low latency, ultra-reliability, etc.) of a group of services. Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies have been identified as main enablers of network slicing, facilitating the fulfillment of the aforementioned services’ requirements. In this paper, we study the Latency-Aware service scheduling (LASS) problem to solve the network function mapping, the traffic routing and the network service scheduling in the context of an ultra-low latency network slice to consider services with stringent deadlines. We propose the LASS-Game, a novel game-theoretic approach presenting a scalable solution for the LASS problem that accounts for the centralized aspect of the problem while leveraging a decentralized mapping, routing and scheduling decisions.

Network slicing is a key technology in fifth-generation (5G) mobile networks. Slicing divides a physical network into multiple dedicated logical networks to meet the requirements of diverse use cases. Efficient slice deployment algorithms are critical in reducing network operators' costs and energy consumption and in providing users better service. Many researchers have focused on static deployment when investigating network slices, effectively ignoring network operators' requirements for the dynamic deployment and expansion of such slices. In this paper, we first construct a joint optimization problem of cost and energy consumption. Then, we propose a prediction-assisted adaptive network slice expansion algorithm to deploy network slices dynamically. The proposed algorithm consists of three parts. First, we devise a Holt-Winters (HW) prediction algorithm to determine traffic demand for network slices. This method is intended to avoid frequent changes in network topology. Second, we propose a virtual network function (VNF) adaptive scaling strategy to reasonably determine the number of VNFs and resources required for network slices to avoid resource wastage. Finally, we develop a proactive online algorithm to deploy network slices. This method deploys network slices reasonably via the VNF deployment algorithm and link-routing algorithm to ensure slices' service requirements. Resource capacity and delay requirements are also considered in our evaluation to ensure that network costs and energy consumption are minimized. We then perform a series of simulation experiments to compare the proposed method's performance to state-of-the-art dynamic network slicing technologies. Ultimately, our solution is deemed a suitable candidate for dynamic deployment of 5G network slices; the solution demonstrates advantages of high resource utilization, low deployment costs, and low energy consumption.

The discovery process of OPC Unified Architecture (OPC-UA) is well defined but lacks flexibility when implemented in large networks. While the standard provides concepts for local and global discovery services, the core assumption is that all clients and servers are aware of the discovery server(s) in the network. This dependency is a barrier for Plug-and-Play installations and is overcome by self announces via the multicast Domain Name System (mDNS), however, this scales badly with an increasing number of network nodes and deployed servers. We propose a discovery approach based on Software-Defined Networking (SDN) and Network Function Virtualization (NFV), that installs a plug-and-play mechanism for global OPC-UA discovery in modern virtualized networks.

Within the dynamic landscape of fifth-generation (5G) and emerging sixth-generation (6G) wireless networks, the adoption of network slicing has revolutionized telecommunications by enabling flexible and efficient resource allocation. However, this advancement introduces new security challenges, as traditional protection mechanisms struggle to address the dynamic and complex nature of sliced network environments. This study proposes a Hybrid Security Framework Using Cross-Layer Integration, combining Software-Defined Networking (SDN), Network Function Virtualization (NFV), and AI-driven anomaly detection to strengthen network defenses. By integrating security mechanisms across multiple layers, the framework effectively mitigates threats, ensuring the integrity and confidentiality of network slices. An implementation was developed, focusing on the AI-based detection process using a representative 5G security dataset. The results demonstrate promising detection accuracy and real-time response capabilities. While full SDN/NFV integration remains under development, these findings lay the groundwork for scalable, intelligent security architectures tailored to the evolving needs of next-generation networks.

Network slicing is an emerging technology that is anticipated to realize the diverse QoS requirements required by the next generation (5G) networks. As key enablers of network slicing, software-defined network (SDN) and network function virtualization (NFV) has been fully researched and integrated into 5G architecture. In this paper, we propose a NFV MANO based network slicing framework for 5G networks. The proposed framework is a three-layered architecture using the MANO to control, manage and coordinate the slices' behavior.

Network Slicing is significant in facilitating a multiple of Fifth-generation (5G) applications, use cases, and services. It provides end-to-end isolation among the slices for customizing each slice in terms of service demands. The increasing demands of 5G networks and advanced features to meet complex business needs have rendered conventional approaches insufficient. Therefore, an efficient approach is required to mitigate the issues in the slicing process. As a result, Exponential Pine Cone Optimization enabled Quantum-Inspired Convolutional Neural Network (EPCO_QuCNet) is introduced for secure network slicing in 5G network. The system model of 5G network is carried out. When network slicing receives requests from a User Equipment (UE), various network parameters, like speed, packet loss rate, jitter, and packet delay budget—are gathered from multiple devices. Hence, a secure slicing of network is classified by Quantum-Inspired Convolutional Neural Networks (QuCNet), which is trained by Exponential Pine Cone Optimization (EPCO) algorithm. Furthermore, the services provided in 5G is accessed by the internet service provider using Virtual Network Function (VNF). EPCO_QuCNet has achieved better outcomes for the evaluation metrics, like acceptance rate, execution time and resource efficiency of 0.927, 0.167 sec and 0.911

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones.

Network slicing is one of the most promising enabling technologies for next generation mobile network (5G), which spans over all domains of the network, including data centers, core networks (CNs), transport networks, and radio access networks (RANs). As such wide range of technologies are involved, it is difficult to ensure an end-to-end (network) slice working stably all the time. First, for both CN and RAN slices, bugs may accidentally occur in virtual network functions(VNFs), which can invalidate the slice and trigger a recovery process. In addition, the traffic demands to be accommodated by a CN or RAN slice can be stochastic rather than deterministic parameters, and changes of traffic demands that are too drastic may trigger slice reconfiguration. In this chapter, we investigate a robust RAN slicing mechanism to address slice recovery and reconfiguration in a unified framework. A slice is constituted by a set of VNFs and links and remapping is a process of re-selecting VNFs and links to recover failures. As a baseline, we first formulate a recovery problem to solve remapping of a slice with deterministic traffic demands. Based on that, we propose two robust RAN slicing algorithms for slice recovery under stochastic demands. Numerical results reveal that the proposed robust RAN slicing algorithms can provide adjustable tolerance of traffic uncertainties, compared with the baseline algorithm. In addition, the tradeoff between the robustness of slices and the average load of links and the tradeoff between the robustness and the average recovery time of slices can be efficiently managed and controlled.