5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues

The Internet of Things (IoT) is a new technology that improves the quality of people's lives by dominating a wide range of applications and service sectors. Fifth-generation (5G) wireless networks will soon be able to link this IoT ecosystem. It has been thoughtfully created to support the IoT industry's explosive development. One of the essential technologies in the 5G architecture, network slicing allows for dividing the physical network into several logical networks (i.e., slices) with various network properties. As a result, network slicing is a crucial driver of IoT in 5G. Through dedicated slices, network slicing can meet the different networking needs of heterogeneous IoT applications. We thoroughly review network slicing's use in IoT realization in this study. We cover network slicing in various IoT application scenarios and the technical problems that can be resolved using network slicing. This paper also discusses open research issues and integration challenges linked to network slicing in the IoT ecosystem.

Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.

Network slicing is a fundamental feature of 5G systems that facilitates the provision of particular system behaviours adapted to specific service/application domains on top of a common network infrastructure. A network slice is in general composed by a core network slice and a Radio Access Network (RAN) slice. The realization of RAN slices is particularly challenging because it requires configuring and operating traffic differentiation and protection mechanisms to simultaneously deliver multiple and diverse RAN behaviors over a given pool of radio resources. In this context, this paper proposes to characterize the behavior of a RAN slice through the specification of a set of control parameters that are used to dictate the operation of the packet scheduling function at Layer 2 and the radio admission control function at Layer 3. An evaluation of the suitability of these parameters for achieving efficient radio resource sharing and isolation between RAN slices is presented when configuring a network for supporting a slice with multiple enhanced Mobile BroadBand services and another slice for providing Mission Critical services. The analysis reveals the different impact of the Layer 3 and Layer 2 parameters for isolating services of different slices depending on whether they require guaranteed or non-guaranteed bit rates.

ABSTRACT- The rise of the internet of things (IoT) and autonomous systems has made connecting vehicles more critical. Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications. With the advent of 5G networks, vehicle-to-everything (V2X) networks are expected to be highly intelligent, reside on superfast, reliable, and low-latency connections. Network slicing, Machine Learning (ML), and Deep Learning (DL) are related to network automation and optimization in V2X communication. Machine Learning and Deep Learning (ML/DL) with network slicing aims to optimize the performance, reliability of the V2X network, personalized services, reduced costs, and scalability and enhance the overall driving experience. These advantages can ultimately lead to a safer and more efficient transportation system. However, existing Long-Term Evolution (LTE) systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels. Machine learning algorithms mitigate complexity levels, which can be highly instrumental in such vehicular communication systems. This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing, a different configuration of slicing, the requirements of slicing, and the ML algorithm used to control and manage to slice. This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases, focusing on V2X network slicing and considering efficient control and management. The enabler technologies are considered in light of the network requirements, particular configurations, and the underlying methods and algorithms, with a review of some critical challenges and possible solutions available. The paper concludes with a future roadmap by discussing some open research issues and future directions.

The 5th generation of mobile communications is expected to provide a network suitable for any service requirements using a technology termed network slicing. In network slicing, network resources are divided and are allocated to slices for services. Radio access network slicing is essential for end-to-end network slicing. The state of the radio access network changes from moment to moment and automatic control of network slicing is necessary to respond to service requirements in real time. Automatic control is applied using deep reinforcement learning that can learn optimal control by repeating trial and error. In this paper, we propose a method to allocate radio resources that satisfy the service requirements regardless of the number of slices using deep reinforcement learning. The proposed method flexibly controls multiple slices by observing the state of one slice and calling a model that controls one slice multiple times. The proposed method was evaluated by simulation using a scenario in which the number of slices changes with time. The results of the evaluation show that variations in slice numbers achieve almost completely satisfying of slice requirements without being affected by other slices.

Multi-access Edge Computing fundamentals, services, enablers and challenges: A complete survey

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their infrastructure. The MNOs should be able to offer network slices as a service and provide customisable and independent virtual networks to verticals. The slicing of an end-to-end (E2E) mobile network is divided into Core Network (CN) slicing, and Radio Access Network (RAN) slicing. In this paper, we assess the requirements for using radio hypervisors to enable RAN as a Service (RANaaS). We evaluate the current state-of-the-art on radio virtualisation with respect to these requirements and identify the missing features. Then, we present the eXtensible Virtualisation Layer (XVL), a software layer that provides the missing functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the delay to provision virtual radios, the delay introduced to forward IQ samples, and the computational overhead. Our results show that XVL enables leveraging existing radio hypervisors to support RANaaS.

Network slicing in 5G and beyond networks allows the network to be customized for each application or service by chaining together different virtualized network functions (VNFs) according to service requirements. The increased flexibility offered by network slicing comes at the cost of complexity in management and orchestration, which cannot be solved by traditional reactive human-in-the-loop solutions. This necessitates minimizing human intervention through the use of artificial intelligence techniques (zero touch network management). In particular, the scaling and placement of the chain of VNFs that constitute a network slice is a complex combinatorial optimization problem that is difficult to solve effectively with traditional approaches. Driven by the benefits of deep reinforcement learning (DRL) in solving various combinatorial optimization problems, in this article, we survey various DRL-based approaches to slice scaling and placement, including different ways to model the problem and benefits of various DRL techniques in addressing specific aspects of the problem. Further, we highlight key challenges and open issues in the effective use of DRL for network slice scaling and placement.

In the upcoming fifth-generation (5G) ecosystem, the delivery of a variety of personalized services is envisioned. With the development of software-defined networks and network function virtualization technologies, networks display increasingly flexible features, such as programmability. Network slicing is a state-of-the-art technology that provides services tailored to the specific demands of users, such as smart grids and e-health applications. In this article, we introduce the network slicing concept and its application and discuss related work. In addition, we propose an architecture for network slicing by combining software-defined networks and network function virtualization technologies. Finally, we note important challenges and open issues in the development and application of these technologies.

Network slicing is a technology that will change the way in which future networks and services will be designed and operated. The approach impacts telco operators from the technical and business point of view. The technology is a subject of intensive research and standardization. The on-going work on network slicing has shown that many issues still have to be solved before the solution can be used by telco operators. Currently, the technology does not fulfill telco functional and performance-related requirements. In this paper, we describe the present status of network slicing and list the network slicing-related issues that still have to be solved. The list includes issues related to the scalability of slice management and orchestration, end-to-end slice orchestration, data plane and RAN slicing problems as well as business-oriented and legal issues.

Internet of Things and cloud computing, a huge repository of terabytes of data is generated each day from modern information systems and digital technologies. For decision making, analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Based on the challenges and open research issues, it opens a new horizon for researchers to develop the solution additionally. Keywords: Big data analytics; Hadoop; Massive data; Structured data; Unstructured Data

As one of key technologies of the fifth-generation (5G) communication system, network slicing can share the underlying infrastructure with different application requirements and ensure that the slices can be isolated from each other. This paper proposes an end-to-end (E2E) network slicing resource allocation algorithm based on Deep Q-Networks (DQN), which is suitable for multi-slice and multi-service scenarios. This algorithm jointly considers the radio access network slices and core network slices to dynamically allocate resources to maximize the number of access users. First we build such a model, which is a mixed integer programming problem and it needs to be dynamically adjusted according to the changes of environment. We propose to use DQN algorithm to solve this problem, which can perceive changes in the environment and make dynamic decisions. Under each decision, we need to calculate the reward value of DQN, so we divide the problem into the core side and the access side. Then the dynamic knapsack algorithm and the link mapping algorithm are used to obtain the reward. The simulation results show that the average access rate of DQN scheme is higher than 97%. Compared with the optimal allocation scheme of access side, the average access rate is increased by 9% for delay constrained slices and 5% for rate constrained slices in a dynamic environment.

Owing to their flexibility, fast deployment, increasing affordability, and numerous application opportunities, unmanned aerial vehicles (UAVs) became an undisputed target of studies and research to discover and exploit the immense opportunities they provide. Moreover, network slicing is a critical enabler for 5G and beyond. Network slices guarantee certain network user expectations such as data rate and latency thresh‐holds. There is no doubt that crowd‐sourced and incentive driven solutions are on the rise. Crowd‐sourcing allowed companies and organizations both small and large to gather data and perform other large‐scale tasks with minimum expenses and maintenance. Indeed, the major concerns for 5G and network slicing are guaranteeing high throughput, reliability, and latency. With data rates being the most important attribute for common regular users, that is, mobile devices, video streaming, navigation, and content consumption. As a result, the focus of this work is to utilize crowd‐sourced UAVs in an incentive driven network slice assistance system to extend coverage and provide mobile users with the high throughput they require. This work presents some basic 5G cellular radio access technologies and network slicing background and proposes a novel system, namely UAV slice assistance (UAVSA), and its hybrid alternative version: UAV hybrid slice assistance (UAVHSA). The hybrid mode combines benefits from the proposed UAVSA system and the traditional base station (BS) system. The proposed algorithms address the issue of coverage and guaranteeing high data rate slices for users. The proposed systems maximize satisfaction rate of network slice users. Moreover, all systems are extensively tested and compared in various scenarios to determine their performance and provide a coherent study. Finally, future works and opportunities are highlighted in conclusion.

The proliferation of mobile demands and increasingly multifarious services pose challenges to both the radio access networks (RANs) and the core network (CN). Unfortunately, the traditional networks can hardly meet the increasing requirements. Software-defined network (SDN) and network virtualization (NV) are considered as innovative paradigms to stress these problems. By extending SDN to wireless networks, software-defined wireless network (SDWN) consisting of both RANs and CN can be obtained. In this paper, the RANs and CN of SDWN are virtualized to virtual access points (VAPs) and network slices respectively based on NV technology. Considering the system scenario that a SDWN consists of overlapping RANs, an optimal joint VAP and network slice selection scheme is proposed. To characterize the complicated transmission performance of VAPs and network slices, the concepts of utility function and network calculus are introduced, and the joint utility of VAPs and network slices is formulated based on which the optimal VAP and network slice combination corresponding to the maximal joint utility can be selected. The simulation results demonstrate the efficiency of the proposed scheme.

The Fifth Generation of mobile networks is emerging as a key enabler for Ultra Reliable and Low Latency Communications. However, to effectively design and provide safety-critical applications through mobile systems, many research issues still need to be deeply investigated. The most important ones include: (1) the dynamic and flexible management of radio resources of a new Radio Access Network jointly used by many virtual mobile operators, (2) the optimized and real-time configuration of network slices, and (3) the harmonious integration of Multi-access Edge Computing services. Starting from the efficient methodologies and solutions available in the current state of the art, this position paper sheds some important basis for the design of a comprehensive architecture enabling Radio Access Network slicing for Ultra Reliable and Low Latency Communications, including design criteria, system components and their baseline interactions, and critical open issues to be investigated in future research initiatives.