Network Slicing Research Articles

Feature selection based machine learning models for 5G network slicing approximation

The development of 5G networks is expected to introduce novel concepts and challenges in domains such as technology, security, and customer demands due to advancements in mobile internet and communication services. The three main services that the 5G network provides are enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). Network slicing is the practice of dividing a single physical network into many virtual networks in order to facilitate the delivery of multiple services across that network. These slices improve the network’s reliability and make it possible to provide more customized services. This article provides an overview of 5G network slicing, discussing its layers and overall architecture. Additionally, the paper proposes a machine learning network slicing model based on feature selection, comprising three main components. Firstly, data collection involves gathering two different datasets from various sources. Secondly, feature selection algorithms are applied to choose the most relevant set of features, as the collected datasets may contain numerous unnecessary attributes and values. Lastly, various classification models are utilized on the selected features to predict the optimal network slice, thereby improving the efficiency of the 5G network. The analytical results and simulation models demonstrate that the proposed machine learning models with feature selection perform exceptionally well and accurately predict 5G network slices.

Artificial algae optimization for Virtual Network Embedding problems in 5G network slicing scenarios

Network slicing enables diverse scenarios within The Fifth Generation of Cellular System (5G) and beyond systems. Mapping virtual networks to their physical counterparts, known as the Virtual Network Embedding (VNE) problem, is challenging with mathematical complexities. While Linear Programming (LP) approaches can address the VNE problem, they may prove impractical due to the execution time required to obtain optimal solutions. Furthermore, meta-heuristic algorithms offer a promising alternative in balancing solution optimality and execution time. In this study, we propose the Artificial Algae Algorithm (AAA) as a solution for the VNE problem in network slicing scenarios specific to 5G. We compare the performance of AAA with other commonly used meta-heuristics, including Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Since the evolutionary process of AAA consistently operates in three random dimensions, irrespective of the problem’s dimensionality, the runtime performance of AAA remains unaffected by the number of virtual nodes, yielding execution times up to ten times faster than DE and PSO when considering 30 nodes. In that scenario, the proposed approach with AAA presented an improvement of more than 60% compared to DE and PSO in an execution time ten times lower.

On the feasibility of using an autonomic control loop and automated planning for tenant-oriented reconfiguration of network slices

In 5G, 6G and beyond networks, tenants need capabilities to reconfigure network slices since they must react quickly to changing network conditions and end-users’ demands to meet Service Level Agreements. Offering reconfiguration operations to tenants is challenging since those functions must be autonomous and governed by high-level network management policies. Existing approaches face the network slice reconfiguration problem from the provider perspective, disregarding the importance of providing slice reconfiguration capabilities to tenants. This paper presents an approach called ATRAP to demonstrate the feasibility of using an autonomic control loop and automated planning for reconfiguring network slices from the tenant perspective. ATRAP introduces a tenant-oriented architecture based on the monitor-analyze-plan-execute-knowledge method and automated planning for computing reconfiguration plans autonomously. An ATRAP’s plan aims to turn a network from a source configuration where one or more intents representing high-level network management policies are unmet into a target configuration in which the network meets the intents again. As ATRAP represents a network slice as a Service Function Chain comprising Virtual Network Functions (VNFs) and virtual links connecting them, a reconfiguration plan involves tasks to migrate VNFs and virtual links into the substrate network in a particular order. Results revealed as ATRAP computes plans with few migrations of VNFs and their adjacent links, leading to low reconfiguration costs related to short service disruptions. Also, results showed as ATRAP’s planning time depends on the size of the substrate network and the tenant’s slices. The results evidence that ATRAP is an attractive and feasible solution for the in-tenant reconfiguration of network slices.

Big Data Network Optimization for Mobile Cellular Networks in 5G

5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing.

Highly Flexible and Traffic Isolating RAN Slicing: A Consumer IoT-Based Use Case

Future networks are envisaged to support an increasingly wide range of diverse services, with distinct service requirements. Network slicing presents an economically sustainable solution to properly handle these stringent requirements, on top of a common physical infrastructure. The support of network slicing at different ran resource management tasks is particularly challenging, as it involves the dynamic split of the scarce radio resources among the slices, aiming to achieve optimal resource share with traffic isolation of slices. To tackle those challenges, we propose an efficient low-complexity dynamic ran slicing scheme supported jointly at the ac and the ps levels. The scheme relies on a fair ac module to regulate the admission of users from the slices according to a penalty expression considering the expected sla with the contracted tenants’ priority. We also develop a ps algorithm based on the tunable slices’ weight, to efficiently fulfill kpi of services. Consumer Internet-of-Things (CIoT)-based healthcare services are considered a use case, effectively representing multi-service network slicing applications. Performance evaluation based on extensive simulations shows that the proposed scheme outperforms the referenced baseline schemes, achieving optimal trade-off towards flexible, fair, and efficient resource sharing with sufficient traffic isolation of slices.

DRIVE: Dynamic Resource Introspection and VNF Embedding for 5G Using Machine Learning

Network Slicing (NS) technique is comprehensively reshaping the next-generation communication networks (e.g. 5G, 6G). Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) predominantly control the flow of service functions on NS to incorporate versatile applications as per user demands. In the virtualized-Software Defined Networking vSDN environment, a chain of well-defined virtual network functions (VNFs) are installed on Service Function Chains (SFCs) by multiple Internet Service Providers (ISPs) concurrently. Generation, allocation, re-allocation, release and destroying associative VNFs on SFC is an extremely difficult task while keeping high selection accuracy. Towards solving this fundamental issue, in this work, we have proposed a multi-layered SFC formation for adaptive VNF allocation on dynamic slices. We have formulated an ILP to address the VNF-EAP (VNF-Embedding and Allocation Problem) over real network topology (AT&T Topology). Leveraging machine learning techniques we have shown an intelligent VNF selection mechanism to optimize resource utilization. The performance evaluation shows remarkable efficiency on ML-driven dynamic VNF selections over static allocations on SFCs by halving resource usage. Further, we have also studied a VNF typecasting technique for service backup on outage slices in the field of disaster management activities.

Maximizing the Connectivity of Network Slicing Enabled Internet of Vehicle With Differentiated Services

The advent of 5G opens up hitherto unimagined opportunities for delivering the much-anticipated Internet of Vehicles (IoV). There are two typical types of services in IoV, i.e., safety service and non-safety service. However, the existing IoV framework cannot efficiently support the differentiated IoV services. In addition, maximizing the number of accessed vehicles/users is another critical issue in IoV, especially in the dense urban area. To address these problems, in this paper, we utilize the emerging network slicing technology to support different services and employ Non-Orthogonal Multiple Access technology (NOMA) to help increase the connectivity. In particular, for the safety service, we take advantage of the finite blocklength capacity to correctly record the delay. Our goal is to maximize the connectivity of users by jointly considering the user association and their beamforming vectors, under the restrictions of limited physical resource. The problem is formulated as a Mixed-Integer Nonlinear Programming problem (MINLP). To tackle the intractable MINLP, we propose a two-stage scheme. Firstly, we exploit efficient approaches to solve the beamforming problem, i.e., successive convex approximation, semidefinite relaxation and second-order cone programming. Secondly, we propose a low-complexity Greedy User Association (GUA) algorithm to solve the user association problem. Finally, comprehensive simulations verify that our proposed GUA algorithm is close to global optimal solution and outperforms the benchmark schemes.

Assessment of Security KPIs for 5G Network Slices for Special Groups of Subscribers

It is clear that 5G networks have already become integral to our present. However, a significant issue lies in the fact that current 5G communication systems are incapable of fully ensuring the required quality of service and the security of transmitted data, especially in government networks that operate in the context of the Internet of Things, hostilities, hybrid warfare, and cyberwarfare. The use of 5G extends to critical infrastructure operators and special users such as law enforcement, governments, and the military. Adapting modern cellular networks to meet the specific needs of these special users is not only feasible but also necessary. In doing so, these networks must meet additional stringent requirements for reliability, performance, and, most importantly, data security. This scientific paper is dedicated to addressing the challenges associated with ensuring cybersecurity in this context. To effectively improve or ensure a sufficient level of cybersecurity, it is essential to measure the primary indicators of the effectiveness of the security system. At the moment, there are no comprehensive lists of these key indicators that require priority monitoring. Therefore, this article first analyzed the existing similar indicators and presented a list of them, which will make it possible to continuously monitor the state of cybersecurity systems of 5G cellular networks with the aim of using them for groups of special users. Based on this list of cybersecurity KPIs, as a result, this article presents a model to identify and evaluate these indicators. To develop this model, we comprehensively analyzed potential groups of performance indicators, selected the most relevant ones, and introduced a mathematical framework for their quantitative assessment. Furthermore, as part of our research efforts, we proposed enhancements to the core of the 4G/5G network. These enhancements enable data collection and statistical analysis through specialized sensors and existing servers, contributing to improved cybersecurity within these networks. Thus, the approach proposed in the article opens up an opportunity for continuous monitoring and, accordingly, improving the performance indicators of cybersecurity systems, which in turn makes it possible to use them for the maintenance of critical infrastructure and other users whose service presents increased requirements for cybersecurity systems.

The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges

The emergence of the sixth generation of cellular systems (6G) signals a transformative era and ecosystem for mobile communications, driven by demands from technologies like the internet of everything (IoE), V2X communications, and factory automation. To support this connectivity, mission-critical applications are emerging with challenging network requirements. The primary goals of 6G include providing sophisticated and high-quality services, extremely reliable and further-enhanced mobile broadband (feMBB), low-latency communication (ERLLC), long-distance and high-mobility communications (LDHMC), ultra-massive machine-type communications (umMTC), extremely low-power communications (ELPC), holographic communications, and quality of experience (QoE), grounded in incorporating massive broad-bandwidth machine-type (mBBMT), mobile broad-bandwidth and low-latency (MBBLL), and massive low-latency machine-type (mLLMT) communications. In attaining its objectives, 6G faces challenges that demand inventive solutions, incorporating AI, softwarization, cloudification, virtualization, and slicing features. Technologies like network function virtualization (NFV), network slicing, and software-defined networking (SDN) play pivotal roles in this integration, which facilitates efficient resource utilization, responsive service provisioning, expanded coverage, enhanced network reliability, increased capacity, densification, heightened availability, safety, security, and reduced energy consumption. It presents innovative network infrastructure concepts, such as resource-as-a-service (RaaS) and infrastructure-as-a-service (IaaS), featuring management and service orchestration mechanisms. This includes nomadic networks, AI-aware networking strategies, and dynamic management of diverse network resources. This paper provides an in-depth survey of the wireless evolution leading to 6G networks, addressing future issues and challenges associated with 6G technology to support V2X environments considering presenting +challenges in architecture, spectrum, air interface, reliability, availability, density, flexibility, mobility, and security.

Cloud native Lightweight Slice Orchestration (CLiSO) framework

Cloud-native network functions are becoming promising for 5G and beyond networks. They provide the much needed agility and flexibility that was missing from virtual machines. Though, this paradigm shift of using cloud-native application design principles, containerization, microservices, high resilience, and on-demand scaling has created challenges for legacy orchestration systems. They were designed for handling virtual machine-based network functions. Indeed, network slice orchestration requires interaction with multiple technological domain orchestrators, access, transport, core network, and edge computing. The specifications and existing orchestrators are made on top of the legacy virtual machine based network function orchestration. Hence, this limitation constrains their approach to managing a cloud-native network function. To overcome their challenges, we propose a novel Cloud-native Lightweight Slice Orchestration (CLiSO) framework extending our previously proposed Lightweight edge Slice Orchestration (LeSO) framework. In addition, we present a technology-agnostic and deployment-oriented network slice template. To allow zero-touch management of network slices, our framework provides a concept of Domain Specific Handlers. The framework has been thoroughly evaluated via orchestrating OpenAirInterface container network functions on public and private cloud platforms.

The application of advanced computational algorithms used for cooperative communication transmission of vehicular networks: a proposed method

Telematics will be one of the critical technologies in the future intelligent transportation system and establish communication between vehicles and vehicles, vehicles and networks, and vehicles and people. Thus, vehicles can sense mobile environments and make rational driving decisions. Therefore, the safety and efficiency of traffic flow would be enhanced. However, due to the unknown nature and higher complexity of the connected network environments of vehicles, the utilization of conventional optimization theory fails to generate satisfying results. To address the problem, this article proposes a methodology for collaborative transmission for communication regarding the Internet of Vehicles (IoV) with the help of advanced computational algorithms. The article employs a multi-intelligence advanced computational algorithm to construct a collaborative communication transmission mechanism in the telematics communication system model. The proposed algorithm fully considers the vehicle mobility and quality-of-service (QoS) of telematics services within the network slice. It adjusts the slice’s radio resource allocation and parameter settings on an expanded time scale to improve the QoS of telematics services and increase the system’s long-term revenue. The simulation results show that the proposed algorithm has a more significant performance improvement than conventional algorithms using telematics information transmission. For example, when the same load conditions are under consideration, the total capacity of the vehicle-to-infrastructure (V2I) link optimized by the proposed algorithm is still higher than that of the other three baseline strategies.

A hybrid analytical concept to QoE index evaluation: Enhancing eMBB service detection in 5G SA networks

The launch of commercial 5G networks has unlocked numerous opportunities for heavy data users and high-speed applications. The expected requirements for enhanced mobile broadband (eMBB) are pushing end-users to adopt 5G optimistically. Though already deployed 5G networks have shown high data rates and very low latency, the service-based experience and application behavior have been challenging to monitor. The legacy quality of experience (QoE) and service (QoS) monitoring and evaluation techniques have shown limitations in 5G standalone networks. The current 5G deployment large amount of user plane traffic generated by end-users makes the legacy-monitoring task very costly for mobile network operators (MNOs). And the complexity of the projected future 5G architecture, including advanced technologies such as network functions virtualization (NFV), software-defined networking (SDN), and network slicing, makes traditional service detection and QoE assessment ineffective. In this paper, we discuss a cost-effective hybrid analytical approach to eMBB service detection, analysis, and perceived user QoE measurement from raw traffic in a live 5G standalone (SA) network. We first use flow-level-based packet inspection and machine learning to detect and classify eMBB services from raw traffic. We then use a statistical approach to compute the user quality index (UQI). The concept is tested on traffic captured on a fixed 5G SA network. And the output enabled the MNO to have a 5G QoE assessment structure and awareness to adjust network traffic policies.

Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities

With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

Spatial–temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks

Balancing network traffic among base stations poses a primary challenge for mobile operators because of the escalating demand for enhanced data speeds in large-scale 5G radio applications. Within cellular networks, traffic flow prediction constitutes a pivotal issue in numerous applications, such as resource allocation, load balancing, and network slicing. In this paper, traffic prediction based load balancing framework with reinforcement learning is proposed to optimize neighbor cell relational parameters that can better balance traffic within a defined geographical cluster. Spatial–temporal-event cross attention graph convolution neural network (STECA-GCN) is put forward to predict the precise traffic flow. The model takes event dimension features into account, while also incorporating direct cross-fusion among diverse features. Concurrently, we have developed a strategy based on deep reinforcement learning to facilitate dynamic load balancing decisions. Simulation results show that our proposed load balancing framework can improve overall system performance. In particular, the combination of loading rate and energy efficiency can achieve a 12% improvement. The load balancing of the base station can better deal with social emergencies.

An intelligent technique for network resource management and analysis of 5G-IoT smart healthcare application

<p>The transformation of traditional hospitals to a smart healthcare system is a patient-centric, reliable, and smarter way to enhance healthcare facilities for individuals. These healthcare devices are based on the Internet of Things (IoT) and conventional network structures such as 3rd generation (3G) or 4th generation (4G). That did not provide the patient health data in the real-time scenario. Therefore, the integrated 5G-IoT system offers distinct benefits such as remote monitoring, surgery, and real-time data analysis of healthcare system that is operated with slice of dedicated network. It generates huge data with the billions of healthcare equipment that can be evaluated for decision making. This enormous data requires low latency, high-security, capacity, and more reliable techniques for evaluation. In this scenario, network slicing is one of the key solutions that can be considered for isolated end-to-end network structures. Network slicing enables logical and independent network virtual networks to be multiplexed for the same physical network. In this article, we propose an intelligent approach for network resource optimization and analysis of a 5G-IoT based smart healthcare network. We examine the performance of the machine learning algorithm by an automatic model, named A Fast Library for automated machine learning (FLAML). In addition, we perform network slicing to obtain resource optimization in 5G-IoT-based healthcare application.<strong></strong></p>

Energy Efficient Resource Allocation in 5G RAN Slicing with Grey Wolf Optimization

The massive connections and the real time control applications have different requirement on delay, energy, rate and reliability of the system. In order to meet the diversified 5G requirements, network slicing technique guarantees on the wide scale applications. In this paper, we have proposed a dynamic resource allocation system with two time scale. The one time scale is used for the resource allocation in the system and the other is used for optimized use of latency and power. Lyapunov drift function is used for the balance between the power consumption and the user satisfaction. Further, Grey Wolf Optimization (GWO) is used for the resource allocation strategy so as to gain the reliability of the system with heterogeneous requirements. The proposed methodology shows the improvement of 27% in user satisfaction and 17.5% in power consumption. The proposed framework can be utilized for the rate as well as latency sensitive applications in 5G.

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions. Considering the rapidly emerging new machine learning techniques, such as graph learning, federated learning, and transfer learning, a timely survey is needed to provide an overview of resource management and network slicing techniques of AI-enabled wireless networks. This article provides such a survey along with an application of knowledge transfer in radio access network (RAN) slicing. In particular, we first provide some background on resource management and network slicing, and review relevant state-of-the-art AI and machine learning (ML) techniques and their applications. Then, we introduce our AI-enabled knowledge transfer and reuse-based resource management (AKRM) scheme, where we apply transfer learning to improve system performance. Compared with most existing works, which focus on the training of standalone agents from scratch, the main difference of AKRM lies in its knowledge transfer and reuse capability between different tasks. Our paper aims to be a roadmap for researchers to use knowledge transfer schemes in AI-enabled wireless networks, and we provide a case study over the resource allocation problem in RAN slicing.

Consortium Blockchain-Based Spectrum Trading for Network Slicing in 5G RAN: A Multi-Agent Deep Reinforcement Learning Approach

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.

Digital-Twin-Assisted Resource Allocation for Network Slicing in Industry 4.0 and Beyond Using Distributed Deep Reinforcement Learning

Personalization is one of the primary emerging trends in Industry 4.0 and Beyond. Highly personalized services will present a significant challenge to the existing algorithms for Network Slicing (NS) and resource allocation, leading to issues such as nonequilibratory resource allocation in which some services are sacrificed for the maximum total reward of the algorithm, excessive cost and slow algorithm convergence. Digital Twin Network (DTN) is offered as a novel solution to the challenges listed above. By integrating the DTN and IIoT network slicing, we propose a Digital Twin Network Assisted Industry Internet of Things Network Slicing (DTN-IIoT NS) Architecture for Personalized IIoT services in Industry 4.0 and Beyond. The DTN-IIoT NS Architecture consists of three layers, three modules and two closed loops. On the basis of the aforementioned architecture, we focus on the resource allocation process in DTN-IIoT NS, model the DT-assisted resource allocation for highly personalized IIoT services, propose the service equilibrium rate, and formulate the optimization problem aiming at maximizing the equilibrium rate weighted net profit of network providers. Then, we propose a Dual-Channel Weighted (DCW) Critic network for service equilibrium in DTN-IIoT NS resource allocation and the matching Improved Prioritized Experience Replay (PER) to enhance convergent speed. In addition, we present a distributed DT-assisted DCW-PER Multiagent Deep Deterministic Policy Gradient (PER-DCW MADDPG) algorithm for the resource allocation process in DTN-IIoT NS. Simulation results indicate that the PER-DCW MADDPG algorithm can produce a better service equilibrium and accelerate the convergence speed of the algorithm.

Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services

Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services