Journal articles on the topic 'Virtual networks and slicing'
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1
Gomes, Rayner, Dario Vieira, and Miguel Franklin de Castro. "Application of Meta-Heuristics in 5G Network Slicing: A Systematic Review of the Literature." Sensors 22, no. 18 (September 6, 2022): 6724. http://dx.doi.org/10.3390/s22186724.
Abstract:
Network slicing is a vital component of the 5G system to support diverse network scenarios, creating virtual networks (slices) by mapping virtual network requests to real networks. The mapping is an arduous computing process, mathematically studied and known as the Virtual Network Embedding (VNE) problem, and its complexity is NP-Hard. The mapping process is oriented to respect the QoS demands from the virtual network requests and the available resources in the physical-substrate infrastructure. Meta-heuristic approaches are a suitable way to solve the VNE problems because of their capacity to escape from the local optimum and adapt the solution search to complex networks; these abilities are essential in 5G networks scenarios. This article presents a systematic review of meta-heuristics organized by application, development and problem-solving approaches to VNE. It also provides the standard parameters to model the infrastructure and virtual network requests to simulate network slicing as a service. Finally, our work proposes some future research based on the discovered gaps.
2
Lorincz, Josip, Amar Kukuruzović, and Zoran Blažević. "A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks." Sensors 24, no. 10 (May 20, 2024): 3242. http://dx.doi.org/10.3390/s24103242.
Abstract:
The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks’ energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks’ energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices’ energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.
3
Guijarro, Luis, Jose Vidal, and Vicent Pla. "Competition in Service Provision between Slice Operators in 5G Networks." Electronics 7, no. 11 (November 12, 2018): 315. http://dx.doi.org/10.3390/electronics7110315.
Abstract:
Network slicing is gaining an increasing importance as an effective way to introduce flexibility in the management of resources in 5G networks. We envision a scenario where a set of network operators outsource their respective networks to one Infrastructure Provider (InP), and use network slicing mechanisms to request the resources as needed for service provision. The InP is then responsible for the network operation and maintenance, while the network operators become Virtual Network Operators (VNOs). We model a setting where two VNOs compete for the users in terms of quality of service, by strategically distributing its share of the aggregated cells capacity managed by the InP among its subscribers. The results show that the rate is allocated among the subscribers at each cell in a way that mimics the overall share that each VNO is entitled to, and that this allocation is the Nash equilibrium of the strategic slicing game between the VNOs. We conclude that network sharing and slicing provide an attractive flexibility in the allocation of resources without the need to enforce a policy through the InP.
4
Murakami, Masaya, Daichi Kominami, Kenji Leibnitz, and Masayuki Murata. "Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks." Applied Sciences 9, no. 18 (September 11, 2019): 3809. http://dx.doi.org/10.3390/app9183809.
Abstract:
In realizing the network environment assumed by the Internet-of-Things, network slicing has drawn considerable attention as a way to enhance the utilization of physical networks (PNs). Meanwhile, slicing has been shown to cause interdependence among sliced virtual networks (VNs) by propagating traffic fluctuations from one network to others. However, for interconnected networks with mutual dependencies, known as a network of networks (NoN), finding a reliable design method that can cope with environmental changes is an important issue that is yet to be addressed. Some NoN models exist that describe the behavior of interdependent networks in complex systems, and previous studies have shown that an NoN model based on the functional networks of the brain can achieve high robustness, but its application to dynamic and practical systems is yet to be considered. Consequently, this paper proposes the Physical–Virtual NoN (PV-NoN) model assuming a network-slicing environment. This model defines an NoN availability state to deal with traffic fluctuations and interdependence among a PN and VNs. Further, we assume three basic types of interdependence among VNs for this model. Simulation experiments confirm that the one applying complementary interdependence inspired by brain functional networks achieves high availability and communication performance while preventing interference among the VNs. Also investigated is a method for designing a reliable network structure for the PV-NoN model. To this end, the deployment of network influencers (i.e., the most influential elements over the entire network) is configured from the perspective of intra/internetwork assortativity. Simulation experiments confirm that availability or communication performance is improved when each VN is formed assortatively or disassortatively, respectively. Regarding internetwork assortativity, both the availability and communication performance are improved when the influencers are deployed disassortatively among the VNs.
5
Richart, Matias, Javier Baliosian, Joan Serrat, and Juan-Luis Gorricho. "Resource Slicing in Virtual Wireless Networks: A Survey." IEEE Transactions on Network and Service Management 13, no. 3 (September 2016): 462–76. http://dx.doi.org/10.1109/tnsm.2016.2597295.
6
Jia, Qingmin, RenChao Xie, Tao Huang, Jiang Liu, and Yunjie Liu. "Caching Resource Sharing for Network Slicing in 5G Core Network." Journal of Organizational and End User Computing 31, no. 4 (October 2019): 1–18. http://dx.doi.org/10.4018/joeuc.2019100101.
Abstract:
Network slicing has been considered a promising technology in next generation mobile networks (5G), which can create virtual networks and provide customized service on demand. Most existing works on network slicing mainly focus on virtualization technology, and have not considered in-network caching well. However, in-network caching, as the one of the key technologies for information-centric networking (ICN), has been considered as a significant approach in 5G network to cope with the traffic explosion and network challenges. In this article, the authors jointly consider in-network caching combining with network slicing. They propose an efficient caching resource sharing scheme for network slicing in 5G core network, aiming at solving the problem of how to efficiently share the limited physical caching resource of Infrastructure Provider (InP) among multiple network slices. In addition, from the perspective of network slicing, the authors formulate caching resource sharing problem as a non-cooperative game, and propose an iteration algorithm based on caching resource updating to obtain the Nash Equilibrium solution. Simulation results show that the proposed algorithm has good convergence performance, and illustrate the effectiveness of the proposed scheme.
7
Delgado, Carmen, Maria Canales, Jorge Ortin, Jose Ramon Gallego, Alessandro Redondi, Sonda Bousnina, and Matteo Cesana. "Joint Application Admission Control and Network Slicing in Virtual Sensor Networks." IEEE Internet of Things Journal 5, no. 1 (February 2018): 28–43. http://dx.doi.org/10.1109/jiot.2017.2769446.
8
Kim, Yohan, Sunyong Kim, and Hyuk Lim. "Reinforcement Learning Based Resource Management for Network Slicing." Applied Sciences 9, no. 11 (June 9, 2019): 2361. http://dx.doi.org/10.3390/app9112361.
Abstract:
Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.
9
Makhija, Deven. "5G Communication Systems: Network Slicing and Virtual Private Network Architecture." ITM Web of Conferences 54 (2023): 02001. http://dx.doi.org/10.1051/itmconf/20235402001.
Abstract:
5G communication systems are being rolled out with multiple technological solutions and applications being fielded on existing as well as enhanced infrastructure. The utilization of 5G systems and infrastructure by verticals over different platforms as well as industries is achieved with slicing. Slicing in 5G provides guaranteed resources for end users of vertical industries and applications over varied platforms, architecture, and infrastructure. Standards for network slicing in 5G have been formulated by 3GPP and further specifications are being released. Implementation of slicing at various layers are being researched. This Paper reviews the advancements in development of specifications for Layer 2 implementation of slicing and communication systems using virtual Private Network and Virtual Transport Network and its architecture. The enhancements to communication systems using existing Multi-Protocol Label Switching (MPLS) and its exploitation based on slicing technology has been reviewed. The research challenges and way ahead on same have been discussed including end resource allocation.
10
Gao, Shujuan, Ruyan Lin, Yulong Fu, Hui Li, and Jin Cao. "Security Threats, Requirements and Recommendations on Creating 5G Network Slicing System: A Survey." Electronics 13, no. 10 (May 10, 2024): 1860. http://dx.doi.org/10.3390/electronics13101860.
Abstract:
Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.
11
Esmaeily, Ali, and Katina Kralevska. "Orchestrating Isolated Network Slices in 5G Networks." Electronics 13, no. 8 (April 18, 2024): 1548. http://dx.doi.org/10.3390/electronics13081548.
Abstract:
Sharing resources through network slicing in a physical infrastructure facilitates service delivery to various sectors and industries. Nevertheless, ensuring security of the slices remains a significant hurdle. In this paper, we investigate the utilization of State-of-the-Art (SoA) Virtual Private Network (VPN) solutions in 5G networks to enhance security and performance when isolating slices. We deploy and orchestrate cloud-native network functions to create multiple scenarios that emulate real-life cellular networks. We evaluate the performance of the WireGuard, IPSec, and OpenVPN solutions while ensuring confidentiality and data protection within 5G network slices. The proposed architecture provides secure communication tunnels and performance isolation. Evaluation results demonstrate that WireGuard provides slice isolation in the control and data planes with higher throughput for enhanced Mobile Broadband (eMBB) and lower latency for Ultra-Reliable Low-Latency Communications (URLLC) slices compared to IPSec and OpenVPN. Our developments show the potential of implementing WireGuard isolation, as a promising solution, for providing secure and efficient network slicing, which fulfills the 5G key performance indicator values.
12
Dangi, Ramraj, Akshay Jadhav, Gaurav Choudhary, Nicola Dragoni, Manas Kumar Mishra, and Praveen Lalwani. "ML-Based 5G Network Slicing Security: A Comprehensive Survey." Future Internet 14, no. 4 (April 8, 2022): 116. http://dx.doi.org/10.3390/fi14040116.
Abstract:
Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.
13
Cunha, José, Pedro Ferreira, Eva M. Castro, Paula Cristina Oliveira, Maria João Nicolau, Iván Núñez, Xosé Ramon Sousa, and Carlos Serôdio. "Enhancing Network Slicing Security: Machine Learning, Software-Defined Networking, and Network Functions Virtualization-Driven Strategies." Future Internet 16, no. 7 (June 27, 2024): 226. http://dx.doi.org/10.3390/fi16070226.
Abstract:
The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI’s predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks.
14
Skondras, Emmanouil, Emmanouel T. Michailidis, Angelos Michalas, Dimitrios J. Vergados, Nikolaos I. Miridakis, and Dimitrios D. Vergados. "A Network Slicing Framework for UAV-Aided Vehicular Networks." Drones 5, no. 3 (July 30, 2021): 70. http://dx.doi.org/10.3390/drones5030070.
Abstract:
In a fifth generation (5G) vehicular network architecture, several point of access (PoA) types, including both road side units (RSUs) and aerial relay nodes (ARNs), can be leveraged to undertake the service of an increasing number of vehicular users. In such an architecture, the application of efficient resource allocation schemes is indispensable. In this direction, this paper describes a network slicing scheme for 5G vehicular networks that aims to optimize the performance of modern network services. The proposed architecture consists of ground RSUs and unmanned aerial vehicles (UAVs) acting as ARNs enabling the communication between ground vehicular nodes and providing additional communication resources. Both RSUs and ARNs implement the LTE vehicle-to-everything (LTE-V2X) technology, while the position of each ARN is optimized by applying a fuzzy multi-attribute decision-making (fuzzy MADM) technique. With regard to the proposed network architecture, each RSU maintains a local virtual resource pool (LVRP) which contains local RBs (LRBs) and shared RBs (SRBs), while an SDN controller maintains a virtual resource pool (VRP), where the SRBs of the RSUs are stored. In addition, each ARN maintains its own resource blocks (RBs). For users connected to the RSUs, if the remaining RBs of the current RSU can satisfy the predefined threshold value, the LRBs of the RSU are allocated to user services. On the contrary, if the remaining RBs of the current RSU cannot satisfy the threshold, extra RBs from the VRP are allocated to user services. Similarly, for users connected to ARNs, the satisfaction grade of each user service is monitored considering both the QoS and the signal-to-noise plus interference (SINR) factors. If the satisfaction grade is higher than the predefined threshold value, the service requirements can be satisfied by the remaining RBs of the ARN. On the contrary, if the estimated satisfaction grade is lower than the predefined threshold value, the ARN borrows extra RBs from the LVRP of the corresponding RSU to achieve the required satisfaction grade. Performance evaluation shows that the suggested method optimizes the resource allocation and improves the performance of the offered services in terms of throughput, packet transfer delay, jitter and packet loss ratio, since the use of ARNs that obtain optimal positions improves the channel conditions observed from each vehicular user.
15
Zong, Yue, Chuan Feng, Yingying Guan, Yejun Liu, and Lei Guo. "Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges." Sensors 20, no. 9 (May 6, 2020): 2655. http://dx.doi.org/10.3390/s20092655.
Abstract:
The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms.
16
Srinivasan, Thiruvenkadam, Sujitha Venkatapathy, Han-Gue Jo, and In-Ho Ra. "VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management." Journal of Sensor and Actuator Networks 12, no. 5 (September 13, 2023): 65. http://dx.doi.org/10.3390/jsan12050065.
Abstract:
Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.
17
Al-Yassari, Mohammed Mousa Rashid, and Nadia Adnan Shiltagh Al-Jamali. "Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment." Journal of Engineering 29, no. 6 (June 1, 2023): 87–97. http://dx.doi.org/10.31026/j.eng.2023.06.07.
Abstract:
The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.
18
Kannan, Yamini. "Network Slicing in 5G Systems: Challenges, Opportunities and Implementation Approaches." International Journal of Innovative Research in Information Security 10, no. 02 (February 24, 2024): 51–56. http://dx.doi.org/10.26562/ijiris.2023.v1002.02.
Abstract:
The introduction of the fifth-generation (5G) of network technology has radically transformed the telecommunications landscape by providing high-speed, low-latency communication suitable for a range of innovative applications. However, this transformation also introduces novel network complexity and resource management challenges. An emerging solution to these formidable challenges is 'Network Slicing,' a powerful technology that plays a crux role in the efficient management of 5G network systems. Network slicing, enabled by key technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), allows the creation of multiple virtual and independent networks operating on a shared physical infrastructure. This ability contributes to a more flexible, scalable, and efficient network system, making it aptly suited for diverse 5G applications. In this paper, we conduct an in-depth examination of network slicing in 5G systems, its implementation strategies, associated challenges, and potential solutions. Two real-world case studies underline its practical applications, while a discussion on the future outlook anticipates advances in AI and ML to refine network slicing management. The paper posits that while network slicing brings its own set of complexities, its continuous evolution and relentless innovations gear towards overcoming such challenges, paving the way to a future of 5G networking marked by versatility, reliability, and efficiency of unprecedented levels.
19
Li, Wenjing, Yueqi Zi, Lei Feng, Fanqing Zhou, Peng Yu, and Xuesong Qiu. "Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing." Applied Sciences 9, no. 4 (February 18, 2019): 701. http://dx.doi.org/10.3390/app9040701.
Abstract:
The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.
20
Tipantuña, Christian, and Xavier Hesselbach. "Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies." Energies 13, no. 15 (August 2, 2020): 3984. http://dx.doi.org/10.3390/en13153984.
Abstract:
Energy consumption is a critical issue for the communications network operators, impacting deeply the cost of the services, as well as the ecological footprint. Network slicing architecture for 5G mobile communications enables multiple independent virtual networks to be created on top of a common shared physical infrastructure. Each network slice needs different types of resources, including energy, to fulfill the demands requested by each application, operator, or vertical market. The existing literature on network slicing is mainly targeted at the partition of network resources; however, the corresponding management of energy consumption is an unconsidered critical concern. This paper analyzes the requirements for an energy-aware 5G network slicing provisioning according to the 3GPP specifications, proposes an architecture, and studies the strategies to provide efficient energy consumption in terms of renewable and non-renewable sources. NFV and SDN technologies are the essential enablers and leverage the Internet of Things (IoT) connectivity provided by 5G networks. This paper also presents the technical 5G technology documentation related to the proposal, the requirements for adaptive energy management, and the Integer Linear Programming (ILP) formulation of the energy management model. To validate the improvements, an exact optimal algorithmic solution is presented and some heuristic strategies.
21
Hamza, Hawraa S., and Mehdi Ebady Manaa. "A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network." Journal of Physics: Conference Series 2701, no. 1 (February 1, 2024): 012028. http://dx.doi.org/10.1088/1742-6596/2701/1/012028.
Abstract:
Abstract 5G is considered as a key contributor and infrastructure supplier in the communication technology industry, capable of supporting a wide range of services such as virtual reality, driverless automobiles, e-health, and a variety of intelligent applications. Network slicing is designed to support the diversity of services applications with increased performance and flexibility needs by dividing the physical network into many logical networks. Service classification allows 5G service providers to accurately select the network slices for each service. We propose a Network Slicing classifier that uses a Multi-level Deep learning Model. First, we created a dataset of 5G network slicing that contain attributes connected with various network services. Next, we performed a multi-level model that consist of a set of Machine learning and deep learning model (such as deep Neural Network, Random Forest and Decision Tree) as a first level followed by next level that which is represent Attentive Interpretable Tabular Learning model. The outcomes of the experiment showed that the proposed model was able to exceed the normal models with high performance results.
22
Tam, Prohim, Seyha Ros, Inseok Song, and Seokhoon Kim. "QoS-Driven Slicing Management for Vehicular Communications." Electronics 13, no. 2 (January 10, 2024): 314. http://dx.doi.org/10.3390/electronics13020314.
Abstract:
Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.
23
Odarchenko, Roman, Serhii Dakov, and Larisa Dakova. "RESEARCH OF CYBER SECURITY MECHANISMS IN MODERN 5G CELLULAR NETWORKS." Information systems and technologies security, no. 1 (5) (2021): 27–36. http://dx.doi.org/10.17721/ists.2021.1.25-34.
Abstract:
The main feature of the 5G network is Network slicing. This concept enables network resource efficiency, deployment flexibility, and support for rapid growth in over the top (OTT) applications and services. Network Slicing involves splitting the 5G physical architecture into multiple virtual networks or layers. Each network layer (slice) includes control layer functions, user traffic level functions, and a radio access network. Slice isolation is an important requirement that allows the basic concept of Network slicing to be applied to the simultaneous coexistence of multiple fragments in a single infrastructure. This property is achieved by the fact that the performance of each slice should not affect the performance of the other. The architecture of network fragments expands in two main aspects: slice protection (cyber attacks or malfunctions affect only the target slice and have a limited impact on the life cycle of other existing ones) and slice privacy (private information about each slice, such as user statistics) does not exchange between other slices). In 5G, the interaction of the user's equipment with the data networks is established using PDU sessions. Multiple PDU sessions can be active at the same time to connect to different networks. In this case, different sessions can be created using different network functions following the concept of Network Slicing. The concept of "network architecture", which is developed on hardware solutions, is losing its relevance. It will be more appropriate to call 5G a system, or a platform because it is implemented using software solutions. 5G functions are implemented in VNF virtual software functions running in the network virtualization infrastructure, which, in turn, is implemented in the physical infrastructure of data centers, based on standard commercial COTS equipment, which includes only three types of standard devices - server, switch and a storage system. For the correct operation of a network, it is necessary to provide constant monitoring of parameters which are described above. Monitoring is a specially organized, periodic observation of the state of objects, phenomena, processes for their assessment, control, or forecasting. The monitoring system collects and processes information that can be used to improve the work process, as well as to inform about the presence of deviations. There is a lot of network monitoring software available today, but given that 5G is implemented on virtual elements, it is advisable to use the System Center Operations Manager component to monitor network settings and performance and to resolve deviations on time. The Operations Manager reports which objects are out of order sends alerts when problems are detected and provides information to help determine the cause of the problem and possible solutions. So, for the 5G network, it is extremely important to constantly monitor its parameters for the timely elimination of deviations, as it can impair the performance and interaction of smart devices, as well as the quality of communication and services provided. System Center Operations Manager provides many opportunities for this. The purpose and objectives of the work. The work aims to analyze the main mechanisms of cybersecurity in 5G cellular networks
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Sacoto-Cabrera, Erwin J., Luis Guijarro, Jose R. Vidal, and Vicent Pla. "Economic feasibility of virtual operators in 5G via network slicing." Future Generation Computer Systems 109 (August 2020): 172–87. http://dx.doi.org/10.1016/j.future.2020.03.044.
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Saleh, Wardah, and Shahrin Chowdhury. "RANSlicing: Towards Multi-Tenancy in 5G Radio Access Networks." International Journal of Wireless & Mobile Networks 14, no. 2 (April 30, 2022): 43–51. http://dx.doi.org/10.5121/ijwmn.2022.14204.
Abstract:
A significant purpose of 5G networks is allowing sharing resources among different network tenants such as service providers and Mobile Virtual network Operators. Numerous domains are taken in account regarding resource sharing containing different infrastructure (storage, compute and networking), Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum, transport. Spectrum sharing and RAN are anticipated as the fundamental part in multi-tenant 5G network. Nevertheless, there is a shortage of evaluation platforms to determine the number of benefits that can be acquired from multilevel spectrum sharing rather than single-level spectrum sharing. The work presented in this paper intend to address this issue by presenting a modified SimuLTE model is used for evaluating active RAN based on multi-tenant 5G networks. The result shows an understanding into the actual advantages of RAN slicing for multi-tenants in 5G networks.
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Ngo, Duc-Thinh, Ons Aouedi, Kandaraj Piamrat, Thomas Hassan, and Philippe Raipin-Parvédy. "Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities." Future Internet 15, no. 12 (November 24, 2023): 377. http://dx.doi.org/10.3390/fi15120377.
Abstract:
As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks. This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, and opportunities. We discuss how GNNs and NDTs can be leveraged to improve network performance, optimize routing, enable network slicing, and enhance security in future networks. Additionally, we highlight certain advantages of incorporating GNNs into NDTs and present two case studies. Finally, we address the key challenges and promising directions in the field, aiming to inspire further advancements and foster innovation in GNN-based NDTs for future networks.
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Sunday Oladayo Oladejo, Stephen Obono Ekwe, and Lateef Adesola Akinyemi. "Multi-tier multi-tenant network slicing: A multi-domain games approach." ITU Journal on Future and Evolving Technologies 2, no. 6 (September 23, 2021): 57–82. http://dx.doi.org/10.52953/dxzq6155.
Abstract:
The 5G slice networks will play a critical role in meeting the stringent quality-of-service requirements of different use cases, reducing the Capital Expenditure (CapEX) and Operational Expenditure (OpEX) of mobile network operators. Owing to the flexibility and ability of 5G slice networks to meet the needs of different verticals, it attracts new network players and entities to the mobile network ecosystem, and therefore it creates new business models and structures. Motivated by this development, this paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from a multiplayer multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising Infrastructure Providers (InPs), Mobile Virtual Network Operators (MVNOs), Service Providers (SPs), and slice users are investigated. We propose two schemes: a multi-tier multi-domain slice user matching game scheme and a distributed backtracking multiplayer multi-domain game scheme in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a Genetic Algorithm (GA) Intelligent Latency-Aware Resource (GI-LARE) allocation scheme, and a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme significantly outperforms these other schemes.
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Adoga, Haruna Umar, and Dimitrios P. Pezaros. "Network Function Virtualization and Service Function Chaining Frameworks: A Comprehensive Review of Requirements, Objectives, Implementations, and Open Research Challenges." Future Internet 14, no. 2 (February 15, 2022): 59. http://dx.doi.org/10.3390/fi14020059.
Abstract:
Network slicing has become a fundamental property for next-generation networks, especially because an inherent part of 5G standardisation is the ability for service providers to migrate some or all of their network services to a virtual network infrastructure, thereby reducing both capital and operational costs. With network function virtualisation (NFV), network functions (NFs) such as firewalls, traffic load balancers, content filters, and intrusion detection systems (IDS) are either instantiated on virtual machines (VMs) or lightweight containers, often chained together to create a service function chain (SFC). In this work, we review the state-of-the-art NFV and SFC implementation frameworks and present a taxonomy of the current proposals. Our taxonomy comprises three major categories based on the primary objectives of each of the surveyed frameworks: (1) resource allocation and service orchestration, (2) performance tuning, and (3) resilience and fault recovery. We also identify some key open research challenges that require further exploration by the research community to achieve scalable, resilient, and high-performance NFV/SFC deployments in next-generation networks.
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Okello, Fred Otieno, Vitalice Oduol, Ciira Maina, and Antonio Apiyo. "Improvement of 5G Core Network Performance using Network Slicing and Deep Reinforcement Learning." International Journal of Electrical and Electronics Research 12, no. 2 (May 30, 2024): 493–502. http://dx.doi.org/10.37391/ijeer.120222.
Abstract:
Users have increasingly been having more use cases for the network while expecting the best Quality of Service (QoS) and Quality of Experience (QoE). The Fifth Generation of mobile telecommunications technology (5G) network had promised to satisfy most of the expectations and network slicing had been introduced in 5G to be able to satisfy various use cases. However, creating slices in a real-life environment with just the resources required while having optimized QoS has been a challenge. This has necessitated more intelligence to be required in the network and machine learning (ML) has been used recently to add the intelligence and ensure zero-touch automation. This research addresses the open question of creating slices to satisfy various use cases based on their QoS requirements, managing, and orchestrating them optimally with minimal resources while allowing the isolation of services by introducing a Deep reinforcement Machine Learning (DRL) algorithm. This research first evaluates the previous work done in improving QoS in the 5G core. 5G architecture is simulated by following the ETSI NFV MANO (European Telecommunications Standards Institute for Network Function Virtualization Management and Orchestration) framework and uses Open5G in 5G core, UERANISM for RAN, Openstack for Virtual Infrastructure Manager (VIM), and Tacker for Virtual Network Function Management and orchestration (VNFMO). The research simulates network slicing at the User Plane Function (UPF) level and evaluates how it has improved QoS. The network slicing function is automated by following ETSI Closed Loop Architecture and using Deep Reinforcement Learning (DRL) by modeling the problem as a Markov Decision Problem (MDP). Throughput is the Reward for the actions of the DRL agent. Comparison is done on the impact of slicing on throughput and compares models that have not been sliced, the ones that have been sliced and combined to work together, and models with slices that have been assigned more bandwidth. Sliced networks have better throughput than the ones not sliced. If more slices are load-balanced the throughput is increased. Deep Reinforcement Learning has managed to achieve the dynamic assigning of slices to compensate for declining throughput.
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P. Malathi, E. Grace Lydia, and P. Vidhyavathi. "EXPLORING THE POTENTIAL OF 5G TECHNOLOGIES: NAVIGATING OPPORTUNITIES AND CHALLENGES FOR INNOVATIVE FUTURE APPLICATIONS." Industrial Engineering Journal 52, no. 05 (2023): 736–42. http://dx.doi.org/10.36893/iej.2023.v52i05.736-742.
Abstract:
This paper explores the potential impact of the fifth generation (5G) [1] of wireless networks on communication and the opportunities and challenges it presents. It highlights the transformative possibilities of 5G, including enabling a broad range of new applications and services that were previously unattainable. The authors provide an extensive overview of the fundamental capabilities of 5G networks, which include high data rates, low latency [7] massive connectivity, and network slicing, among others. They further delve into the various application domains that could benefit from these features, such as autonomous vehicles [2], smart cities [3], virtual reality [4], and e-health [5]. The paper concludes by emphasizing the significant challenges that must be addressed to fully realize the potential of 5G networks, including concerns regarding spectrum [6] availability, infrastructure deployment, security, and privacy.
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Park, Seok-Woo, Kang-Hyun Moon, Kyung-Taek Chung, and In-Ho Ra. "Graph Neural Network and Reinforcement Learning based Optimal VNE Method in 5G and B5G Networks." Korean Institute of Smart Media 12, no. 11 (December 31, 2023): 113–24. http://dx.doi.org/10.30693/smj.2023.12.11.113.
Abstract:
With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.
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Rios, Álvaro, Barbara Valera-Muros, Pedro Merino-Gomez, and Jerry Sobieski. "Expanding GÉANT Testbeds Service to Support Pan-European 5G Network Slices for Research in the EuWireless Project." Mobile Information Systems 2019 (April 23, 2019): 1–13. http://dx.doi.org/10.1155/2019/6249247.
Abstract:
This paper presents the design options for creating a Pan-European mobile network for research in the context of the European Horizon 2020 EuWireless project. The most likely direction is a platform that makes it easier to create network slices for research. In this context, we identify one promising technology to implement network slicing in 5G networks: the framework GÉANT Testbeds Service (GTS). GTS is currently a production service by GÉANT that offers remote construction and use of virtual testbeds for wired networks mapped to the real GÉANT infrastructure. These GTS-virtualized testbed environments conform to Software Define Networks (SDNs) principles and offer compute, storage, and switching resources, at scale and with line rate performance. In this paper, we explain how the current (wired oriented) GTS can be extended with the 5G components, such as radio access nodes (gNBs), transport networks, user devices, etc., in order to implement 5G network slices. Our first conclusion is that using GTS for EuWireless implementation is feasible, dramatically increasing the potential impact of this service in the research community.
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Gupta, Amit. "5G Networks: Technologies, Applications and Challenges." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (April 30, 2020): 857–61. http://dx.doi.org/10.17762/turcomat.v11i1.13568.
Abstract:
The rollout of 5G networks is gaining momentum rapidly around the globe, and it holds the promise of delivering incredibly high data transfer rates, minimal latency, and huge interconnectedness. This article provides an overview of 5G networks, including a discussion of the technology, applications, and issues involved. First, we will discuss the technical features of 5G networks. These aspects include the spectrum, radio access methods, network design, and upcoming technologies such as network slicing and edge computing. After that, we will talk about a variety of applications, such as the Internet of Things, virtual reality, and autonomous cars, that have the potential to benefit from the capabilities of 5G networks. In conclusion, we investigate the primary obstacles that need to be conquered in order to fully fulfil the potential of 5G networks. These obstacles include concerns over regulation, privacy, and security. In addition, we give an overview of the present state of 5G networks and finish up with potential future research and development possibilities. The study is based on a survey of the relevant literature, and its primary objective is to deliver a full comprehension of the current state of the art regarding 5G networks.
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Jiang, Weiwei, Yafeng Zhan, and Xiaolong Xiao. "Multi-Domain Network Slicing in Satellite–Terrestrial Integrated Networks: A Multi-Sided Ascending-Price Auction Approach." Aerospace 10, no. 10 (September 23, 2023): 830. http://dx.doi.org/10.3390/aerospace10100830.
Abstract:
With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.
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Song, Fei, Jun Li, Chuan Ma, Yijin Zhang, Long Shi, and Dushantha Nalin K. Jayakody. "Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing." IEEE Open Journal of Vehicular Technology 1 (2020): 215–26. http://dx.doi.org/10.1109/ojvt.2020.2990072.
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Lei, Lifeng, Liang Kou, Xianghao Zhan, Jilin Zhang, and Yongjian Ren. "An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment." Sensors 22, no. 19 (September 30, 2022): 7436. http://dx.doi.org/10.3390/s22197436.
Abstract:
With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.
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Cristiano Bonato Both, Joao Borges, Luan Gon�alves, Cleverson Nahum, Ciro Macedo, Aldebaro Klautau, and Kleber Cardoso. "System intelligence for UAV-based mission critical services with challenging 5G/B5G connectivity." ITU Journal on Future and Evolving Technologies 3, no. 2 (June 23, 2022): 359–73. http://dx.doi.org/10.52953/wwdq6893.
Abstract:
Unmanned Aerial Vehicles (UAVs) and communication systems are fundamental elements in mission critical services, such as search and rescue. In this article, we introduce an architecture for managing and orchestrating 5G and beyond networks that operate over a heterogeneous infrastructure with UAVs' aid. UAVs are used for collecting and processing data, as well as improving communications. The proposed System Intelligence (SI) architecture was designed to comply with recent standardization works, especially the ETSI Experiential Networked Intelligence specifications. Another contribution of this article is an evaluation using a testbed based on a virtualized non-standalone 5G core and a 4G Radio Access Network (RAN) implemented with open-source software. The experimental results indicate, for instance, that SI can substantially improve the latency of UAV-based services by splitting deep neural networks between UAVs and edge or cloud equipment. Other experiments explore the slicing of RAN resources and efficient placement of virtual network functions to assess the benefits of incorporating intelligence in UAV-based mission critical services.
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Ge, Liping, and Jinhe Zhou. "Game-based cache allocation strategy for ICN slicing." MATEC Web of Conferences 336 (2021): 05030. http://dx.doi.org/10.1051/matecconf/202133605030.
Abstract:
To reduce the delay of content acquisition, this paper proposes a game-based cache allocation strategy in the Information-Centric Network (ICN) slice. The cache resource allocation of different mobile virtual network operators (MVNOs) is modeled as a non-cooperative game model. The Newton iterative method is used to solve this problem, and the cache space allocated by the base station for each MVNO is obtained. Finally, the Nash equilibrium solution is obtained. Simulation results show that the proposed algorithm can reduce the delay.
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Nkenyereye, Lionel, Lewis Nkenyereye, and Jong-Wook Jang. "Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey." Electronics 12, no. 9 (April 29, 2023): 2066. http://dx.doi.org/10.3390/electronics12092066.
Abstract:
Vehicular cloud computing (VCC) and connected vehicles have prompted the intensive investigation of communication and computing solutions. As an important enabler, software-defined network (SDN) broadly changes the design of vehicle services, from resource allocation to ambitious autonomous cars. However, current VCC architectures face challenges that hinder the vision of providing reliable services to connected vehicles. As a result, deploying VC services using SDN network has emerged as a viable option. Therefore, software-defined VC architecture (SDVC) dynamically manages the control and resource utilization of VC by centralizing the overall knowledge. In addition, SDN stands as the representative technique of virtual resources and network function virtualization (NFV). NFV is integrated into SDVC frameworks to design extended SDVC (ESDVC) for dynamic, adaptive VC maintenance, VC network slicing management, and to meet constraint requirements such as network latency and reliable connectivity. This paper presents and discusses: (1) the architecture scenario of both SDVC and ESDVC; (2) the effective deployment methods enabling NFV and network slicing (NS) frameworks to customize VC frameworks; (3) challenges and future concepts of more VC services based on ESDVC architecture. From this survey, we believe readers would find relevant methods for realigning information dispersed across the SDVC, fifth generation (5G)-based VC, and NS domains and comprehending the relationships between these technologies while encouraging further debate on the fusion of 5G enabling technologies over SDVC to enable VC network slicing.
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Elagin, Vasiliy, and Egor Mitin. "Technological Aspects of IBN Technology for Slicing at 5G Multiservice Network." Telecom IT 11, no. 2 (December 28, 2023): 35–46. http://dx.doi.org/10.31854/2307-1303-2023-11-2-35-46.
Abstract:
Purpose: The 5G network offers a variety of services, each of which requires specific parameters such as performance, throughput, reliability and latency. In this regard, network virtualization is a robust solution known as network slicing that provides support for different types of services and provides resources that match the needs of each service. The purpose of this review is to examine the implementation of a slicing platform within a 5G multiservice intent-based network that allows network operators to deploy network services in a flexible and customizable manner. Results: The considered system architecture provides the ability to monitor virtual and physical resources using OpenStack Neutron and the FlexRAN controller application. In addition, the implementation of the platform for intent-based network segmentation under consideration gives operators the ability to deploy network services in a flexible and customizable manner.
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Skondras, Emmanouil, Angelos Michalas, Dimitrios J. Vergados, Emmanouel T. Michailidis, Nikolaos I. Miridakis, and Dimitrios D. Vergados. "Network Slicing on 5G Vehicular Cloud Computing Systems." Electronics 10, no. 12 (June 19, 2021): 1474. http://dx.doi.org/10.3390/electronics10121474.
Abstract:
Fifth generation Vehicular Cloud Computing (5G-VCC) systems support various services with strict Quality of Service (QoS) constraints. Network access technologies such as Long-Term Evolution Advanced Pro with Full Dimensional Multiple-Input Multiple-Output (LTE-A Pro FD-MIMO) and LTE Vehicle to Everything (LTE-V2X) undertake the service of an increasing number of vehicular users, since each vehicle could serve multiple passenger with multiple services. Therefore, the design of efficient resource allocation schemes for 5G-VCC infrastructures is needed. This paper describes a network slicing scheme for 5G-VCC systems that aims to improve the performance of modern vehicular services. The QoS that each user perceives for his services as well as the energy consumption that each access network causes to user equipment are considered. Subsequently, the satisfactory grade of the user services is estimated by taking into consideration both the perceived QoS and the energy consumption. If the estimated satisfactory grade is above a predefined service threshold, then the necessary Resource Blocks (RBs) from the current Point of Access (PoA) are allocated to support the user’s services. On the contrary, if the estimated satisfactory grade is lower than the aforementioned threshold, additional RBs from a Virtual Resource Pool (VRP) located at the Software Defined Network (SDN) controller are committed by the PoA in order to satisfy the required services. The proposed scheme uses a Management and Orchestration (MANO) entity implemented by a SDN controller, orchestrating the entire procedure avoiding situations of interference from RBs of neighboring PoAs. Performance evaluation shows that the suggested method improves the resource allocation and enhances the performance of the offered services in terms of packet transfer delay, jitter, throughput and packet loss ratio.
42
Chen, Geng, Xu Zhang, Fei Shen, and Qingtian Zeng. "Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless Access Networks." Sensors 22, no. 9 (May 4, 2022): 3495. http://dx.doi.org/10.3390/s22093495.
Abstract:
Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5–2.7% higher than DQN and Double DQN, but the convergence is faster.
43
Salleras, Xavier, and Vanesa Daza. "SANS: Self-Sovereign Authentication for Network Slices." Security and Communication Networks 2020 (November 23, 2020): 1–8. http://dx.doi.org/10.1155/2020/8823573.
Abstract:
5G communications proposed significant improvements over 4G in terms of efficiency and security. Among these novelties, the 5G network slicing seems to have a prominent role: deploy multiple virtual network slices, each providing a different service with different needs and features. Like this, a Slice Operator (SO) ruling a specific slice may want to offer a service for users meeting some requirements. It is of paramount importance to provide a robust authentication protocol, able to ensure that users meet the requirements, providing at the same time a privacy-by-design architecture. This makes even more sense having a growing density of Internet of Things (IoT) devices exchanging private information over the network. In this paper, we improve the 5G network slicing authentication using a Self-Sovereign Identity (SSI) scheme: granting users full control over their data. We introduce an approach to allow a user to prove his right to access a specific service without leaking any information about him. Such an approach is SANS, a protocol that provides nonlinkable protection for any issued information, preventing an SO or an eavesdropper from tracking users’ activity and relating it to their real identities. Furthermore, our protocol is scalable and can be taken as a framework for improving related technologies in similar scenarios, like authentication in the 5G Radio Access Network (RAN) or other wireless networks and services. Such features can be achieved using cryptographic primitives called Zero-Knowledge Proofs (ZKPs). Upon implementing our solution using a state-of-the-art ZKP library and performing several experiments, we provide benchmarks demonstrating that our approach is affordable in speed and memory consumption.
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张, 筱筠. "Virtual Service Failure Recovery Algorithm Based on Particle Swarm in Network Slicing." Hans Journal of Wireless Communications 11, no. 03 (2021): 87–93. http://dx.doi.org/10.12677/hjwc.2021.113011.
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De Domenico, Antonio, Ya-Feng Liu, and Wei Yu. "Optimal Virtual Network Function Deployment for 5G Network Slicing in a Hybrid Cloud Infrastructure." IEEE Transactions on Wireless Communications 19, no. 12 (December 2020): 7942–56. http://dx.doi.org/10.1109/twc.2020.3017628.
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AlQahtani, Salman Ali. "Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities." Future Internet 15, no. 10 (October 19, 2023): 343. http://dx.doi.org/10.3390/fi15100343.
Abstract:
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.
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Casado-Vara, Roberto, Angel Martin-del Rey, Soffiene Affes, Javier Prieto, and Juan M. Corchado. "IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings." Future Generation Computer Systems 102 (January 2020): 965–77. http://dx.doi.org/10.1016/j.future.2019.09.042.
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Kandah, Farah, and Steven Schmitt. "SAND: Smart and Adaptable Networking Design Using Virtual Slicing over Software-Defined Network." EAI Endorsed Transactions on Internet of Things 4, no. 13 (September 11, 2018): 155333. http://dx.doi.org/10.4108/eai.6-4-2018.155333.
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Tang, Lun, Qi Tan, Yingjie Shi, Chenmeng Wang, and Qianbin Chen. "Adaptive Virtual Resource Allocation in 5G Network Slicing Using Constrained Markov Decision Process." IEEE Access 6 (2018): 61184–95. http://dx.doi.org/10.1109/access.2018.2876544.
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Li, Xin, Lu Wang, Yang Xin, Yixian Yang, Qifeng Tang, and Yuling Chen. "Automated Software Vulnerability Detection Based on Hybrid Neural Network." Applied Sciences 11, no. 7 (April 2, 2021): 3201. http://dx.doi.org/10.3390/app11073201.
Abstract:
Vulnerabilities threaten the security of information systems. It is crucial to detect and patch vulnerabilities before attacks happen. However, existing vulnerability detection methods suffer from long-term dependency, out of vocabulary, bias towards global features or local features, and coarse detection granularity. This paper proposes an automatic vulnerability detection framework in source code based on a hybrid neural network. First, the inputs are transformed into an intermediate representation with explicit structure information using lower level virtual machine intermediate representation (LLVM IR) and backward program slicing. After the transformation, the size of samples and the size of vocabulary are significantly reduced. A hybrid neural network model is then applied to extract high-level features of vulnerability, which learns features both from convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The former is applied to learn local vulnerability features, such as buffer size. Furthermore, the latter is utilized to learn global features, such as data dependency. The extracted features are made up of concatenated outputs of CNN and RNN. Experiments are performed to validate our vulnerability detection method. The results show that our proposed method achieves excellent results with F1-scores of 98.6% and accuracy of 99.0% on the SARD dataset. It outperforms state-of-the-art methods.