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Готові списки джерел за темами / Network Slice management

Добірка наукової літератури з теми "Network Slice management"

Автор: Grafiati

Опубліковано: 22 червня 2023

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Статті в журналах з теми "Network Slice management":

1

Pazhani.A, Azhagu Jaisudhan, P. Gunasekaran, Vimal Shanmuganathan, Sangsoon Lim, Kaliappan Madasamy, Rajesh Manoharan, and Amit Verma. "Peer–Peer Communication Using Novel Slice Handover Algorithm for 5G Wireless Networks." Journal of Sensor and Actuator Networks 11, no. 4 (November 29, 2022): 82. http://dx.doi.org/10.3390/jsan11040082.

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Анотація:

The goal of 5G wireless networks is to address the growing need for network services among users. User equipment has progressed to the point where users now expect diverse services from the network. The latency, reliability, and bandwidth requirements of users can all be classified. To fulfil the different needs of users in an economical manner, while guaranteeing network resources are resourcefully assigned to consumers, 5G systems plan to leverage technologies like Software Defined Networks, Network Function Virtualization, and Network Slicing. For the purpose of ensuring continuous handover among network slices, while catering to the advent of varied 5G application scenarios, new mobility management techniques must be adopted in Sliced 5G networks. Users want to travel from one region of coverage to another region without any fading in their network connection. Different network slices can coexist in 5G networks, with every slice offering services customized to various QoS demands. As a result, when customers travel from one region of coverage to another, the call can be transferred to a slice that caters to similar or slightly different requirements. The goal of this study was to develop an intra- and inter-slice algorithm for determining handover decisions in sliced 5G networks and to assess performance by comparing intra- and inter-slice handovers. The proposed work shows that an inter-slice handover algorithm offers superior quality of service when compared to an intra-slice algorithm.

2

An, Namwon, Yonggang Kim, Juman Park, Dae-Hoon Kwon, and Hyuk Lim. "Slice Management for Quality of Service Differentiation in Wireless Network Slicing." Sensors 19, no. 12 (June 19, 2019): 2745. http://dx.doi.org/10.3390/s19122745.

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Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method.

3

Ren, Zhe, Xinghua Li, Qi Jiang, Qingfeng Cheng, and Jianfeng Ma. "Fast and Universal Inter-Slice Handover Authentication with Privacy Protection in 5G Network." Security and Communication Networks 2021 (January 31, 2021): 1–19. http://dx.doi.org/10.1155/2021/6694058.

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In a 5G network-sliced environment, mobility management introduces a new form of handover called inter-slice handover among network slices. Users can change their slices as their preferences or requirements vary over time. However, existing handover-authentication mechanisms cannot support inter-slice handover because of the fine-grained demand among network slice services, which could cause challenging issues, such as the compromise of service quality, anonymity, and universality. In this paper, we address these issues by introducing a fast and universal inter-slice (FUIS) handover authentication framework based on blockchain, chameleon hash, and ring signature. To address these issues, we introduce an anonymous service-oriented authentication protocol with a key agreement for inter-slice handover by constructing an anonymous ticket with the trapdoor collision property of chameleon hash functions. In order to reduce the computation overhead of the user side in the process of authentication, a privacy-preserving ticket validation with a ring signature is designed to finish in the consensus phase of the blockchain in advance. Thanks to the edge computing capabilities in 5G, distributed edge nodes help to store the anonymous ticket information, which guarantees that the legal users can finish authentication swiftly during handover. Our scheme's performance is evaluated through simulation experiments to testify the efficiency and feasibility in a 5G network-sliced environment. The results show that compared to other authentication schemes of the same type, the overall inter-slice handover delay has been reduced by 97.94%.

4

Hurtado Sánchez, Johanna Andrea, Katherine Casilimas, and Oscar Mauricio Caicedo Rendon. "Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey." Sensors 22, no. 8 (April 15, 2022): 3031. http://dx.doi.org/10.3390/s22083031.

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Анотація:

Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services’ demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.

5

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.

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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%.

6

Koutlia, K., R. Ferrús, E. Coronado, R. Riggio, F. Casadevall, A. Umbert, and J. Pérez-Romero. "Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support." Wireless Communications and Mobile Computing 2019 (June 12, 2019): 1–17. http://dx.doi.org/10.1155/2019/2361352.

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Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.

7

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.

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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.

8

Wang, Qian, Yanan Zhang, and Xuanzhong Wang. "Resource Allocation Optimization Algorithm of Power 5G Network Slice Based on NFV and SDN." Journal of Physics: Conference Series 2476, no. 1 (April 1, 2023): 012085. http://dx.doi.org/10.1088/1742-6596/2476/1/012085.

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Abstract To realize 5G network slicing, the virtual resource scheduling and allocation method based on NFV and SDN is designed. The scheme comprehensively considers 5G communication technology and network slice structure, and establishes the optimal mathematical model of network slice operation economy. Then the network functions and resources are virtualized by NFV and SDN, the greedy algorithm is adopted to solve the mapping problem of network slices, and the specific implementation algorithm is provided. The simulation results show that the allocation algorithm can customize the management of network slice resources, achieve flexible control and sharing of network traffic and resources, and significantly promote the construction, optimization and promotion of 5G mobile communication network architecture.

9

Abbas, Khizar, Muhammad Afaq, Talha Ahmed Khan, Adeel Rafiq, and Wang-Cheol Song. "Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks." Electronics 9, no. 10 (October 18, 2020): 1710. http://dx.doi.org/10.3390/electronics9101710.

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The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

10

Singh, Sushil Kumar, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan, and Jong Hyuk Park. "Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment." Sustainability 12, no. 15 (August 3, 2020): 6250. http://dx.doi.org/10.3390/su12156250.

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Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.

Більше джерел

Дисертації з теми "Network Slice management":

1

Ben, Saad Sabra. "Security architectures for network slice management for 5G and beyond." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS023V2.pdf.

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L'architecture de découpage du réseau en sous-réseaux "Network slicing", rendue possible grâce aux nouvelles technologies telles que la virtualisation des fonctions réseau (NFV) et le réseau défini par logiciel (SDN), est l'un des principaux piliers des réseaux 5G et au-delà (B5G). Dans les environnements de la cinquième génération et au-delà (B5G), on s'attend à une multiplication du nombre de sous-réseaux coexistant, plus ou moins complexes, avec des durées de vie, des besoins en ressources et des objectifs de performance très divers. Cela crée des défis importants pour la gestion et l'orchestration des sous-réseaux sans intervention humaine, y compris la sécurité, la gestion des pannes et la confiance. En outre, le découpage du réseau ouvre le marché à de nouvelles parties prenantes, à savoir le vertical ou le locataire, le fournisseur de tranches de réseau et le fournisseur d'infrastructure. Dans ce contexte, il est nécessaire d'assurer non seulement une interaction sécurisée entre ces acteurs, mais aussi que chaque acteur fournisse le service attendu pour répondre aux exigences des sous-réseaux. Il convient donc de concevoir de nouvelles architectures sécurisées capables d'identifier/détecter en temps réel les nouvelles formes d'attaques liées au découpage de réseaux en tranches, tout en gérant de manière sûre et automatique les accords de niveau de service (SLAs) entre les acteurs impliqués. Dans cette thèse, nous concevons une nouvelle architecture sécurisée adaptée aux réseaux prêts pour le "Network slicing" (réseaux de cinquième génération (5G) et au-delà), en nous appuyant fortement sur la blockchain et l'intelligence artificielle (IA) pour permettre une gestion sécurisée et fiable des sous-réseaux
Network slicing architecture, enabled by new technologies such as Network Functions Virtualization (NFV) and Software-Defined Networking (SDN), is one of the main pillars of Fifth-generation and Beyond (B5G). In B5G settings, the number of coexisting slices with varying degrees of complexity and very diverse lifespans, resource requirements, and performance targets is expected to explode. This creates significant challenges towards zero-touch slice management and orchestration, including security, fault management, and trust. In addition, network slicing opens the business market to new stakeholders, namely the vertical or tenant, the network slice provider, and the infrastructure provider. In this context, there is a need to ensure not only a secure interaction between these actors, but also that each actor delivers the expected service to meet the network slice requirements. Therefore, new trust architectures should be designed, which are able to identify/detect the new forms of slicing-related attacks in real-time, while securely and automatically managing Service Level Agreements (SLA) among the involved actors. In this thesis, we devise new security architectures tailored to network slicing ready networks (B5G), heavily relying on blockchain and Artificial Intelligence (AI) to enable secure and trust network slicing management

2

wang, yang. "Resource Management in Survivable Multi-Granular Optical Networks." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/67.

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The last decade witnessed a wild growth of the Internet traffic, promoted by bandwidth-hungry applications such as Youtube, P2P, and VoIP. This explosive increase is expected to proceed with an annual rate of 34% in the near future, which leads to a huge challenge to the Internet infrastructure. One foremost solution to this problem is advancing the optical networking and switching, by which abundant bandwidth can be provided in an energy-efficient manner. For instance, with Wavelength Division Multiplexing (WDM) technology, each fiber can carry a mass of wavelengths with bandwidth up to 100 Gbits/s or higher. To keep up with the traffic explosion, however, simply scaling the number of fibers and/or wavelengths per fiber results in the scalability issue in WDM networks. One major motivation of this dissertation is to address this issue in WDM networks with the idea of waveband switching (WBS). This work includes the author's study on multiple aspects of waveband switching: how to address dynamic user demand, how to accommodate static user demand, and how to achieve a survivable WBS network. When combined together, the proposed approaches form a framework that enables an efficient WBS-based Internet in the near future or the middle term. As a long-term solution for the Internet backbone, the Spectrum Sliced Elastic Optical Path (SLICE) Networks recently attract significant interests. SLICE aims to provide abundant bandwidth by managing the spectrum resources as orthogonal sub-carriers, a finer granular than wavelengths of WDM networks. Another important component of this dissertation is the author's timely study on this new frontier: particulary, how to efficiency accommodate the user demand in SLICE networks. We refer to the overall study as the resource management in multi-granular optical networks. In WBS networks, the multi-granularity includes the fiber, waveband, and wavelength. While in SLICE networks, the traffic granularity refers to the fiber, and the variety of the demand size (in terms of number of sub-carriers).

3

Hamouda, Ahmed Mohamed Abdelaty Elmekkawi. "Resource allocation and management under priorities based on the squatting-kicking model for multi-slice 5G networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672663.

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The upcoming Beyond Fifth Generation networks aim to meet network services characterized by low latency and high reliability among others in different slices to provide a high-quality user experience. However, existing best-effort networking schemes that implement traditional methods of controlling and allocating network and computing resources do not meet such strict service requirements well. In International Telecommunication Union-Telecommunication sector, future services are defined as Network 2030 Services under a chartered Focus Group on Networks 2030 (FG-NET2030). The results from the FG-NET2030 analysis suggests that current networks cannot accommodate real-time applications with low latency and high bandwidth requirements. Moreover, current networks lack the capabilities to dynamically aggregate and share network resources through multiple flows, which is essential for future services. However, to satisfy the strict requirements of those services, intelligent algorithms and techniques that incorporate 5G enablers are needed to introduce novel network management systems. These intelligent algorithms shall not only result in efficient utilization of network resources but also guarantee the required quality of service for the priority slices. Moreover, cognizant of the strict latency requirements of the different services, such algorithms should include delay constraints of requests. Despite the advantages expected from future services are real-time applications, should benefit from reduced physical and logical paths between end-users and data or service hosts. However, all the above requirements are not intended for the network slicing paradigm alone. Therefore, in addition to network slicing, we want to leverage technologies and components that have features such as network programming, dynamic network reconfiguration and orchestration to enable improved performance and efficient resource management. Such technologies include NFV and SDN among others. Consequently, the main objective of this PhD thesis is to develop a service deployment algorithm that uses Squatting and Kicking techniques intelligence to effectively allocate, manage, and control slice resources under several constraints in a real-time multi-slice scenario, such as priority, bandwidth, and E2E delay with targeting to maximize the total resource usage in the substrate network. The proposed online algorithm allocates the available resource to different priority demands from source node to destination node along the routed path according to more realistic constraints, such as links' bandwidth and E2E delay. Moreover, the benefits of the new proposed algorithm will be reflected in creating real-time demands for 5G applications that are sensitive to delay, in addition to solving the resource allocation problem for large scale networks, using fewer resources and generating lower costs. Further, the proposed algorithm is adaptable to meet various QoS requirements of services, guaranteeing high QoS levels and providing high admission for higher priority classes under congested scenarios. In terms of managing bandwidth resources in a multi-slice scenario, Bandwidth Allocation Models offer improved metrics over best effort models. The proposed algorithm outperforms the others by far especially, during congested scenarios. To this end, this thesis proposes a resource allocation model called Squatting and Kicking model (SKM) to maximize the number of successfully embedded demands while maximizing the utilization in the multi-slice networks by choosing less congested paths through the efficient allocation of demands on the network. Moreover, this thesis analyzes the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient SKM, proved in this work to be the most effective up to the present time yet.
Enginyeria telemàtica

4

Cruz, Henrique Almeida Andrade de Castro. "5G network slice manager." Master's thesis, 2019. http://hdl.handle.net/10773/29573.

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The Fifth Generation of Mobile Networks (5G) is presented as the most evolutionary generation of mobile networks to date. As new and upcoming technologies and use cases promote new challenges, 5G will have to meet these demands. In order to do this, new technologies are being introduced, such as Software Defined Networks (SDN), Network Function Virtualization (NFV) and Network Slicing. Network Slices will be essential to guarantee customized Quality of Service and Experience to vertical industries and use cases while they benefit from dedicated virtual networks whose physical elements are shared by other Slices or Network Functions. Consequently, Network Operators will provide new and improved services while guaranteeing lesser operational and capital costs. However, technologies such as Network Slicing are still in a premature state and a practical management system is essential to effectively reach its goals. This thesis presents a proof of concept of a 5G Network Slice Manager through a web platform specifically developed for this purpose.
A Quinta Geração de Redes Móveis (5G) apresenta-se como a mais revolucionária geração de redes móveis até agora. Impulsionada por novos desafios que tecnologias emergentes e casos de uso promovem, o 5G será essencial para potenciar novos serviços e lidar com a exigência dos requisitos que daí advêm. Para atingir estas metas definidas para o 5G, novas tecnologias destacam-se como as Redes Definidas por Software (SDN), a Virtualização de Funções de Rede (NFV) e as Slices de rede. Estas últimas serão essenciais para garantir Qualidades de Serviço e Experiência a serviços verticais que necessitam de garantias de segurança e isolamento dos seus dados enquanto beneficiam de redes virtuais dedicadas cujos elementos físicos são partilhados para outras Slices ou Funções de Rede. Desta forma, os Operadores de Rede conseguem fornecer novos e melhorados serviços enquanto garantem menores custos operacionais e capitais. No entanto, tecnologias como o Slicing de Redes encontram-se permaturas e um sistema prático para a sua gestão entre o cliente e o fornecedor será essencial para alcançar eficientemente os objetivos da mesma. Esta dissertação apresenta uma prova de conceito de um gestor de Slices de Rede 5G através de uma plataforma web desenvolvida para o efeito.
Mestrado em Engenharia de Computadores e Telemática

Частини книг з теми "Network Slice management":

1

Hsiung, Chun, Fuchun Joseph Lin, Jyh-Cheng Chen, and Chien Chen. "5G Network Slice Scalability Based on Management Data Analytics Function (MDAF)." In Communications in Computer and Information Science, 587–98. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9582-8_52.

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Thantharate, Anurag. "FED6G: Chameleon Learning for Network Slice Management in Beyond 5G Systems." In International Symposium on Intelligent Informatics, 413–26. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8094-7_32.

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3

Zhao, Guanghuai, Mingshi Wen, Jiakai Hao, and Tianxiang Hai. "Application of Dynamic Management of 5G Network Slice Resource Based on Reinforcement Learning in Smart Grid." In Advances in Intelligent Systems and Computing, 1485–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8462-6_169.

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4

Kukliński, Sławomir, Lechosław Tomaszewski, Ioannis P. Chochliouros, Christos Verikoukis, Robert Kołakowski, Anastasia S. Spiliopoulou, and Alexandros Kostopoulos. "A Novel Architectural Approach for the Provision of Scalable and Automated Network Slice Management, in 5G and Beyond." In Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops, 39–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79157-5_4.

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5

Miselis, Bartosz, Michał Kulus, Tomasz Jurek, Andrzej Rusiecki, and Łukasz Jeleń. "Deep Neural Network for Whole Slide Vein Segmentation." In Computer Information Systems and Industrial Management, 57–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99954-8_6.

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6

Kammoun, Amal, Nabil Tabbane, Gladys Diaz, Nadjib Achir, and Abdulhalim Dandoush. "Inter-slice Mobility Management in the Context of SDN/NFV Networks." In Communications in Computer and Information Science, 77–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40131-3_5.

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7

Toosi, Adel Nadjaran, Redowan Mahmud, Qinghua Chi, and Rajkumar Buyya. "Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds." In Fog and Edge Computing, 79–101. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119525080.ch4.

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8

Kammoun, Amal, Nabil Tabbane, Gladys Diaz, Nadjib Achir, and Abdulhalim Dandoush. "Proactive Network Slices Management Algorithm Based on Fuzzy Logic System and Support Vector Regression Model." In Lecture Notes in Networks and Systems, 386–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33506-9_34.

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9

Messaoud, Seifeddine, Soulef Bouaafia, Abbas Bradai, Mohamed Ali Hajjaji, Abdellatif Mtibaa, and Mohamed Atri. "Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges." In Emerging Trends in Wireless Sensor Networks [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.102472.

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Анотація:

5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools.

10

Jayanth, K. O. L. N., G. Veerapandu, Sridevi Gamini, and Y. Yamini Devi. "Cloud Enabled Architecture of 5-G Small Cell Network." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde221278.

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5G technology will allow communication networks to manage a wider range of network services from a wider range of locations, making them more adaptable. It also provides an overview of the 5G-ESSENCE project and a small cell design for 5G networks that was created as a result of the initiative. Cloud computing on the edge and small cells as a service are at the core of this system. Researchers explore how to convert their proposed architecture for 5G radio resource management and how to slice the network based on that architecture in this paper. This research also looks at many aspects of 5G technology, such as radio links, multi-RAT, and so on. Radio access networks (RANs) can be improved via network function virtualization, as well (NFV). A public safety use case’s improvement in defined key performance criteria is then evaluated. Finally, the performance of a 5G network capable of supporting an increase in the number of multicast multimedia broadcast services will be evaluated.

Тези доповідей конференцій з теми "Network Slice management":

1

Chahbar, Mohammed, Gladys Diaz, Abdulhalim Dandoush, Christophe Cerin, and Kamal Ghoumid. "NESSMA: Network Slice Subnet Management Framework." In 2019 10th International Conference on Networks of the Future (NoF). IEEE, 2019. http://dx.doi.org/10.1109/nof47743.2019.9015010.

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2

Han, Bin, Shreya Tayade, and Hans D. Schotten. "Modeling profit of sliced 5G networks for advanced network resource management and slice implementation." In 2017 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2017. http://dx.doi.org/10.1109/iscc.2017.8024590.

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3

Rodriguez, Veronica Quintuna, Fabrice Guillemin, and Amina Boubendir. "Network Slice Management on top of ONAP." In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2020. http://dx.doi.org/10.1109/noms47738.2020.9110338.

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4

Chen, Ke, Ying Wang, Peng Yu, and Naling Li. "Security-Oriented Network Slice Backup Method." In 2021 22nd Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2021. http://dx.doi.org/10.23919/apnoms52696.2021.9562592.

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5

Taeb, Sepehr, Nashid Shahriar, Shihabur R. Chowdhury, Massimo Tornatore, Raouf Boutaba, Jeebak Mitra, and Mahdi Hemmati. "Reoptimizing Network Slice Embedding on EON-enabled Transport Networks." In 2021 17th International Conference on Network and Service Management (CNSM). IEEE, 2021. http://dx.doi.org/10.23919/cnsm52442.2021.9615515.

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6

Chen, Jen-Jee, Meng-Hsun Tsai, Liqiang Zhao, Wei-Chiao Chang, Yu-Hsiang Lin, Qianwen Zhou, Yu-Zhang Lu, Jia-Ling Tsai, and Yun-Zhan Cai. "Realizing Dynamic Network Slice Resource Management based on SDN networks." In 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA). IEEE, 2019. http://dx.doi.org/10.1109/icea.2019.8858288.

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7

Rabitsch, Alexander, George Xilouris, Themistoklis Anagnostopoulos, Karl-Johan Grinnemo, Thanos Sarlas, Anna Brunstrom, Özgü Alay, and Giuseppe Caso. "Extending network slice management to the end-host." In SIGCOMM '21: ACM SIGCOMM 2021 Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472771.3472775.

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8

Bera, Samaresh, and Neelesh B. Mehta. "Network Slicing in 5G Edge Networks with Controlled Slice Redistributions." In 2021 17th International Conference on Network and Service Management (CNSM). IEEE, 2021. http://dx.doi.org/10.23919/cnsm52442.2021.9615516.

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9

Fossati, Francesca, Stefano Moretti, Stephane Rovedakis, and Stefano Secci. "Decentralization of 5G slice resource allocation." In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2020. http://dx.doi.org/10.1109/noms47738.2020.9110391.

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10

Kim, Do Hyeon, S. M. Ahsan Kazmi, and Choong Seon Hong. "Cooperative Slice Allocation for Virtualized Wireless Network." In IMCOM '18: The 12th International Conference on Ubiquitous Information Management and Communication. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3164541.3164565.

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