Relevant bibliographies by topics / Slice admission control

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Slice admission control'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Slice admission control"

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Han, Bin, Di Feng, and Hans D. Schotten. "A Markov Model of Slice Admission Control." IEEE Networking Letters 1, no. 1 (March 2019): 2–5. http://dx.doi.org/10.1109/lnet.2018.2873978.

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Ibrahim, Mai, Mohamed TALAAT FAHIM, and Nada Elshennawy. "Slice Admission control based on Reinforcement Learning for 5G Networks." Journal of Engineering Research 7, no. 3 (September 1, 2023): 144–52. http://dx.doi.org/10.21608/erjeng.2023.228909.1209.

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Challa, Rajesh, Vyacheslav V. Zalyubovskiy, Syed M. Raza, Hyunseung Choo, and Aloknath De. "Network Slice Admission Model: Tradeoff Between Monetization and Rejections." IEEE Systems Journal 14, no. 1 (March 2020): 657–60. http://dx.doi.org/10.1109/jsyst.2019.2904667.

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Ampririt, Phudit, Yi Liu, Makoto Ikeda, Keita Matsuo, Leonard Barolli, and Makoto Takizawa. "Effect of Slice Priority for admission control in 5G Wireless Networks: A comparison study for two Fuzzy-based systems considering Software-Defined-Networks." Journal of High Speed Networks 26, no. 3 (November 27, 2020): 169–83. http://dx.doi.org/10.3233/jhs-200637.

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The Fifth Generation (5G) networks are expected to be flexible to satisfy demands of high-quality services such as high speed, low latencies and enhanced reliability from customers. Also, the rapidly increasing amount of user devices and high user’s requests becomes a problem. Thus, the Software-Defined Network (SDN) will be the key function for efficient management and control. To deal with these problems, we propose a Fuzzy-based SDN approach. This paper presents and compares two Fuzzy-based Systems for Admission Control (FBSAC) in 5G wireless networks: FBSAC1 and FBSAC2. The FBSAC1 considers for admission control decision three parameters: Grade of Service (GS), User Request Delay Time (URDT) and Network Slice Size (NSS). In FBSAC2, we consider as an additional parameter the Slice Priority (SP). So, FBSAC2 has four input parameters. The simulation results show that the FBSAC2 is more complex than FBSAC1, but it has a better performance for admission control.
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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.
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Ojijo, Mourice O., and Olabisi E. Falowo. "A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network." IEEE Access 8 (2020): 14977–90. http://dx.doi.org/10.1109/access.2020.2967626.

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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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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.
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Hikmah Puspita, Ratih, Jehad Ali, and Byeong-hee Roh. "An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G Network Slicing." Computers, Materials & Continua 75, no. 2 (2023): 4611–31. http://dx.doi.org/10.32604/cmc.2023.033598.

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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.

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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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Othman, Anuar, and Nazrul Anuar Nayan. "Efficient admission control and resource allocation mechanisms for public safety communications over 5G network slice." Telecommunication Systems 72, no. 4 (July 27, 2019): 595–607. http://dx.doi.org/10.1007/s11235-019-00600-9.

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Dissertations / Theses on the topic "Slice admission control"

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Luu, Quang Trung. "Dynamic Control and Optimization of Wireless Virtual Networks." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG039.

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Le découpage du réseau est une technologie clé des réseaux 5G, grâce à laquelle les opérateurs de réseaux mobiles peuvent créer des tranches de réseau indépendantes. Chaque tranche permet à des fournisseurs d'offrir des services personnalisés. Comme les tranches sont opérées sur une infrastructure de réseau commune gérée par un fournisseur d'infrastructure, il est essentiel de développer des méthodes de partage efficace des ressources. Cette thèse adopte le point de vue du fournisseur d'infrastructure et propose plusieurs méthodes de réservation de ressources pour les tranches de réseau. Actuellement, les chaines de fonctions appartenant à une tranche sont déployées séquentiellement sur l'infrastructure, sans avoir de garantie quant à la disponibilité des ressources. Afin d'aller au-delà de cette approche, nous considérons dans cette thèse des approches de réservation des ressources pour les tranches en considérant les besoins agrégés des chaines de fonctions avant le déploiement effectif des chaines de fonctions. Lorsque la réservation a abouti, les chaines de fonctions ont l'assurance de disposer de suffisamment de ressources lors de leur déploiement et de leur mise en service afin de satisfaire les exigences de qualité de service de la tranche. La réservation de ressources permet également d'accélérer la phase d'allocation de ressources des chaines de fonctions
Network slicing is a key enabler for 5G networks. With network slicing, Mobile Network Operators (MNO) create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), efficiently sharing the resources across various slices is very important. In this thesis, taking the InP perspective, we propose several methods for provisioning resources for network slices. Previous best-effort approaches deploy the various Service Function Chains (SFCs) of a given slice sequentially in the infrastructure network. In this thesis, we provision aggregate resources to accommodate slice demands. Once provisioning is successful, the SFCs of the slice are ensured to get enough resources to be properly operated. This facilitates the satisfaction of the slice quality of service requirements. The proposed provisioning solutions also yield a reduction of the computational resources needed to deploy the SFCs
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Bakri, Sihem. "Towards enforcing network slicing in 5G networks." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS067.

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Les architectures de réseaux sans fil actuelles, de type « une taille pour tous », ne peuvent pas prendre en charge ces critères de services hétérogènes de nouvelle génération 5G. Par conséquent, la recherche autour de la 5G vise à fournir des architectures et des mécanismes plus adéquats pour répondre à ce besoin. L'architecture 5G est conçue pour répondre aux exigences variées et contradictoires des services, en termes de latence, de bande passante et de fiabilité, qui ne peuvent être assurées par la même infrastructure du réseau. Dans ce contexte, le découpage du réseau fourni par la virtualisation du réseau permet de diviser l'infrastructure en différentes tranches, chaque tranche est adaptée aux besoins spécifiques des services, où elle permet à différents services (comme l'automobile, l'Internet des objets...) d'être fournis par différentes instances de la tranche du réseau. Les chercheurs ont défini trois grandes classes de services de découpage en réseau, qui sont: enhanced Mobile BroadBand (eMBB), massive Machine Type Communication (mMTC), and ultra-Reliable and Low-Latency Communication (uRLLC). L'un des principaux défis du déploiement des tranches de réseau est le découpage du réseau d'accès radio (RAN). En effet, la gestion des ressources RAN et leur partage entre les tranches de réseau est une tâche particulièrement difficile. Cette thèse propose des solutions qui visent à améliorer les performances du réseau et d'introduire de la flexibilité et une plus grande utilisation des ressources du réseau, en fournissant de manière précise et dynamique aux tranches de réseau activées les quantités de ressources appropriées pour répondre à leurs divers besoins
The current architecture “one size fits all” of 4G network cannot support the next-generation 5G heterogeneous services criteria. Therefore, research around 5G aims to provide more adequate architectures and mechanisms to deal with this purpose. The 5G architecture is envisioned to accommodate the diverse and conflicting demands of services in terms of latency, bandwidth, and reliability, which cannot be sustained by the same network infrastructure. In this context, network slicing provided by network virtualization allows the infrastructure to be divided into different slices. Each slice is tailored to meet specific service requirements allowing different services (such as automotive, Internet of Things, etc.) to be provided by different network slice instances. Each of these instances consists of a set of virtual network functions that run on the same infrastructure with specially adapted orchestration. Three main service classes of network slicing have been defined by the researchers as follows: Enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and ultra-Reliable and Low-Latency Communication (uRLLC). One of the main challenges when it comes to deploying Network Slices is slicing the Radio Access Network (RAN). Indeed, managing RAN resources and sharing them among Network Slices is an increasingly difficult task, which needs to be properly designed. This thesis proposes solutions that aim to improve network performance, and introduce flexibility and greater utilization of network resources by accurately and dynamically provisioning the activated network slices with the appropriate amounts of resources to meet their diverse requirements
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Book chapters on the topic "Slice admission control"

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Batista, Pedro, Shah Nawaz Khan, Peter Öhlén, and Aldebaro Klautau. "Tenant-Aware Slice Admission Control Using Neural Networks-Based Policy Agent." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 17–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25748-4_2.

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Ampririt, Phudit, Ermioni Qafzezi, Kevin Bylykbashi, Makoto Ikeda, Keita Matsuo, and Leonard Barolli. "A Fuzzy-Based Scheme for Admission Control in 5G Wireless Networks: Improvement of Slice QoS Considering Slice Reliability as a New Parameter." In Advanced Information Networking and Applications, 17–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75078-7_3.

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Ampririt, Phudit, Seiji Ohara, Makoto Ikeda, Keita Matsuo, Leonard Barolli, and Makoto Takizawa. "Effect of Network Slice Duration for 5G Wireless Networks: A Fuzzy-Based Admission Control System Considering Software-Defined Network Approach." In Advances in Intelligent Systems and Computing, 508–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57811-4_51.

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Ampririt, Phudit, Seiji Ohara, Ermioni Qafzezi, Makoto Ikeda, Leonard Barolli, and Makoto Takizawa. "Effect of Slice Overloading Cost on Admission Control for 5G Wireless Networks: A Fuzzy-Based System and Its Performance Evaluation." In Advances in Internet, Data and Web Technologies, 24–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70639-5_3.

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Conference papers on the topic "Slice admission control"

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Kołakowski, Robert, Sławomir Kukliński, and Lechosław Tomaszewski. "Time-of-Day-Aware Slice Admission Control." In 2023 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). IEEE, 2023. http://dx.doi.org/10.1109/meditcom58224.2023.10266645.

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II Moon, Seung, Haruhisa Hirayama, Yu Tsukamoto, Shinobu Nanba, and Hiroyuki Shinbo. "Ensemble Learning Method-Based Slice Admission Control for Adaptive RAN." In 2020 IEEE Globecom Workshops (GC Wkshps). IEEE, 2020. http://dx.doi.org/10.1109/gcwkshps50303.2020.9367536.

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Ajayi, Jesutofunmi, Antonio Di Maio, Torsten Braun, and Dimitrios Xenakis. "An Online Multi-dimensional Knapsack Approach for Slice Admission Control." In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC). IEEE, 2023. http://dx.doi.org/10.1109/ccnc51644.2023.10060460.

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Haque, Md Ariful, and Vassilka Kirova. "5G Network Slice Admission Control Using Optimization and Reinforcement Learning." In 2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2022. http://dx.doi.org/10.1109/wcnc51071.2022.9771643.

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Perveen, Abida, Mohammad Patwary, and Adel Aneiba. "Dynamically Reconfigurable Slice Allocation and Admission Control within 5G Wireless Networks." In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). IEEE, 2019. http://dx.doi.org/10.1109/vtcspring.2019.8746625.

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Yese, Solomon Orduen, Sara Berri, and Arsenia Chorti. "Novel Slice Admission Control Scheme with Overbooking and Dynamic Buyback Process." In 2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2023. http://dx.doi.org/10.1109/nfv-sdn59219.2023.10329608.

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Chakraborty, Kushal, Krishnendu T. S, Moumita Patra, and T. Venkatesh. "A Novel Network Slice Admission Control Scheme in 5G-V2X Communication." In 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2024. http://dx.doi.org/10.1109/comsnets59351.2024.10427310.

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Vila, I., J. Perez-Romero, O. Sallent, A. Umbert, and R. Ferrus. "Performance Measurements-Based Estimation of Radio Resource Requirements for Slice Admission Control." In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall). IEEE, 2019. http://dx.doi.org/10.1109/vtcfall.2019.8891429.

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Shen, Hong, Yukai Ye, and Tiankui Zhang. "Joint Slice Level and Base Station Level Admission Control in RAN Slicing." In 2022 IEEE 22nd International Conference on Communication Technology (ICCT). IEEE, 2022. http://dx.doi.org/10.1109/icct56141.2022.10073118.

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Bai, Yaofu, Chengchao Liang, and Qianbin Chen. "Network Slice Admission Control and Resource Allocation in LEO Satellite Networks: A Robust Optimization Approach." In 2022 27th Asia Pacific Conference on Communications (APCC). IEEE, 2022. http://dx.doi.org/10.1109/apcc55198.2022.9943670.

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