Добірка наукової літератури з теми "Tranchage de réseau"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Tranchage de réseau".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Tranchage de réseau":
Dellière, Sarah. "Les réseaux sociaux au service de l’intégrité scientifique, une arme à double tranchant." médecine/sciences 38, no. 5 (May 2022): 477–79. http://dx.doi.org/10.1051/medsci/2022061.
Lins de Barros, Myriam Moraes, and Sara Nigri Goldman. "Internet: Y a-t-il une place pour les “vieux”?" Revista Trace, no. 41 (September 5, 2018): 97. http://dx.doi.org/10.22134/trace.41.2002.568.
Lambert, Claude. "De la nécessité de Désordre dans la Démocratie." Acta Europeana Systemica 6 (July 12, 2020): 41–48. http://dx.doi.org/10.14428/aes.v6i1.56803.
Дисертації з теми "Tranchage de réseau":
Rachedi, Abdennour. "Optimisation de la gestion des ressources des réseaux ITS-G5 pour le support des services C-ITS." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0070.
This thesis unfolds in the dynamic context of Cooperative Intelligent Transport Systems (C-ITS) and Vehicle-to-Everything (V2X) communications, with a particular focus on the integration of emerging technologies such as Artificial Intelligence (AI), Network Slicing, and Multi-access Edge Computing (MEC). These revolutionary technologies are reshaping the way vehicular networks manage traffic safety and efficiency while presenting unique challenges. The first major challenge addressed in this thesis is the degradation of communication channel quality in congested V2X networks. This common situation in dense traffic environments negatively impacts vehicular communication performance, thus hindering the efficiency of C-ITS. The second challenge is to ensure ultra-low end-to-end (E2E) latency in these congested networks, particularly for services and user groups requiring high priority. This need is especially crucial in scenarios where vehicles, such as emergency services, rely on rapid and reliable communication. The third significant issue tackled is service migration in MEC-enabled vehicular networks, an essential aspect to ensure service continuity in highly mobile environments. The mobile nature of vehicular networks, combined with the limited coverage of edge servers, poses significant challenges in maintaining QoS and minimizing service interruptions. To address these challenges, the thesis proposes several innovative solutions. A proactive approach for Decentralized Congestion Control (DCC) was developed using Long Short-Term Memory (LSTM) recurrent neural networks. This technique aims to optimize channel performance by forecasting the Channel Busy Ratio (CBR), thus improving network stability and ensuring fair resource allocation. Simulations demonstrated the effectiveness of proactive DCC algorithms, showing faster convergence and better resource management. Next, we address the innovative aspects of network slicing in ITS-G5 vehicular communications. The second contribution proposes an ITS-G5 RAN slicing architecture, aiming to create slices with varied priorities for efficient and secure traffic, while ensuring isolation and prioritization between slices. This approach aims to maintain performance and security for each slice, even in the presence of conflicting services. In the third contribution, we develop an end-to-end network slicing architecture, aiming to improve latency for specific user groups, particularly in congested areas. Simulations confirmed the effectiveness of these architectures in traffic flow management and latency reduction for high-priority services, demonstrating the importance of these approaches in advancing intelligent and efficient vehicular networks. III Finally, to address service migration in MEC vehicular networks, we formulated the problem as a Markov Decision Process (MDP) and developed an adaptive migration strategy using Deep Reinforcement Learning (DRL), specifically Deep Q Networks (DQN) and Double Deep Q-network (DDQN) approaches. This strategy aims to balance migration costs and latency. Simulation results showed that the DDQN method excels in managing migration costs while maintaining optimal QoS, particularly for latency-sensitive services, and offers an optimal balance for high-priority services. These contributions, combining technological advances and innovative analytical approaches, provide robust solutions to current and emerging challenges in cooperative intelligent transport systems, paving the way for significant improvements in road safety, traffic efficiency, and user experience in the field of smart mobility
Matoussi, Salma. "User-Centric Slicing with Functional Splits in 5G Cloud-RAN." Electronic Thesis or Diss., Sorbonne université, 2021. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2021SORUS004.pdf.
5G Radio Access Network (RAN) aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the RAN functions between centralized and distributed locations to improve the RAN flexibility. However, one of the major concerns is to efficiently allocate RAN resources, while supporting heterogeneous 5G service requirements. In this thesis, we address the problematic of the user-centric RAN slice provisioning, within a Cloud RAN infrastructure enabling flexible functional splits. Our research aims to jointly meet the end users’ requirements, while minimizing the deployment cost. The problem is NP-hard. To overcome the great complexity involved, we propose a number of heuristic provisioning strategies and we tackle the problem on four stages. First, we propose a new implementation of a cost efficient C-RAN architecture, enabling on-demand deployment of RAN resources, denoted by AgilRAN. Second, we consider the network function placement sub-problem and propound a new scalable user-centric functional split selection strategy named SPLIT-HPSO. Third, we integrate the radio resource allocation scheme in the functional split selection optimization approach. To do so, we propose a new heuristic based on Swarm Particle Optimization and Dijkstra approaches, so called E2E-USA. In the fourth stage, we consider a deep learning based approach for user-centric RAN Slice Allocation scheme, so called DL-USA, to operate in real-time. The results obtained prove the efficiency of our proposed strategies
Patry, Jean-Luc. "Intégration sur tranche d'une architecture massivement parallèle tolérant les défauts de fin de fabrication." Phd thesis, Grenoble INPG, 1992. http://tel.archives-ouvertes.fr/tel-00341630.
Domas, Jérémie. "Valorisation de sables issus de boues de curage des réseaux d'assainissementbTexte imprimé : exemple en remblayage de tranchée." Marne-la-vallée, ENPC, 1999. http://www.theses.fr/1999ENPC9933.
Hurat, Philippe. "APLYSIE : un circuit neuro-mimétique : réalisation et intégration sur tranche." Phd thesis, Grenoble INPG, 1989. http://tel.archives-ouvertes.fr/tel-00332382.
Luu, Quang Trung. "Dynamic Control and Optimization of Wireless Virtual Networks." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG039.
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
Song, Jinyan. "Capteurs optiques intégrés basés sur des lasers à semiconducteur et des résonateurs en anneaux interrogés en intensité." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00811402.