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Статті в журналах з теми "Placement de Serveurs Edge":
Ma, Rong. "Edge Server Placement for Service Offloading in Internet of Things." Security and Communication Networks 2021 (September 30, 2021): 1–16. http://dx.doi.org/10.1155/2021/5109163.
Luo, Fei, Shuai Zheng, Weichao Ding, Joel Fuentes, and Yong Li. "An Edge Server Placement Method Based on Reinforcement Learning." Entropy 24, no. 3 (February 23, 2022): 317. http://dx.doi.org/10.3390/e24030317.
Wang, Shangguang, Yali Zhao, Jinlinag Xu, Jie Yuan, and Ching-Hsien Hsu. "Edge server placement in mobile edge computing." Journal of Parallel and Distributed Computing 127 (May 2019): 160–68. http://dx.doi.org/10.1016/j.jpdc.2018.06.008.
Guo, Feiyan, Bing Tang, and Jiaming Zhang. "Mobile edge server placement based on meta-heuristic algorithm." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 8883–97. http://dx.doi.org/10.3233/jifs-200933.
Zhang, Qiyang, Shangguang Wang, Ao Zhou, and Xiao Ma. "Cost-aware edge server placement." International Journal of Web and Grid Services 18, no. 1 (2022): 83. http://dx.doi.org/10.1504/ijwgs.2022.119275.
Ma, Xiao, Ao Zhou, Qiyang Zhang, and Shangguang Wang. "Cost-aware edge server placement." International Journal of Web and Grid Services 18, no. 1 (2022): 83. http://dx.doi.org/10.1504/ijwgs.2022.10042204.
Kasi, Mumraiz Khan, Sarah Abu Ghazalah, Raja Naeem Akram, and Damien Sauveron. "Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning." Electronics 10, no. 17 (August 30, 2021): 2098. http://dx.doi.org/10.3390/electronics10172098.
Zhang, Jianshan, Ming Li, Xianghan Zheng, and Ching-Hsien Hsu. "A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks." Sensors 22, no. 9 (April 29, 2022): 3422. http://dx.doi.org/10.3390/s22093422.
Shao, Yanling, Zhen Shen, Siliang Gong, and Hanyao Huang. "Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks." Wireless Communications and Mobile Computing 2022 (July 27, 2022): 1–17. http://dx.doi.org/10.1155/2022/8936576.
Yin, Hao, Xu Zhang, Hongqiang H. Liu, Yan Luo, Chen Tian, Shuoyao Zhao, and Feng Li. "Edge Provisioning with Flexible Server Placement." IEEE Transactions on Parallel and Distributed Systems 28, no. 4 (April 1, 2017): 1031–45. http://dx.doi.org/10.1109/tpds.2016.2604803.
Дисертації з теми "Placement de Serveurs Edge":
Khamari, Sabri. "Architectures et protocoles pour les véhicules connectés." Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0483.
The advent of Intelligent Transportation Systems (ITS) marks a paradigm shift in the approach to managing and optimizing transportation infrastructures. Rooted in the integration of state-of-the-art communication technologies, ITS encompass a variety of applications aimed at enhancing road safety, traffic efficiency, and driving comfort. However, the execution of these increasingly computation-intensive applications raises inherent challenges related to latency, data processing, and service continuity. The emergence of Edge Computing stands as a transformative advancement poised to redefine the efficacy of vehicular applications in Intelligent Transportation Systems (ITS). Contrasting with conventional cloud computing paradigms, which frequently encounter latency issues attributable to the remote nature of data processing, Edge Computing decentralizes computational tasks to be nearer to the point of data generation. This proximity drastically diminishes latency, optimizes data aggregation, and enhances overall resource utilization. Consequently, Edge Computing is uniquely positioned to address and potentially mitigate the limitations that have previously impeded the optimization of ITS functionalities. Nevertheless, the incorporation of Edge Computing into vehicular networks unveils a unique array of complexities, ranging from the strategic placement of edge servers and efficient data offloading techniques to the implementation of robust service migration protocols and safeguarding privacy and security measures.This thesis investigates the problems of edge server placement and service migration in vehicular networks. Our contributions in this thesis are threefold. First, we introduce "ESIAS," an Edge-based Safety Intersection Assistance System, specifically designed to improve safety intersections. The system aims to proactively distribute precise warning messages to drivers, mitigating the risk of common intersection-related accidents. Second, we tackle the challenge of optimal Edge server placement in vehicular networks, employing integer linear programming to find the most effective solutions. The methodology considers latency, cost, and server capacity in real-world traffic conditions. The proposed framework aims not only to minimize the overall deployment cost but also to balance the computational workloads among Edge servers, all while maintaining latency within acceptable thresholds. Finally, we delve into the complex issue of service migration in MEC-enabled vehicular networks, addressing the quandary of maintaining quality of service (QoS) while minimizing migration costs. As vehicles move through different regions, maintaining service quality requires strategic service migration, which poses challenges in terms of timing and location. To resolve this problem, we formulate it as a Markov Decision Process (MDP) and apply deep reinforcement learning techniques, specifically Deep Q-Networks (DQN), to discover optimal migration strategies tailored to each service's requirements. The resulting framework ensures seamless service continuity even within high-mobility constraints, achieving an optimal balance between latency and migration costs
Santoyo, González Alejandro. "Edge computing infrastructure for 5G networks: a placement optimization solution." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669552.
Fernandez-Rubiera, Francisco Jose. "Clitics at the edge clitic placement in Western Iberian Romance languages /." Connect to Electronic Thesis (CONTENTdm), 2009. http://worldcat.org/oclc/450998700/viewonline.
Schäfer, Dominik [Verfasser], and Christian [Akademischer Betreuer] Becker. "Elastic computation placement in edge-based environments / Dominik Schäfer ; Betreuer: Christian Becker." Mannheim : Universitätsbibliothek Mannheim, 2019. http://d-nb.info/1181692911/34.
Schäfer, Dominik Verfasser], and Christian [Akademischer Betreuer] [Becker. "Elastic computation placement in edge-based environments / Dominik Schäfer ; Betreuer: Christian Becker." Mannheim : Universitätsbibliothek Mannheim, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-488322.
POLTRONIERI, Filippo. "Value-of-Information Middlewares for Fog and Edge Computing." Doctoral thesis, Università degli studi di Ferrara, 2021. http://hdl.handle.net/11392/2488252.
Con i termini Fog ed Edge Computing si indicano dei paradigmi computazionali che, spostando l'elaborazione dei dati IoT nelle prossimità sia dei dispositivi che degli utenti, mirano a fornire servizi a bassa latenza, immersivi e real-time. Fog ed Edge Computing trovano applicazione in contesti Smart Cities, dove è possibile sfruttare la capacità computazionale di gateway IoT, Cloudlet e Base Station per elaborare parte dei dati generati dall'IoT direttamente ai margini della rete. L'adozione dei paradigmi di Fog ed Edge Computing è tuttavia complessa in quanto pone una serie di sfide tra cui il processamento dell’enorme mole di dati generati dall’IoT, la presenza di un numero limitato di dispositivi altamente eterogenei e con capacità computazionali scarse, requisiti di servizio altamente dinamici e reti con caratteristiche eterogenee. Per garantire i requisiti stringenti di bassa latenza, soluzioni per Fog ed Edge Computing devono essere in grado di utilizzare al meglio le scarse risorse a disposizione, gestendole al meglio. Se questi paradigmi sono oggetto di ampie ricerche, vi è la necessità di investigare soluzioni innovative che consentano di gestire l’enorme mole dati IoT e permettere una concreta applicazione di Fog ed Edge Computing. Questa tesi propone middleware innovativi in grado di fornire soluzioni complete per fronteggiare al meglio le caratteristiche altamente dinamiche di scenari Smart Cities, fornendo metodologie e strumenti per allocare e distribuire servizi tra le risorse a disposizione, monitorare lo stato delle risorse e modificare prontamente la loro configurazione. Come criterio innovativo per la prioritizzazione dei dati IoT per processamento e disseminazione, questa tesi adotta il concetto di Value-of-Information (VoI), nato come estensione della Teoria dell'Informazione di Shannon e applicato in ambiti decisionali. A tal fine, questa tesi propone politiche di gestione delle informazioni che consentono di realizzare servizi modulari e facilmente (ri-)componibili e tecniche di ottimizzazione innovative che ben si adattano a questi servizi. Inoltre, i middleware presentati in questa tesi integrano il concetto di VoI sia a livello di servizio che a livello di gestione per selezionare le informazioni più preziose per l'elaborazione e la diffusione, riducendo così il carico computazionale e garantendo una gestione ottimale dei dispositivi e della rete. Le ricerche presentate in questa tesi sono il risultato della collaborazione con istituti di ricerca internazionali e di un periodo di ricerca trascorso presso il Florida Institute for Human and Machine Cognition (IHMC), FL, USA.
Santi, Nina. "Prédiction des besoins pour la gestion de serveurs mobiles en périphérie." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILB050.
Multi-access Edge computing is an emerging paradigm within the Internet of Things (IoT) that complements Cloud computing. This paradigm proposes the implementation of computing servers located close to users, reducing the pressure and costs of local network infrastructure. This proximity to users is giving rise to new use cases, such as the deployment of mobile servers mounted on drones or robots, offering a cheaper, more energy-efficient and flexible alternative to fixed infrastructures for one-off or exceptional events. However, this approach also raises new challenges for the deployment and allocation of resources in terms of time and space, which are often battery-dependent.In this thesis, we propose predictive tools and algorithms for making decisions about the allocation of fixed and mobile resources, in terms of both time and space, within dynamic environments. We provide rich and reproducible datasets that reflect the heterogeneity inherent in Internet of Things (IoT) applications, while exhibiting a high rate of contention and interference. To achieve this, we are using the FIT-IoT Lab, an open testbed dedicated to the IoT, and we are making all the code available in an open manner. In addition, we have developed a tool for generating IoT traces in an automated and reproducible way. We use these datasets to train machine learning algorithms based on regression techniques to evaluate their ability to predict the throughput of IoT applications. In a similar approach, we have also trained and analysed a neural network of the temporal transformer type to predict several Quality of Service (QoS) metrics. In order to take into account the mobility of resources, we are generating IoT traces integrating mobile access points embedded in TurtleBot robots. These traces, which incorporate mobility, are used to validate and test a federated learning framework based on parsimonious temporal transformers. Finally, we propose a decentralised algorithm for predicting human population density by region, based on the use of a particle filter. We test and validate this algorithm using the Webots simulator in the context of servers embedded in robots, and the ns-3 simulator for the network part
Abderrahim, Mohamed. "Conception d’un système de supervision programmable et reconfigurable pour une infrastructure informatique et réseau répartie." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0119/document.
Cloud offers compute, storage and network as services. To reduce the offer cost, the operators tend to rely on centralized and massive infrastructures. However, such a configuration hinders the satisfaction of the latency and bandwidth requirements of new generation applications. The Edge aims to rise this challenge by relying on massively distributed resources. To satisfy the operators and the users of Edge, management services similar to the ones that made the success of Cloud should be designed. In this thesis, we focus on the monitoring service. We design a framework to establish a holistic monitoring service. This framework determines a peer-to-peer deployment architecture for the observation, processing, and exposition of measurements. It verifies that this architecture satisfies the functional and quality of service constraints of the users. For this purpose, it relies on a description of users requirement sand a description of the Edge infrastructure.The expression of these two elements can be unified with two languages offered by the Framework. The deployment architecture is determined with the aim of minimizing the compute and network footprint of the monitoring service. For this purpose, the functions are mutualized as much as possible among the different users. The tests we did showed the relevance of our proposal for reducing monitoring footprint with a gain of -28% for the compute and -24% for the network
Sténson, Carl. "Object Placement in AR without Occluding Artifacts in Reality." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211112.
Placering av virtuella objekt i Augumented Reality görs ofta utan att ta hänsyn till objekt i den fysiska miljön. Den här studien utreder hur placering kan göras med hänsyn till den fysiska miljön och dess objekt. Den behandlar enbart placering av objekt på vertikala ytor. För undersökningen utvecklas två prototyper som använder sig av kantigenkänning i foton samt en volymmetrisk representation av den fysiska miljön. I denna miljö föreslår prototyperna var placering av objekt kan ske. Den första prototypen analyserar varje triangel i den volymmetriska representationen av rummet, vilket visade sig vara krävande och med låg precision av lokaliseringen av objekt i miljön. Den andra prototypen analyserar de detekterade kanterna i fotona och projicerar dem till deras positioner i miljön. Vilket var något som visade sig hitta objekt i rummet med god precision samt snabbare än den första prototypen. Den andra prototypen lyckas med detta i en kontrollerad miljö. I en mer komplex och utmanande miljö kan problem uppstå. Placering av objekt i Augumented Reality med hänsyn till både en volymmetrisk och texturerad representation av en miljö kan uppnås. Placeringen kan då ske på ett mer naturligt sätt och därmed förstärka upplevelsen av att virtuella och verkliga objekt befinner sig i samma värld.
Shinde, Swapnil Sadashiv. "Radio Access Network Function Placement Algorithms in an Edge Computing Enabled C-RAN with Heterogeneous Slices Demands." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20063/.
Книги з теми "Placement de Serveurs Edge":
Fine, Janice. Worker centers: Organizing communities at the edge of the dream. Ithaca, N.Y: Cornell University Press, 2005.
Boles, Melanie. DsPIC33/PIC24 FRM, HRPWM with Fine Edge Placement. Microchip Technology Incorporated, 2019.
Jiang, Linda. DsPIC33/PIC24 FRM - HRPWM with Fine Edge Placement. Microchip Technology Incorporated, 2020.
Boles, Melanie. DsPIC33CH FRM, High-Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2017.
Jiang, Linda. DsPIC33C/PIC24 FRM, High-Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2018.
Boles, Melanie. DsPIC33/PIC24 FRM, High-Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2020.
Boles, Melanie. DsPIC33/PIC24 FRM, High Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2018.
Takenaka, Norio. DsPIC33/PIC24 FRM, High-Resolution PWM with Fine Edge Placement (KC). Microchip Technology Incorporated, 2019.
Fine, Janice. Worker Centers: Organizing Communities at the Edge of the Dream. ILR Press, 2006.
Fine, Janice. Worker Centers: Organizing Communities at the Edge of the Dream. ILR Press, 2006.
Частини книг з теми "Placement de Serveurs Edge":
Huang, Tao, Fengmei Chen, Shengjun Xue, Zheng Li, Yachong Tian, and Xianyi Cheng. "OPECE: Optimal Placement of Edge Servers in Cloud Environment." In Green, Pervasive, and Cloud Computing, 3–16. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9896-8_1.
Xu, Xiaolong, Yuan Xue, Lianyong Qi, Xuyun Zhang, Shaohua Wan, Wanchun Dou, and Victor Chang. "Load-Aware Edge Server Placement for Mobile Edge Computing in 5G Networks." In Service-Oriented Computing, 494–507. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33702-5_38.
Wang, Lijuan, Yingya Guo, Jiangyuan Yao, and Siyu Zhou. "SCESP: An Edge Server Placement Method Based on Spectral Clustering in Mobile Edge Computing." In Advances in Artificial Intelligence and Security, 527–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06761-7_42.
Zhang, Chaoyue, Bin Lin, Lin X. Cai, Liping Qian, Yuan Wu, and Shuang Qi. "Joint Edge Server Deployment and Service Placement for Edge Computing-Enabled Maritime Internet of Things." In Wireless Algorithms, Systems, and Applications, 541–53. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19211-1_44.
Guo, Feiyan, Bing Tang, Linyao Kang, and Li Zhang. "Mobile Edge Server Placement Based on Bionic Swarm Intelligent Optimization Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 95–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67540-0_6.
Moorthy, Rajalakshmi Shenbaga, K. S. Arikumar, and B. Sahaya Beni Prathiba. "An Improved Whale Optimization Algorithm for Optimal Placement of Edge Server." In Lecture Notes in Networks and Systems, 89–100. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_8.
Yan, Xuan, Zhanyang Xu, Mohammad R. Khosravi, Lianyong Qi, and Xiaolong Xu. "An NPGA-II-Based Multi-objective Edge Server Placement Strategy for IoV." In Advances in Parallel & Distributed Processing, and Applications, 541–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69984-0_39.
Zhang, Xing, Jielin Jiang, Lianyong Qi, and Xiaolong Xu. "An Edge Server Placement Method with Cyber-Physical-Social Systems in 5G." In Simulation Tools and Techniques, 127–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72795-6_11.
Hu, Haiquan, Jifu Chen, and Chengying Mao. "HR-kESP: A Heuristic Algorithm for Robustness-Oriented k Edge Server Placement." In Algorithms and Architectures for Parallel Processing, 17–33. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0862-8_2.
Bakshi, Mohana, Moumita Roy, Ujjwal Maulik, and Chandreyee Chowdhury. "An Optimal Edge Server Placement Algorithm Based on Glowworm Swarm Optimization Technique." In Proceedings of 4th International Conference on Frontiers in Computing and Systems, 3–12. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2614-1_1.
Тези доповідей конференцій з теми "Placement de Serveurs Edge":
Li, Yuanzhe, and Shangguang Wang. "An Energy-Aware Edge Server Placement Algorithm in Mobile Edge Computing." In 2018 IEEE International Conference on Edge Computing (EDGE). IEEE, 2018. http://dx.doi.org/10.1109/edge.2018.00016.
Wang, Futian, Xingxiang Huang, Hongfang Nian, Qiang He, Yun Yang, and Cheng Zhang. "Cost-Effective Edge Server Placement in Edge Computing." In ICSCC 2019: 2019 5th International Conference on Systems, Control and Communications. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377458.3377461.
Liu, Haotian, Shiyun Wang, Hui Huang, and Qiang Ye. "On the Placement of Edge Servers in Mobile Edge Computing." In 2023 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2023. http://dx.doi.org/10.1109/icnc57223.2023.10074304.
Cui, Guangming, Qiang He, Xiaoyu Xia, Feifei Chen, Hai Jin, and Yun Yang. "Robustness-oriented k Edge Server Placement." In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE, 2020. http://dx.doi.org/10.1109/ccgrid49817.2020.00-85.
Lu, Dongyu, Yuben Qu, Fan Wu, Haipeng Dai, Chao Dong, and Guihai Chen. "Robust Server Placement for Edge Computing." In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2020. http://dx.doi.org/10.1109/ipdps47924.2020.00038.
Chen, Xiao, Wei Liu, Jing Chen, and Jin Zhou. "An Edge Server Placement Algorithm in Edge Computing Environment." In 2020 12th International Conference on Advanced Infocomm Technology (ICAIT). IEEE, 2020. http://dx.doi.org/10.1109/icait51223.2020.9315526.
Gong, Yadong. "Optimal Edge Server and Service Placement in Mobile Edge Computing." In 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2020. http://dx.doi.org/10.1109/itaic49862.2020.9339180.
Takeda, Ayaka, Tomotaka Kimura, and Kouji Hirata. "Evaluation of edge cloud server placement for edge computing environments." In 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2019. http://dx.doi.org/10.1109/icce-tw46550.2019.8991970.
Li, Wenyao, Jingduo Zhang, and Zhijie Han. "Workload balance-aware edge server placement in mobile edge computing." In 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), edited by Paulo Batista and Yudong Zhang. SPIE, 2023. http://dx.doi.org/10.1117/12.2692001.
Zheng, Danyang, Chengzong Peng, and Xiaojun Cao. "On the Placement of Edge Server for Mobile Edge Computing." In 2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021. http://dx.doi.org/10.1109/iccc54389.2021.9674609.
Звіти організацій з теми "Placement de Serveurs Edge":
Ginzel. L51748 Detection of Stress Corrosion Induced Toe Cracks-Advancement of the Developed Technique. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 1996. http://dx.doi.org/10.55274/r0010659.
Briggs, Nicholas E., Robert Bailey Bond, and Jerome F. Hajjar. Cyclic Behavior of Steel Headed Stud Anchors in Concrete-filled Steel Deck Diaphragms through Push-out Tests. Northeastern University. Department of Civil and Environmental Engineering., February 2023. http://dx.doi.org/10.17760/d20476962.