Relevant bibliographies by topics / Placement de Serveurs Edge

Academic literature on the topic 'Placement de Serveurs Edge'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Placement de Serveurs Edge.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Placement de Serveurs Edge":

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
With the rapid development of the Internet of Things, a large number of smart devices are being connected to the Internet while the data generated by these devices have put unprecedented pressure on existing network bandwidth and service operations. Edge computing, as a new paradigm, places servers at the edge of the network, effectively relieving bandwidth pressure and reducing delay caused by long-distance transmission. However, considering the high cost of deploying edge servers, as well as the waste of resources caused by the placement of idle servers or the degradation of service quality caused by resource conflicts, the placement strategy of edge servers has become a research hot spot. To solve this problem, an edge server placement method orienting service offloading in IoT called EPMOSO is proposed. In this method, Genetic Algorithm and Particle Swarm Optimization are combined to obtain a set of edge server placements strategies, and Simple Additive Weighting Method is utilized to determine the most balanced edge server placement, which is measured by minimum delay and energy consumption while achieving the load balance of edge servers. Multiple experiments are carried out, and results show that EPMOSO fulfills the multiobjective optimization with an acceptable convergence speed.
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through minimizing both communication delay and load difference as the optimization goal. Then, an Edge Server placement based on meta-Heuristic alGorithM (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom’s real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches.
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04–44.99%.
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be reduced. The capital investment cost is limited for edge resource providers, so the deployment of edge servers needs to consider the trade-off between user experience and capital investment cost. In our study, for edge server deployment problems, we summarize three critical issues: edge location, user association, and capacity at edge locations through the research and analysis of edge resource allocation in a real edge computing environment. For these issues, this study considers the user distribution density (load density), determines a reasonable deployment location of edge servers, and deploys an appropriate number of edge computing nodes in this location to improve resource utilization and minimize the deployment cost of edge servers. Based on the objective minimization function of construction cost and total access delay cost, we formulate the edge server placement as a mixed-integer nonlinear programming problem (MINP) and then propose an edge server deployment optimization algorithm to seek the optimal solution (named Benders_SD). Extensive simulations and comparisons with the other three existing deployment methods show that our proposed method achieved an intended performance. It not only meets the low latency requirements of users but also reduces the deployment cost.
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Placement de Serveurs Edge' for particular source types:
Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Placement de Serveurs Edge":

1

Khamari, Sabri. "Architectures et protocoles pour les véhicules connectés." Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0483.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
L'avènement des Systèmes de Transport Intelligents (STI) marque un changement de paradigme dans l'approche de la gestion et de l'optimisation des infrastructures de transport. Ancrés dans l'intégration des technologies de communication de pointe, les STI englobent une variété d'applications visant à améliorer la sécurité routière, l'efficacité du trafic et le confort de conduite. Cependant, l'exécution de ces applications de plus en plus gourmandes en calcul pose des défis inhérents liés à la latence, au traitement des données, et à la continuité des services. L'émergence de l'Edge Computing se présente comme une avancée transformatrice prête à redéfinir l'efficacité des applications véhiculaires dans les Systèmes de Transport Intelligents (STI). En contraste avec les paradigmes conventionnels de Cloud Computing, qui rencontrent fréquemment des problèmes de latence attribuables à la nature distante du traitement des données, l'Edge Computing décentralise les tâches computationnelles pour être plus proche du point de génération des données. Cette proximité réduit drastiquement la latence, optimise l'agrégation des données, et améliore l'utilisation globale des ressources. Par conséquent, l'Edge Computing est idéalement positionné pour adresser et potentiellement atténuer les limitations qui ont précédemment entravé l'optimisation des fonctionnalités des STI. Néanmoins, l'incorporation de l'Edge Computing dans les réseaux véhiculaires révèle un éventail unique de complexités, allant du placement stratégique des serveurs de bord et des techniques efficaces de déchargement de données à la mise en œuvre de protocoles robustes de migration de services et la sauvegarde des mesures de confidentialité et de sécurité.Cette thèse examine les problèmes de placement des serveurs Edge et de migration des services dans l'architecture de l’Edge Computing pour véhicules. Nos contributions dans cette thèse sont triples. Premièrement, nous introduisons "ESIAS", un Système d'Assistance de Sécurité à l'Intersection basé sur l'Edge, spécialement conçu pour améliorer la sécurité des intersections. Le système vise à distribuer proactivement des messages d'avertissement précis aux conducteurs, atténuant ainsi le risque d'accidents courants liés aux intersections. Deuxièmement, nous abordons le défi du placement optimal des serveurs en bordure dans les réseaux véhiculaires, en utilisant la programmation linéaire en nombres entiers pour trouver les solutions les plus efficaces. La méthodologie prend en compte la latence, le coût et la capacité des serveurs dans des conditions de trafic réelles. Le cadre proposé vise non seulement à minimiser le coût global de déploiement, mais aussi à équilibrer les charges de travail computationnelles entre les serveurs en bordure, tout en maintenant la latence dans des seuils acceptables. Enfin, nous nous plongeons dans la question complexe de la migration des services dans les réseaux véhiculaires, en abordant le dilemme du maintien de la qualité de service (QoS) tout en minimisant les coûts de migration. À mesure que les véhicules se déplacent à travers différentes régions, le maintien de la qualité du service nécessite une migration de service stratégique, qui pose des défis en termes de timing et de localisation. Pour résoudre ce problème, nous formulons le problème en tant que processus décisionnel de Markov (PDM) et appliquons des techniques d'apprentissage par renforcement profond, spécifiquement les Deep Q Networks (DQN), pour découvrir des stratégies de migration optimales adaptées aux exigences de chaque service. Le cadre résultant assure une continuité de service transparente, même dans des contraintes de haute mobilité, en réalisant un équilibre optimal entre la latence et les coûts de migration
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
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
This thesis focuses on how to optimize the placement of the Edge Computing infrastructure for upcoming 5G networks. To this aim, the core contributions of this research are twofold: 1) a novel heuristic called Hybrid Simulated Annealing to tackle the NP-hard nature of the problem and, 2) a framework called EdgeON providing a practical tool for real-life deployment optimization. In more detail, Edge Computing has grown into a key solution to 5G latency, reliability and scalability requirements. By bringing computing, storage and networking resources to the edge of the network, delay-sensitive applications, location-aware systems and upcoming real-time services leverage the benefits of a reduced physical and logical path between the end-user and the data or service host. Nevertheless, the edge node placement problem raises critical concerns regarding deployment and operational expenditures (i.e., mainly due to the number of nodes to be deployed), current backhaul network capabilities and non-technical placement limitations. Common approaches to the placement of edge nodes are based on: Mobile Edge Computing (MEC), where the processing capabilities are deployed at the Radio Access Network nodes and Facility Location Problem variations, where a simplistic cost function is used to determine where to optimally place the infrastructure. However, these methods typically lack the flexibility to be used for edge node placement under the strict technical requirements identified for 5G networks. They fail to place resources at the network edge for 5G ultra-dense networking environments in a network-aware manner. This doctoral thesis focuses on rigorously defining the Edge Node Placement Problem (ENPP) for 5G use cases and proposes a novel framework called EdgeON aiming at reducing the overall expenses when deploying and operating an Edge Computing network, taking into account the usage and characteristics of the in-place backhaul network and the strict requirements of a 5G-EC ecosystem. The developed framework implements several placement and optimization strategies thoroughly assessing its suitability to solve the network-aware ENPP. The core of the framework is an in-house developed heuristic called Hybrid Simulated Annealing (HSA), seeking to address the high complexity of the ENPP while avoiding the non-convergent behavior of other traditional heuristics (i.e., when applied to similar problems). The findings of this work validate our approach to solve the network-aware ENPP, the effectiveness of the heuristic proposed and the overall applicability of EdgeON. Thorough performance evaluations were conducted on the core placement solutions implemented revealing the superiority of HSA when compared to widely used heuristics and common edge placement approaches (i.e., a MEC-based strategy). Furthermore, the practicality of EdgeON was tested through two main case studies placing services and virtual network functions over the previously optimally placed edge nodes. Overall, our proposal is an easy-to-use, effective and fully extensible tool that can be used by operators seeking to optimize the placement of computing, storage and networking infrastructure at the users’ vicinity. Therefore, our main contributions not only set strong foundations towards a cost-effective deployment and operation of an Edge Computing network, but directly impact the feasibility of upcoming 5G services/use cases and the extensive existing research regarding the placement of services and even network service chains at the edge.
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Fog and Edge Computing aim to deliver low-latency, immersive, and powerful services by processing information close to both devices and users. This is well suited for IoT applications in Smart City, where IoT gateways, Cloudlets, Base Stations, and other computational nodes can process (part of) the data generated by the multitude of IoT sensors directly at the edge of the network. However, the implementation of Fog and Edge Computing is challenging because it requires to deal with a (limited number of) constrained devices, dynamic services' requirements, and heterogeneous network conditions. Differently from the Cloud, where computational resources are supposed to be unlimited, Fog and Edge services should be capable to adapt to scarce and constrained resources and deal with the deluge of IoT data. To facilitate the adoption of Fog and Edge Computing this thesis proposes innovative middlewares capable of providing comprehensive solutions to address the highly dynamic characteristics of these environments. These middlewares provide functions to allocate and distribute Fog and Edge services among the available computational devices, monitor the status of the environment, and promptly modify their configuration. To deal with the IoT data deluge this thesis investigates the interesting criterion of Value-of-Information (VoI). Originally born as an extension of Shannon's Information Theory for decision making science, researchers have studied VoI as an information management tool to select and prioritize information processing and dissemination. For this purpose, this thesis proposes the adoption of information management policies allowing the definition of service components, composable software modules that can be chained to create larger and more complex services. In addition, the middlewares presented in this thesis leverage the promising concept of VoI to select only the most valuable piece of information for processing and dissemination and to scale computational workload in an automated and lossiness fashion. This would enable to reduce the computational and network load and to propose innovative methodologies to optimize the available resources. The research efforts presented in this thesis are the results of the collaboration with international institutes and a research period at the Florida Institute for Human and Machine Cognition (IHMC), FL, USA.
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.
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
L'informatique en périphérie est un paradigme émergent au sein de l'Internet des Objets (IoT) et complémentaire à l'informatique en nuage. Ce paradigme propose l'implémentation de serveurs de calcul situés à proximité des utilisateurs, réduisant ainsi la pression et les coûts de l'infrastructure réseau locale. La proximité avec les utilisateurs suscite de nouveaux cas d'utilisation, tels que le déploiement de serveurs mobiles embarqués sur des drones ou des robots, offrant une alternative moins coûteuse, plus éco-énergétique et flexible par rapport aux infrastructures fixes lors d'événements ponctuels ou exceptionnels. Cependant, cette approche soulève également de nouveaux enjeux pour le déploiement et l'allocation de ressources en temps et en espace, souvent dépendants de la batterie.Dans le cadre de cette thèse, nous proposons des outils et des algorithmes de prédiction pour la prise de décision concernant l'allocation de ressources fixes et mobiles, à la fois en termes de temps et d'espace, au sein d'environnements dynamiques. Nous mettons à disposition des jeux de données riches et reproductibles qui reflètent l'hétérogénéité inhérente aux applications de l'Internet des Objets (IoT), tout en présentant un taux de contention et d'interférence élevé. Pour cela, nous utilisons le FIT-IoT Lab, un banc d'essai ouvert dédié à l'IoT, et nous mettons l'ensemble du code à disposition de manière ouverte. De plus, nous avons développé un outil permettant de générer de manière automatisée et reproductible des traces de l'IoT. Nous exploitons ces jeux de données pour entraîner des algorithmes d'apprentissage automatique basés sur des techniques de régression afin de les évaluer dans leur capacité à prédire le débit des applications de l'IoT. Dans une démarche similaire, nous avons également entraîné et analysé un réseau neuronal de type transformateur temporel pour prédire plusieurs métriques de la Qualité de Service (QoS). Afin de tenir compte de la mobilité des ressources, nous générons des traces de l'IoT intégrant des points d'accès mobiles embarqués sur des robots TurtleBot. Ces traces, qui intègrent la mobilité, sont utilisées pour valider et tester un framework d'apprentissage fédéré reposant sur des transformateurs temporels parcimonieux. Enfin, nous proposons un algorithme décentralisé de prédiction de la densité de la population humaine par régions, basé sur l'utilisation d'un filtre à particules. Nous testons et validons cet algorithme à l'aide du simulateur Webots dans un contexte de serveurs embarqués sur des robots, et du simulateur ns-3 pour la partie réseaux
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
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Le Cloud offre le calcul, stockage etréseau en tant que services. Pour réduire le coûtde cette offre, les opérateurs ont tendance à s’appuyer sur des infrastructures centralisées et gigantesques. Cependant, cette configuration entrave la satisfaction des exigences de latence et de bande passante des applications de nouvelle génération. L'Edge cherche à relever ce défi en s'appuyant sur des ressources massivement distribuées. Afin de satisfaire les attentes des opérateurs et des utilisateurs du Edge, des services de gestion ayant des capacités similaires à celles qui ont permis le succès du Cloud doivent être conçus. Dans cette thèse, nous nous concentrons sur le service de supervision. Nous proposons un canevas logiciel pour la mise en place d’un service holistique. Ce canevas permet de déterminer une architecture de déploiement pair-à-pair pour les fonctions d'observation, de traitement et d'exposition des mesures. Il vérifie que cette architecture satisfait les exigences fonctionnelles et de qualité de service des utilisateurs. Ces derniers peuvent être exprimés à l'aide d'un langage de description offert par le canevas. Le canevas offre également un langage de description pour unifier la description de l'infrastructure Edge. L’architecture de déploiement est déterminée avec l’objectif de minimiser l'empreinte de calcul et réseau du service de supervision. Pour cela, les fonctions de supervision sont mutualisées entre les différents utilisateurs. Les tests que nous avons faits ont montré la capacité de notre proposition à réduire l'empreinte de supervision avec un gain qui atteint -28% pour le calcul et -24% pour leréseau
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
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Placement of virtual objects in Augmented Reality is often done without regarding the artifacts in the physical environment. This thesis investigates how placement can be done with the artifacts included. It only considers placement of wall mounted objects. Through the development of two prototypes, using detected edges in RGB-images in combination with volumetric properties to identify the artifacts, arreas will be suggested for placement of virtual objects. The first prototype analyze each triangle in the model, which is an intensive and with low precision on the localization of the physical artifacts. The second prototype analyzed the detected RGB-edges in world space, which proved to detect the features with precise localization and a reduce calculation time. The second prototype manages this in a controlled setting. However, a more challenging environment would possibly pose other issues. In conclusion, placement in relation to volumetric and edge information from images in the environment is possible and could enhance the experience of being in a mixed reality, where physical and virtual objects coexist in the same world.
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.
10

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/.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Network slicing provides a scalable and flexible solution for resource allocation with performance guaranty and isolation from other services in the 5G architecture. 5G has to handle several active use cases with different requirements. The single solution to satisfy all the extreme requirements requires overspecifies and high-cost network architecture. Further, to fulfill the diverse requirements, each service will require different resources from a radio access network (RAN), edge, and central offices of 5G architecture and hence various deployment options. Network function virtualization allocates radio access network (RAN) functions in different nodes. URLLC services require function placement nearer to the ran to fulfill the lower latency requirement while eMBB require cloud access for implementation. Therefore arbitrary allocation of network function for different services is not possible. We aim to developed algorithms to find service-based placement for RAN functions in a multitenant environment with heterogeneous demands. We considered three generic classes of slices of eMBB, URLLC, mMTC. Every slice is characterized by some specific requirements, while the nodes and the links are resources constrained. The function placement problem corresponds to minimize the overall cost of allocating the different functions to the different nodes organized in layers for respecting the requirements of the given slices. Specifically, we proposed three algorithms based on the normalized preference associated with each slice on different layers of RAN architecture. The maximum preference algorithm places the functions on the most preferred position defined in the preference matrix. On the other hand, the proposed modified preference algorithm provides solutions by keeping track of the availability of computational resources and latency requirements of different services. We also used the Exhaustive Search Method for solving a function allocation problem.
More sources

Books on the topic "Placement de Serveurs Edge":

1

Fine, Janice. Worker centers: Organizing communities at the edge of the dream. Ithaca, N.Y: Cornell University Press, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Boles, Melanie. DsPIC33/PIC24 FRM, HRPWM with Fine Edge Placement. Microchip Technology Incorporated, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Jiang, Linda. DsPIC33/PIC24 FRM - HRPWM with Fine Edge Placement. Microchip Technology Incorporated, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Boles, Melanie. DsPIC33CH FRM, High-Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2017.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jiang, Linda. DsPIC33C/PIC24 FRM, High-Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Boles, Melanie. DsPIC33/PIC24 FRM, High-Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Boles, Melanie. DsPIC33/PIC24 FRM, High Resolution PWM with Fine Edge Placement. Microchip Technology Incorporated, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Takenaka, Norio. DsPIC33/PIC24 FRM, High-Resolution PWM with Fine Edge Placement (KC). Microchip Technology Incorporated, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Fine, Janice. Worker Centers: Organizing Communities at the Edge of the Dream. ILR Press, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Fine, Janice. Worker Centers: Organizing Communities at the Edge of the Dream. ILR Press, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Placement de Serveurs Edge":

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Placement de Serveurs Edge":

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Placement de Serveurs Edge":

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
In the past few years an ongoing problem has existed with stress corrosion cracking (SCC) in pipelines around the world. Several member companies of the Pipeline Research Council International, Inc. have experienced multiple incidents as a result of ERW defects and SCC. TCPL is running a series of hydrostatic tests and trial digs to identify the most severely affected areas. These excavations and failure studies have ascertained that most of the SCC causing failure has been on the outside diameter of long seam welded pipe at the edge of the weld. Defects at that location are known as "Toe-Cracks" Ginzel has developed an ultrasonic inspection technique that will detect both SCC colonies and toe cracks in long seam pipe. The main design objective for this research project was the selection and placement of ultrasonic transducers to combine weld, plate thickness and lamination inspection, along with SCC detection and sizing. Examination of sample pipe sections to demonstrate its success is reported. The primary stages for this research project are: �Assemble test equipment Establish test procedure System trials and data collection Evaluation of system performance and collected data Correlation of test data - Results
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Earthquake disasters in the United States account for $6.1 billion of economic losses each year, much of which is directly linked to infrastructure damage. These natural disasters are unpredictable and represent one of the most difficult design problems in regard to constructing resilient infrastructure. Structural floor and roof diaphragms act as the horizontal portion of the lateral force resisting system (LFRS), distributing the seismically derived inertial loads out from the heavy concrete slabs to the vertical LFRS. Composite concrete-filled steel deck floor and roof diaphragms are ubiquitously used in commercial construction worldwide due to the ease of construction and cost-effective use of structural material. This report presents a series of composite steel deck diaphragm Push-out tests at full scale that explore the effect that cyclic loading has on the strength of steel headed stud anchors. The effect that cyclic loading has on structural performance is explored across the variation of material and geometric parameters in the Push-out specimens, such as concrete density, steel headed stud anchor placement and grouping, steel deck orientation, and edge conditions. As compared to prior tests in the literature, the push-out tests conducted in this work have an extended specimen length that includes four rows of studs along the length rather than the typical two rows of studs, and an ability to impose cyclic loading. This provides novel insight into force flows in the specimens, failure mechanisms, and load distribution between studs and stud groups.

To the bibliography