Relevant bibliographies by topics / Edge server placement

Academic literature on the topic 'Edge server placement'

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Published: 7 July 2024

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Contents

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

Journal articles on the topic "Edge server placement":

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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.

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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.
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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.

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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.
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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.

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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.

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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.

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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.

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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.

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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.
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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.

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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.
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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.

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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.
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Lähderanta, Tero, Teemu Leppänen, Leena Ruha, Lauri Lovén, Erkki Harjula, Mika Ylianttila, Jukka Riekki, and Mikko J. Sillanpää. "Edge computing server placement with capacitated location allocation." Journal of Parallel and Distributed Computing 153 (July 2021): 130–49. http://dx.doi.org/10.1016/j.jpdc.2021.03.007.

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Journal articles

Dissertations / Theses on the topic "Edge server placement":

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

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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

Gupta, Devyani. "Optimal Placement and Traffic Steering of VNFs and Edge Servers using Column Generation in Data Center Networks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5974.

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Telecom Service Providers (TSPs) were traditionally dependent on physical devices to provide end-to-end communication. The services provided were high quality and stable but low in agility and hardware-dependent. As the demand for quick deployment of diverse services increased, TSP-s needed much higher flexibility and agility. This is how Network Functions Virtualization (NFV) came into being. NFV is the concept of replacing dedicated hardware with commercial-off-the-shelf (COTS) servers. It decouples the physical hardware and the function running on it. A network function can be dispatched as an instance of the software, called a Virtual Network Function (VNF). Thus, a service can be decomposed into several VNFs that can be run on industry-standard physical servers. The optimal placement of these VNFs is a potential question for TSPs to reduce the overall cost. We first study a network operations problem where we optimally deploy VNFs in Service Chains (SCs) such that the maximum consumed bandwidth across network links is minimized. The network parameters (link bandwidths, compute capacities of nodes, link propagation delays, etc.) and the number of SCs are known a priori. The problem formulated is a large Mixed-Integer Linear Program (MILP). We use the Column Generation (CG) technique to solve the problem optimally. Through various examples, we show the power of CG. We compare our results with recent heuristics and demonstrate that our approach performs better as it gives exact optimal solutions quickly. Second, we extend our previous setup to the online case where the number of SCs is not known a priori. We serve SC requests as they come. A new SC is implemented on the "residual network" while the previously deployed SCs are undisturbed. The problem formulated is a large MILP, and we use CG as the solution technique. The results show the percentage improvement in the solutions over those obtained using heuristics. Next, we study a network design problem in an Edge Computing Environment. A general communication network has a single Data Center (DC) in its "core," which serves as a gateway to the Internet. For delay-constrained services of the kind needed by online gaming, this model does not suffice because the propagation delay between the subscriber and the DC may be too high. This requires some servers to be located close to the network edge. Thus, the question of the optimal placement of these edge servers arises. To lower the network design cost, it is also essential to ensure good traffic routing, so that aggregate traffic on each link remains as low as possible. This enables lower capacity assignment on each link and thereby minimizes design cost. We study a novel joint optimization problem of network design cost minimization. Edge server placement cost and link capacity assignment cost constitute the total cost. The problem formulated is a large MILP, and we again use CG to solve it. We compare our results with many heuristics and show the improvement in design cost. Finally, we extend the above work by relaxing some assumptions and constraints. Unlike previously, we consider servers with different capacities. Also, a server can serve more than one request depending on its core capabilities. We also consider the split-and-merge of an SC through various paths in the network. The formulated problem can also provide the minimum number of servers to be used. Again, the formulation is a large MILP, and CG is used to solve it exactly.

Book chapters on the topic "Edge server placement":

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Conference papers on the topic "Edge server placement":

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Yeom, Sungwoong, Shivani Sanjay Kolekar, and Kyungbaek Kim. "Effective Edge Server Placement for Efficient Federated Clustering." In 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2022. http://dx.doi.org/10.23919/apnoms56106.2022.9919936.

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Reports on the topic "Edge server placement":

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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.

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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
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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.

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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.
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Briggs, Nicholas E., and Jerome F. Hajjar. Cyclic Seismic Behavior of Concrete-filled Steel Deck Diaphragms. Department of Civil and Environmental Engineering, Northeastern University, September 2023. http://dx.doi.org/10.17760/d20593269.

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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 regarding 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. Concrete-filled steel deck diaphragms are ubiquitously used in steel construction worldwide due to the ease of construction and cost-effective use of material. This report first presents a series of concrete-filled steel deck push-out tests that explores the effect of cyclic loading on the strength of steel headed stud anchors. The effect that cyclic loading has on structural performance is explored across different concrete densities, steel headed stud anchor placements and groupings, steel deck orientations, and edge conditions. As compared to prior tests, the push-out tests conducted in this work included four rows of studs along the length rather than the typical two rows, and an ability to impose cyclic loading. This provided novel insight into force flows, failure mechanisms, and load distribution between studs and stud groups. Most of the specimens also used lightweight concrete, as is common in high seismic zones.Secondly, this report describes a full-scale experimental concrete-filled steel deck diaphragm specimen which explored the cyclic behavior and capacity of this structural system. This experiment builds on previously reported experimental studies. This specimen demonstrated force distribution and flows in an indeterminant floor system and captured realistic boundary conditions and construction practices that affect the performance of this system in building structures. The results showed that concrete-filled steel deck diaphragms fail as expected and may have significant overstrength. Furthermore, a finite element framework is presented that can simulate cyclic fracture through the use of a high-fidelity steel material model. This framework was used and validated against nine experimental push-out specimens tested and documented as part of this research. The simulation capacity provides an avenue to further investigate this structural system through simulated parametric study.

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