Journal articles: 'Placement de Serveurs Edge'

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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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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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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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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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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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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.

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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%.

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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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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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Kosta, Sokol, Francisco Airton Silva, Patricia Takako Endo, Daniel Carvalho, and Laécio Rodrigues. "Edge servers placement in mobile edge computing using stochastic Petri nets." International Journal of Computational Science and Engineering 23, no. 4 (2020): 352. http://dx.doi.org/10.1504/ijcse.2020.10035558.

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Carvalho, Daniel, Laécio Rodrigues, Patricia Takako Endo, Sokol Kosta, and Francisco Airton Silva. "Edge servers placement in mobile edge computing using stochastic Petri nets." International Journal of Computational Science and Engineering 23, no. 4 (2020): 352. http://dx.doi.org/10.1504/ijcse.2020.113181.

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Liu, Yanpei, Yanru Bin, Ningning Chen, and Shuaijie Zhu. "Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing." Applied Sciences 13, no. 16 (August 14, 2023): 9229. http://dx.doi.org/10.3390/app13169229.

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With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience.

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Zeng, Feng, Yongzheng Ren, Xiaoheng Deng, and Wenjia Li. "Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks." Sensors 19, no. 1 (December 21, 2018): 32. http://dx.doi.org/10.3390/s19010032.

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Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible.

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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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Fang, Juan, Kai Li, Juntao Hu, Xiaobin Xu, Ziyi Teng, and Wei Xiang. "SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing." Journal of Sensors 2021 (October 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/4758677.

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The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption.

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Cao, Kun, Liying Li, Yangguang Cui, Tongquan Wei, and Shiyan Hu. "Exploring Placement of Heterogeneous Edge Servers for Response Time Minimization in Mobile Edge-Cloud Computing." IEEE Transactions on Industrial Informatics 17, no. 1 (January 2021): 494–503. http://dx.doi.org/10.1109/tii.2020.2975897.

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Hou, Peng, Bo Li, Zongshan Wang, and Hongwei Ding. "Joint hierarchical placement and configuration of edge servers in C-V2X." Ad Hoc Networks 131 (June 2022): 102842. http://dx.doi.org/10.1016/j.adhoc.2022.102842.

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Nangu, Shota, Ayaka Takeda, Tomotaka Kimura, and Kouji Hirata. "Integrated Design of Edge Computing Systems with Edge Server Placement and Virtual Machine Allocation." IEEJ Transactions on Electronics, Information and Systems 141, no. 12 (December 1, 2021): 1321–30. http://dx.doi.org/10.1541/ieejeiss.141.1321.

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He, Zhenli, Kenli Li, and Keqin Li. "Cost-Efficient Server Configuration and Placement for Mobile Edge Computing." IEEE Transactions on Parallel and Distributed Systems 33, no. 9 (September 1, 2022): 2198–212. http://dx.doi.org/10.1109/tpds.2021.3135955.

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Shen, Bowen, Xiaolong Xu, Lianyong Qi, Xuyun Zhang, and Gautam Srivastava. "Dynamic server placement in edge computing toward Internet of Vehicles." Computer Communications 178 (October 2021): 114–23. http://dx.doi.org/10.1016/j.comcom.2021.07.021.

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Hadzic, Ilija, Yoshihisa Abe, and Hans Christian Woithe. "Server Placement and Selection for Edge Computing in the ePC." IEEE Transactions on Services Computing 12, no. 5 (September 1, 2019): 671–84. http://dx.doi.org/10.1109/tsc.2018.2850327.

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Cai, Chao, Bin Chen, Jiahui Qiu, Yanan Xu, Mengfei Li, and Yujia Yang. "Migratory Perception in Edge-Assisted Internet of Vehicles." Electronics 12, no. 17 (August 30, 2023): 3662. http://dx.doi.org/10.3390/electronics12173662.

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Autonomous driving technology heavily relies on the accurate perception of traffic environments, mainly through roadside cameras and LiDARs. Although several popular and robust 2D and 3D object detection methods exist, including R-CNN, YOLO, SSD, PointPillar, and VoxelNet, the perception range and accuracy of an individual vehicle can be limited by blocking from other vehicles or buildings. A solution is to harness roadside perception infrastructures for vehicle–infrastructure cooperative perception, using edge computing for real-time intermediate features extraction and V2X networks for transmitting these features to vehicles. This emerging migratory perception paradigm requires deploying exclusive cooperative perception services on edge servers and involves the migration of perception services to reduce response time. In such a setup, competition among multiple cooperative perception services exists due to limited edge resources. This study proposes a multi-agent reinforcement learning (MADRL)-based service scheduling method for migratory perception in vehicle–infrastructure cooperative perception, utilizing a discrete time-varying graph to model the relationship between service nodes and edge server nodes. This MADRL-based approach can efficiently address the challenges of service placement and migration in resource-limited environments, minimize latency, and maximize resource utilization for migratory perception services on edge servers.

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Yuan, Lyuzerui, Jie Gu, Jinghuan Ma, Honglin Wen, and Zhijian Jin. "Optimal Network Partition and Edge Server Placement for Distributed State Estimation." Journal of Modern Power Systems and Clean Energy 10, no. 6 (2022): 1637–47. http://dx.doi.org/10.35833/mpce.2021.000512.

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Parveen Shaik, Dr Sajeeda. "Strategic Placement of Servers in Mobile Cloud Computing: A Comprehensive Exploration of Edge Computing, Fog Computing, and Cloudlet Technologies." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 8, no. 4 (2020): 24–28. http://dx.doi.org/10.55083/irjeas.2020.v08i04009.

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Mobile Cloud Computing (MCC) has become integral to the advancement of mobile applications, necessitating strategic server placement for optimized performance. This review article explores three pivotal technologies—Edge Computing, Fog Computing, and Cloudlet Technologies—aimed at addressing the challenges posed by traditional cloud-centric models in MCC environments. The paper provides a thorough analysis of each approach, elucidating their architectural principles, benefits, and applications. Edge Computing’s proximity to end-users, Fog Computing’s intermediary role, and the localized Cloudlet Technologies are scrutinized. A comparative analysis offers insights into their strengths and limitations, aiding in determining their suitability based on diverse use cases. Real-world applications showcase the transformative impact of these technologies in enhancing mobile experiences across sectors such as healthcare, gaming, and more. The review concludes with a discussion on the challenges inherent in each strategy and proposes future research directions. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers navigating the dynamic landscape of strategic server placement in MCC, contributing to the optimization of performance and user experience in mobile applications and services.

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Lu, Yongling, Zhen Wang, Chengbo Hu, Ziquan Liu, and Xueqiong Zhu. "Edge Computing Server Placement Strategy Based on SPEA2 in Power Internet of Things." Security and Communication Networks 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/3810670.

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In order to meet the edge services placement demand for multiobjective optimization of Power Internet of Things, an edge services placement strategy based on an improved strength Pareto evolutionary algorithm (SPEA2) is proposed in this paper. Firstly, we model the delay, resource utilization, and energy consumption. Then, a multiobjective optimization is proposed. Finally, an enhanced genetic algorithm is used to derive the decision candidate set. Moreover, the optimal solution in the candidate set is selected to be utilized in the iteration of the multicriteria decision and the superior-inferior solution distance method. Numerical results and analysis show that the proposed strategy is more effective in reducing system delay, improving resource utilization, and saving energy consumption than the other two benchmark algorithms.

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Bu, Chao, Xinyang Zhang, Jianhui Lv, and Jinsong Wang. "Dynamic Detection and Placement for VSFs over Edge Computing Scenarios: An ACO-Based Approach." Security and Communication Networks 2022 (April 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/2151645.

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As an extension of cloud computing, the edge computing has become an important pattern to deal with novel service scenarios of the Internet of Everything (IoE), especially for the rapidly increasing different kinds of service requests from edge equipment. It is still a great challenge to satisfy the demands of delay-sensitive applications, so as to optimize the service provision delay for edge equipment under 5G. In this paper, by introducing virtualized service functions (VSFs) into edge computing pattern, the mechanism of service function detection and placement among multiple Edge Computing Servers (ECSs) is proposed. We firstly improve the Ant colony optimization (ACO) method to adapt to the situation that the service requests may frequently change from different edge network domains. Based on the improved ACO, a scheme of searching for the locations (i.e., ECSs) of the requested service functions is devised, so as to optimize the service searching delay. Then, a service function placement scheme is presented, and it deploys most of appropriate service functions in each ECS by predicting the future requested frequencies of functions, which further reduces the overall service provision delay. In addition, it also improves the ECS computing capacity utilization. The simulation results show that the proposed mechanism is feasible and effective.

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Li, Jiaqi, Yiqiang Sheng, and Haojiang Deng. "Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking." Applied Sciences 10, no. 10 (May 22, 2020): 3588. http://dx.doi.org/10.3390/app10103588.

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Information-centric networking (ICN) is an emerging network architecture that has the potential to address demands related to transmission latency and reliability in fifth-generation (5G) communication technology and the Internet of Things (IoT). As an essential component of ICN, name resolution provides the capability to translate identifiers into locators. Applications have different demands on name-resolution latency. To meet the demands, deploying name-resolution servers at the edge of the network by dividing it into multilayer overlay networks is effective. Moreover, optimization of the deployment of distributed name-resolution servers in such networks to minimize deployment costs is significant. In this paper, we first study the placement problem of the name-resolution server in ICN. Then, two algorithms called IIT-DOWN and IIT-UP are developed based on the heuristic ideas of inter-layer information transfer (IIT) and server reuse. They transfer server placement information and latency information between adjacent layers from different directions. Finally, experiments are conducted on both simulation networks and a real-world dataset. The experimental results reveal that the proposed algorithms outperform state-of-the-art algorithms such as the latency-aware hierarchical elastic area partitioning (LHP) algorithm in finding more cost-efficient solutions with a shorter execution time.

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Huang, Ping-Chun, Tai-Lin Chin, and Tzu-Yi Chuang. "Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks." IEEE Access 9 (2021): 138200–138208. http://dx.doi.org/10.1109/access.2021.3117870.

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Huang, Ping-Chun, Tai-Lin Chin, and Tzu-Yi Chuang. "Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks." IEEE Access 9 (2021): 138200–138208. http://dx.doi.org/10.1109/access.2021.3117870.

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Alakbarov, R. G. "Model of Optimal Placement of Cloudlets in a Wireless Metropolitan Area Network." Informacionnye Tehnologii 29, no. 4 (April 18, 2023): 182–88. http://dx.doi.org/10.17587/it.29.182-188.

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Cloud computing has recently emerged as a new paradigm for processing and storing large amounts of data. The rapid increase in the number of mobile phones and IoT devices benefitingfrom cloud computing services reduces the Internet bandwidth, resulting in delays in delivering data processed on remote cloud servers to the user. Mobile devices use edge computing systems (cloudlet, fog computing, etc.) to overcome resource shortages, power consumption and delays in communication channels. Edge computing systems place processing devices (cloudlets) close to users. The closer the cloudlets to mobile devices, the lower the processing time and energy consumption of the mobile device, and the higher the bandwidth of communication channels. Thus, cloudlet-based mobile computing clouds are widely used to reduce the latency in the Internet communication channels and energy consumption on mobile devices. This article identifies the most popular places for cloud servers in metropolitan mobile networks and discusses the optimal placement of a limited number of cloudlets in those places.

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Lu, Jiawei, Jielin Jiang, Venki Balasubramanian, Mohammad R. Khosravi, and Xiaolong Xu. "Deep reinforcement learning-based multi-objective edge server placement in Internet of Vehicles." Computer Communications 187 (April 2022): 172–80. http://dx.doi.org/10.1016/j.comcom.2022.02.011.

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Rui, Lanlan, Shuyun Wang, Zhili Wang, Ao Xiong, and Huiyong Liu. "A dynamic service migration strategy based on mobility prediction in edge computing." International Journal of Distributed Sensor Networks 17, no. 2 (February 2021): 155014772199340. http://dx.doi.org/10.1177/1550147721993403.

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Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user’s initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user’s location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user’s mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement.

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Li, Xingcun, Feng Zeng, Guanyun Fang, Yinan Huang, and Xunlin Tao. "Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks." IET Communications 14, no. 21 (December 2020): 3907–16. http://dx.doi.org/10.1049/iet-com.2020.0651.

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Liu, Chunyu, Heli Zhang, Xi Li, and Hong Ji. "Dynamic Rendering-Aware VR Service Module Placement Strategy in MEC Networks." Wireless Communications and Mobile Computing 2022 (August 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/1237619.

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Combining multiaccess edge computing (MEC) technology and wireless virtual reality (VR) game is a promising computing paradigm. Offloading the rendering tasks to the edge node can make up for the lack of computing resources of mobile devices. However, the current offloading works ignored that when rendering is enabled at the MEC server, the rendering operation depends heavily on the environment deployed on this MEC serve. In this paper, we propose a dynamically rendering-aware service module placement scheme for wireless VR games over the MEC networks. In this scheme, the rendering tasks of VR games are offloaded to the MEC server and closely coupled with service module placement. At the same time, to further optimize the end-to-end latency of VR video delivery, the routing delay of the rendered VR video stream and the costs of the service module migration are jointly considered with the proposed placement scheme. The goal of this scheme is to minimize the sum of the network costs over a long time under satisfying the delay constraint of each player. We model our strategy as a high-order, nonconvex, and time-varying function. To solve this problem, we transform the placement problem into the min-cut problem by constructing a series of auxiliary graphs. Then, we propose a two-stage iterative algorithm based on convex optimization and graphs theory to solve our object function. Finally, extensive simulation results show that our proposed algorithm can ensure low end-to-end latency for players and low network costs over the other baseline algorithms.

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Khoshkholghi, Mohammad Ali, Michel Gokan Khan, Kyoomars Alizadeh Noghani, Javid Taheri, Deval Bhamare, Andreas Kassler, Zhengzhe Xiang, Shuiguang Deng, and Xiaoxian Yang. "Service Function Chain Placement for Joint Cost and Latency Optimization." Mobile Networks and Applications 25, no. 6 (November 21, 2020): 2191–205. http://dx.doi.org/10.1007/s11036-020-01661-w.

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AbstractNetwork Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

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Son, Min-Sik, Sang-Hwa Chung, and Won-Suk Kim. "Fog-Server Placement Technique Based on Network Edge Area Traffic for a Fog-Computing Environment." Journal of KIISE 45, no. 6 (June 30, 2018): 598–610. http://dx.doi.org/10.5626/jok.2018.45.6.598.

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Son, A.-Young, and Eui-Nam Huh. "Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing." Applied Sciences 9, no. 17 (August 29, 2019): 3550. http://dx.doi.org/10.3390/app9173550.

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With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

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Asghari, Ali, and Mohammad Karim Sohrabi. "Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet." Computer Science Review 51 (February 2024): 100616. http://dx.doi.org/10.1016/j.cosrev.2023.100616.

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Sulieman, Nour Alhuda, Lorenzo Ricciardi Celsi, Wei Li, Albert Zomaya, and Massimo Villari. "Edge-Oriented Computing: A Survey on Research and Use Cases." Energies 15, no. 2 (January 10, 2022): 452. http://dx.doi.org/10.3390/en15020452.

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Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context of edge computing architectures, thus proposing future directions to be investigated.

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Peng, Kai, Victor C. M. Leung, Xiaolong Xu, Lixin Zheng, Jiabin Wang, and Qingjia Huang. "A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision." Wireless Communications and Mobile Computing 2018 (October 10, 2018): 1–16. http://dx.doi.org/10.1155/2018/8267838.

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Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

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Satish Kumar Mahariya, Awaneesh Kumar, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Bhekisipho Twala, Mohammed Ismail Iqbal, and Neeraj Priyadarshi. "Smart Campus 4.0: Digitalization of University Campus with Assimilation of Industry 4.0 for Innovation and Sustainability." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 1 (August 19, 2023): 120–38. http://dx.doi.org/10.37934/araset.32.1.120138.

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According to the United Nations, global sustainability in terms of social, economic, and environmental issues must be achieved by 2030. SDGs 4 and 9 are related to education and strengthen the attainment of quality education and infrastructure innovation. Resilient infrastructure plays a significant role in strengthening the campus in terms of education, management, placement and environment. These all aspects come under the smart campus. Smart campus 4.0 is the amalgamation of multitude industry 4.0 enabling technologies for delivering smart and innovative facilities with the aspect of sustainability. The previous studies have proved that the sustainable development goals (SDGs) can be achieved with the amalgamation of industry 4.0 enabling technologies in the campus such as cloud computing, artificial intelligence (AI), Internet of things (IoT), edge/fog computing, blockchain, robot process automation (RPA), drones, augmented reality (AR), virtual reality (VR), big data, digital twin, and metaverse. The main objective of this study to provide the detailed discussion of all industry 4.0 enabling technologies in single research related to smart campus. The findings observed are IoT-Based Drone system is intended to ground patrolling, and a cloud server to develop a smart campus energy monitoring system. AI for campus placement prediction model; cloud and Edge computing architecture to build an intelligent air-quality monitoring system. The novelty of the study, it has discussed all industry 4.0 enabling technologies for a smart campus with challenges, recommendations, and future directions.

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Zhang, Xinglin, Zhenjiang Li, Chang Lai, and Junna Zhang. "Joint Edge Server Placement and Service Placement in Mobile Edge Computing." IEEE Internet of Things Journal, 2021, 1. http://dx.doi.org/10.1109/jiot.2021.3125957.

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Li, Yuanzhe, Ao Zhou, Xiao Ma, and Shangguang Wang. "Profit-aware Edge Server Placement." IEEE Internet of Things Journal, 2021, 1. http://dx.doi.org/10.1109/jiot.2021.3082898.

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Ali, Iman Mudhafar, and Mustafa Ismael Salman. "SDN-assisted Service Placement for the IoT-based Systems in Multiple Edge Servers Environment." Iraqi Journal of Science, June 27, 2020, 1525–40. http://dx.doi.org/10.24996/ijs.2020.61.6.32.

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Edge computing is proved to be an effective solution for the Internet of Things (IoT)-based systems. Bringing the resources closer to the end devices has improved the performance of the networks and reduced the load on the cloud. On the other hand, edge computing has some constraints related to the amount of the resources available on the edge servers, which is considered to be limited as compared with the cloud. In this paper, we propose Software-Defined Networking (SDN)-based resources allocation and service placement system in the multi-edge networks that serve multiple IoT applications. In this system, the resources of the edge servers are monitored using the proposed Edge Server Application (ESA) to determine the state of the edge server and, therefore, the acceptable services by each server. Benefiting from the information gathered by ESA, the service offloading decision would be taken by the proposed SDN Non-core Application (SNA) in a way that ensures an efficient load distribution and better resources utilization for the edge servers. A Weighted Aggregated Sum Product Assessment Method (WASPAS) was used to determine the best edge server. The proposed system was compared with a non-SDN system and showed improvement in the performance and the utilization of resources of the edge servers. Furthermore, the request handling time was considerably reduced and settled in constant rates for a different number of devices.

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Chen, Yuanyi, Dezhi Wang, Nailong Wu, and Zhengzhe Xiang. "Mobility-aware edge server placement for mobile edge computing." Computer Communications, June 2023. http://dx.doi.org/10.1016/j.comcom.2023.06.001.

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Zhang, Xinglin, Jinyi Zhang, Chaoqun Peng, and Xiumin Wang. "Multimodal Optimization of Edge Server Placement Considering System Response Time." ACM Transactions on Sensor Networks, May 9, 2022. http://dx.doi.org/10.1145/3534649.

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Mobile edge computing (MEC) deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study the multimodal optimization problem of edge server placement (MESP) with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.

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Li, Guopeng, Haisheng Tan, Liuyan Liu, Hao Zhou, Shaofeng H. C. Jiang, Zhenhua Han, Xiang-Yang Li, and Guoliang Chen. "DAG Scheduling in Mobile Edge Computing." ACM Transactions on Sensor Networks, August 16, 2023. http://dx.doi.org/10.1145/3616374.

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In Mobile Edge Computing, edge servers have limited storage and computing resources which can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc , a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc , an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24% . The number of deadlines that OnDoc can satisfy is at least 1.9 × of that of the baselines.

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Jasim, Ahmed M., and Hamed Al‐Raweshidy. "Optimal intelligent edge‐servers placement in the healthcare field." IET Networks, July 9, 2023. http://dx.doi.org/10.1049/ntw2.12097.

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Ye, Hongfan, Buqing Cao, Jianxun Liu, Pei Li, Bing Tang, and Zhenlian Peng. "An edge server deployment method based on optimal benefit and genetic algorithm." Journal of Cloud Computing 12, no. 1 (October 18, 2023). http://dx.doi.org/10.1186/s13677-023-00524-5.

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AbstractWith the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.

Journal articles on the topic 'Placement de Serveurs Edge'

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