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Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks

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 bee...

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Autores principales: Zeng, Feng, Ren, Yongzheng, Deng, Xiaoheng, Li, Wenjia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339009/
https://www.ncbi.nlm.nih.gov/pubmed/30577685
http://dx.doi.org/10.3390/s19010032
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author Zeng, Feng
Ren, Yongzheng
Deng, Xiaoheng
Li, Wenjia
author_facet Zeng, Feng
Ren, Yongzheng
Deng, Xiaoheng
Li, Wenjia
author_sort Zeng, Feng
collection PubMed
description 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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spelling pubmed-63390092019-01-23 Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks Zeng, Feng Ren, Yongzheng Deng, Xiaoheng Li, Wenjia Sensors (Basel) Article 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. MDPI 2018-12-21 /pmc/articles/PMC6339009/ /pubmed/30577685 http://dx.doi.org/10.3390/s19010032 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zeng, Feng
Ren, Yongzheng
Deng, Xiaoheng
Li, Wenjia
Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
title Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
title_full Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
title_fullStr Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
title_full_unstemmed Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
title_short Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
title_sort cost-effective edge server placement in wireless metropolitan area networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339009/
https://www.ncbi.nlm.nih.gov/pubmed/30577685
http://dx.doi.org/10.3390/s19010032
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