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Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing

Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve th...

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Detalles Bibliográficos
Autores principales: Li, Guangshun, Liu, Yuncui, Wu, Junhua, Lin, Dandan, Zhao, Shuaishuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539192/
https://www.ncbi.nlm.nih.gov/pubmed/31071923
http://dx.doi.org/10.3390/s19092122
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author Li, Guangshun
Liu, Yuncui
Wu, Junhua
Lin, Dandan
Zhao, Shuaishuai
author_facet Li, Guangshun
Liu, Yuncui
Wu, Junhua
Lin, Dandan
Zhao, Shuaishuai
author_sort Li, Guangshun
collection PubMed
description Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.
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spelling pubmed-65391922019-06-04 Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing Li, Guangshun Liu, Yuncui Wu, Junhua Lin, Dandan Zhao, Shuaishuai Sensors (Basel) Article Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling. MDPI 2019-05-08 /pmc/articles/PMC6539192/ /pubmed/31071923 http://dx.doi.org/10.3390/s19092122 Text en © 2019 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
Li, Guangshun
Liu, Yuncui
Wu, Junhua
Lin, Dandan
Zhao, Shuaishuai
Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing
title Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing
title_full Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing
title_fullStr Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing
title_full_unstemmed Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing
title_short Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing
title_sort methods of resource scheduling based on optimized fuzzy clustering in fog computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539192/
https://www.ncbi.nlm.nih.gov/pubmed/31071923
http://dx.doi.org/10.3390/s19092122
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