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Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

In the Fog Computer (FC), the process of data is prone to problems such as low data similarity and poor data tolerance. This paper proposes a hierarchical data job scheduling strategy Based on Intelligent Sensor-Cloud in Fog Computer (HDJS). HDJS dynamically adjusts the priority of the job to avoid...

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Detalles Bibliográficos
Autores principales: Sun, Zeyu, Li, Chuanfeng, Wei, Lili, Li, Zhixian, Min, Zhiyu, Zhao, Guozeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928845/
https://www.ncbi.nlm.nih.gov/pubmed/31766353
http://dx.doi.org/10.3390/s19235083
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author Sun, Zeyu
Li, Chuanfeng
Wei, Lili
Li, Zhixian
Min, Zhiyu
Zhao, Guozeng
author_facet Sun, Zeyu
Li, Chuanfeng
Wei, Lili
Li, Zhixian
Min, Zhiyu
Zhao, Guozeng
author_sort Sun, Zeyu
collection PubMed
description In the Fog Computer (FC), the process of data is prone to problems such as low data similarity and poor data tolerance. This paper proposes a hierarchical data job scheduling strategy Based on Intelligent Sensor-Cloud in Fog Computer (HDJS). HDJS dynamically adjusts the priority of the job to avoid job starvation and maximize the use of resources, uses the key frame to the resource occupied information, distributes the frame sequence to the unit, and then combines the intra frame distribution strategy to balance the load between the nodes. The experimental results show our proposed strategy may be possible to avoid the operation of hunger and resource fragmentation problems, make full use of the advantages of multi-core and multi-thread, improve system resource utilization, and shorten the execution time and response time.
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spelling pubmed-69288452019-12-26 Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy Sun, Zeyu Li, Chuanfeng Wei, Lili Li, Zhixian Min, Zhiyu Zhao, Guozeng Sensors (Basel) Article In the Fog Computer (FC), the process of data is prone to problems such as low data similarity and poor data tolerance. This paper proposes a hierarchical data job scheduling strategy Based on Intelligent Sensor-Cloud in Fog Computer (HDJS). HDJS dynamically adjusts the priority of the job to avoid job starvation and maximize the use of resources, uses the key frame to the resource occupied information, distributes the frame sequence to the unit, and then combines the intra frame distribution strategy to balance the load between the nodes. The experimental results show our proposed strategy may be possible to avoid the operation of hunger and resource fragmentation problems, make full use of the advantages of multi-core and multi-thread, improve system resource utilization, and shorten the execution time and response time. MDPI 2019-11-21 /pmc/articles/PMC6928845/ /pubmed/31766353 http://dx.doi.org/10.3390/s19235083 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
Sun, Zeyu
Li, Chuanfeng
Wei, Lili
Li, Zhixian
Min, Zhiyu
Zhao, Guozeng
Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy
title Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy
title_full Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy
title_fullStr Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy
title_full_unstemmed Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy
title_short Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy
title_sort intelligent sensor-cloud in fog computer: a novel hierarchical data job scheduling strategy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928845/
https://www.ncbi.nlm.nih.gov/pubmed/31766353
http://dx.doi.org/10.3390/s19235083
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