Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.

The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and re...

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Autores principales:	Xuecong Zhang, Haolang Shen, Zhihan Lv
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3ee2056b7f5f4ef394e080032efae11b
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spelling	oai:doaj.org-article:3ee2056b7f5f4ef394e080032efae11b2021-11-25T06:23:35ZDeployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.1932-620310.1371/journal.pone.0252244https://doaj.org/article/3ee2056b7f5f4ef394e080032efae11b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252244https://doaj.org/toc/1932-6203The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user's task execution needs. A comparison with the latest algorithm also verifies the model's performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.Xuecong ZhangHaolang ShenZhihan LvPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252244 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuecong Zhang Haolang Shen Zhihan Lv Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
description	The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user's task execution needs. A comparison with the latest algorithm also verifies the model's performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.
format	article
author	Xuecong Zhang Haolang Shen Zhihan Lv
author_facet	Xuecong Zhang Haolang Shen Zhihan Lv
author_sort	Xuecong Zhang
title	Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
title_short	Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
title_full	Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
title_fullStr	Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
title_full_unstemmed	Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
title_sort	deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3ee2056b7f5f4ef394e080032efae11b
work_keys_str_mv	AT xuecongzhang deploymentoptimizationofmultistageinvestmentportfolioserviceandhybridintelligentalgorithmunderedgecomputing AT haolangshen deploymentoptimizationofmultistageinvestmentportfolioserviceandhybridintelligentalgorithmunderedgecomputing AT zhihanlv deploymentoptimizationofmultistageinvestmentportfolioserviceandhybridintelligentalgorithmunderedgecomputing
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Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.

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