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Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge se...

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Detalles Bibliográficos
Autores principales: Chen, Shichao, Li, Qijie, Zhou, Mengchu, Abusorrah, Abdullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865659/
https://www.ncbi.nlm.nih.gov/pubmed/33498910
http://dx.doi.org/10.3390/s21030779
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author Chen, Shichao
Li, Qijie
Zhou, Mengchu
Abusorrah, Abdullah
author_facet Chen, Shichao
Li, Qijie
Zhou, Mengchu
Abusorrah, Abdullah
author_sort Chen, Shichao
collection PubMed
description In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.
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spelling pubmed-78656592021-02-07 Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm Chen, Shichao Li, Qijie Zhou, Mengchu Abusorrah, Abdullah Sensors (Basel) Review In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated. MDPI 2021-01-24 /pmc/articles/PMC7865659/ /pubmed/33498910 http://dx.doi.org/10.3390/s21030779 Text en © 2021 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 Review
Chen, Shichao
Li, Qijie
Zhou, Mengchu
Abusorrah, Abdullah
Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
title Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
title_full Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
title_fullStr Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
title_full_unstemmed Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
title_short Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
title_sort recent advances in collaborative scheduling of computing tasks in an edge computing paradigm
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865659/
https://www.ncbi.nlm.nih.gov/pubmed/33498910
http://dx.doi.org/10.3390/s21030779
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