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

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          Abstract

          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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          Most cited references72

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          Edge Computing: Vision and Challenges

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            Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

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              Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                24 January 2021
                February 2021
                : 21
                : 3
                : 779
                Affiliations
                [1 ]Faculty of Information Tecnology, Macau University of Science and Technology, Macau 999078, China; shichao.chen@ 123456ia.ac.cn
                [2 ]The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
                [3 ]School of Mechanical and Electrical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518000, China; liqijie1998@ 123456163.com
                [4 ]Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
                [5 ]Department of Electrical and Computer Engineering, Faculty of Engineering, and Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21481, Saudi Arabia; aabusorrah@ 123456kau.edu.sa
                Author notes
                [* ]Correspondence: mengchu.zhou@ 123456njit.edu
                Author information
                https://orcid.org/0000-0001-7677-4211
                https://orcid.org/0000-0002-7295-6373
                https://orcid.org/0000-0002-5408-8752
                Article
                sensors-21-00779
                10.3390/s21030779
                7865659
                33498910
                f7841fe9-6c8d-498b-b746-f5ad2c0a7bcd
                © 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/).

                History
                : 28 December 2020
                : 12 January 2021
                Categories
                Review

                Biomedical engineering
                collaborative scheduling,edge computing,internet of things,limited resources,optimization,task offloading

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