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Review

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

1
Faculty of Information Tecnology, Macau University of Science and Technology, Macau 999078, China
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
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
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 779; https://doi.org/10.3390/s21030779
Submission received: 28 December 2020 / Revised: 10 January 2021 / Accepted: 12 January 2021 / Published: 24 January 2021
(This article belongs to the Section Intelligent Sensors)

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.
Keywords: collaborative scheduling; edge computing; internet of things; limited resources; optimization; task offloading collaborative scheduling; edge computing; internet of things; limited resources; optimization; task offloading

Share and Cite

MDPI and ACS Style

Chen, S.; Li, Q.; Zhou, M.; Abusorrah, A. Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm. Sensors 2021, 21, 779. https://doi.org/10.3390/s21030779

AMA Style

Chen S, Li Q, Zhou M, Abusorrah A. Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm. Sensors. 2021; 21(3):779. https://doi.org/10.3390/s21030779

Chicago/Turabian Style

Chen, Shichao, Qijie Li, Mengchu Zhou, and Abdullah Abusorrah. 2021. "Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm" Sensors 21, no. 3: 779. https://doi.org/10.3390/s21030779

APA Style

Chen, S., Li, Q., Zhou, M., & Abusorrah, A. (2021). Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm. Sensors, 21(3), 779. https://doi.org/10.3390/s21030779

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