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边缘计算范式下计算任务协同调度的最新进展

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

作者信息

Chen Shichao, Li Qijie, Zhou Mengchu, Abusorrah Abdullah

机构信息

Faculty of Information Tecnology, Macau University of Science and Technology, Macau 999078, China.

The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

出版信息

Sensors (Basel). 2021 Jan 24;21(3):779. doi: 10.3390/s21030779.

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.

摘要

在边缘计算中,边缘设备可以将其过载的计算任务卸载到边缘服务器。这可以充分发挥边缘服务器在计算和存储方面的优势,并高效地执行计算任务。然而,如果它们一起将所有过载的计算任务都卸载到边缘服务器,可能会导致边缘服务器过载,从而导致许多计算任务的处理延迟过高以及意外的高能耗。另一方面,空闲边缘设备中的资源可能会被浪费,而资源丰富的云中心可能未得到充分利用。因此,根据任务特征、优化目标和系统状态,探索一种边缘服务器、云中心和边缘设备之间的计算任务协同调度机制至关重要。它可以帮助实现所有计算任务的高效协同调度和精确执行。这项工作分析并总结了边缘计算范式中的边缘计算场景。然后对边缘计算场景中的计算任务进行分类。接下来,为边缘计算系统制定计算卸载的优化问题。根据问题的表述,进而回顾计算任务的协同调度方法。最后,指出了边缘计算环境下高级协同调度的未来研究问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0546/7865659/5f7b3afc7360/sensors-21-00779-g001.jpg

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