移动云-边环境下任务卸载多方案研究综述
湖南工业大学, 计算机学院, 湖南株洲 412007
| 摘 要： | 移动边缘计算（Mobile Edge Computing, MEC）作为传统云计算向网络边沿延伸的一种新兴技术，将计算密集型任务卸载至边缘服务器以解决终端用户设备资源、算力的匮乏，满足移动用户对应用程序任务计算的低能耗、低延迟需求，大幅缓解云中心压力。云-边结合架构已成大势所趋。如何对终端任务执行最优卸载调度一直是MEC研究领域的重点问题之一。本文对任务卸载技术在当今云-边环境下的研究方向与成果进行综述。首先，介绍了MEC环境与任务卸载技术的发展现状，阐述了启发式算法、元启发式算法、强化学习等经典算法的概念及应用，并依据所研文献的主流方案总结、归纳出任务卸载方案的两类方向——基于算法优化的传统任务卸载方案与基于强化学习的交互任务卸载方案；而后从问题约束、优化目标、所作贡献等方面对上述两类主流方向的所研文献进行了归纳与整理，并对其所用经典算法及创新进行相互对比；最后，对该领域当下所面临的挑战与未来研究目标进行了总结，为后续研究开展提供参考与帮助。 |
| 关 键 词： | 移动边缘计算; 任务卸载; 组合优化; 强化学习; 启发式算法 |
| DOI： | 10.57237/j.cst.2023.03.001 |
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收稿日期: 2023-04-09
接受日期: 2023-05-25
在线出版日期: 2023-06-15
Survey on Multiple Methods for Task Offloading in Mobile Cloud-Edge Environment
College of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| Abstract: | Mobile edge computing (MEC) is an emerging technology that extends cloud computing to the edge of the network. It offloads computing intensive tasks to the edge server to solve the problem of insufficient computing power and resource of the terminal device, meets the low energy consumption and low latency requirements of the mobile user for application task computing, and greatly releases the pressure of the cloud center. Cloud-edge integration architecture has become a trend. How to perform optimal offload scheduling for terminal tasks has always been one of the key issue in the field of MEC research. This paper reviews the research directions and achievements of task offloading technology in today’s cloud-edge environment. First, the development status of MEC environment and task offloading technology are introduced, and the concepts and applications of classic schemes such as heuristic algorithm, metaheuristic algorithm, and reinforcement learning are elaborated. Based on the mainstream solutions in the research literature, two types of task offloading solutions are summarized: traditional task offloading solutions based on algorithm optimization and interactive task offloading solutions based on reinforcement learning. Second, the research literature on the two mainstream directions mentioned above is summarized and organized from aspects such as problem constraints, optimization objectives, and contributions made. Meanwhile, schemes are compared through the classic algorithms and innovations they used. Finally, the current challenges and future research objectives in this field are summarized to provide reference and help for the follow-up research. |
| Keywords: | Mobile Edge Computing; Task Offloading; Combinatorial Optimization; Reinforcement Learning; Heuristic Algorithm |
基金
| 1. | 湖南省自然科学基金 (2023JJ50204) |
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