电子测量与仪器学报

王瑶,卢先领,沈义峰.移动边缘计算中依赖型任务的调度模型研究[J].电子测量与仪器学报,2022,36(8):60-68

移动边缘计算中依赖型任务的调度模型研究

Research on scheduling model of dependent tasks in mobile edge computing

DOI：

中文关键词: 移动边缘计算任务调度依赖型任务 Stackelberg 博弈 Q 值

英文关键词:mobile edge computing task scheduling dependent tasks Stackelberg game Q value

基金项目:国家自然科学基金项目（61773181）资助

作者	单位
王瑶	1.江南大学物联网工程学院
卢先领	1.江南大学物联网工程学院
沈义峰	1.江南大学物联网工程学院

Author	Institution
Wang Yao	1.School of Internet of Things Engineering, Jiangnan University
Lu Xianling	1.School of Internet of Things Engineering, Jiangnan University
Shen Yifeng	1.School of Internet of Things Engineering, Jiangnan University

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中文摘要:

当前移动边缘计算(mobile edge computing, MEC)环境中关于任务调度的工作经常忽略任务间的依赖关系,导致其完成时延较长。针对此问题,首先,以降低系统完成时延为目标,在考虑到跨服务器协作的多用户、多边缘服务器场景下,利用广度优先搜索算法(breadth first search, BFS)构建一种依赖型任务的调度模型。然后,根据任务和边缘服务器之间的交互,将模型中各调度层的联合卸载和迁移问题建模为一个多领导者多跟随者的 Stackelberg 博弈。最后,为实现 Stackelberg 博弈均衡,提出基于 Q 值的卸载算法和分布式迭代迁移算法求解模型。仿真结果表明,与基线算法相比,所提算法在不同规模的用户和边缘服务器的场景下,将系统完成时延分别降低了 44. 1%和 63. 2%。进一步实验表明,与传统方案相比,所提模型在不同规模的用户和边缘服务器的场景下使系统完成时延分别降低了 20. 1%和 6. 7%,有效保证了服务质量。

英文摘要:

The task scheduling work in the current mobile edge computing (MEC) environment often ignores the dependency between tasks, resulting in a long delay in completion. In response to this problem, first of all, with the goal of reducing the system completion delay, in the multi-user and multi-edge server scenario that takes cross-server collaboration into account, the breadth first search algorithm (BFS) is used to build a dependent task scheduling model. Then, according to the interaction between tasks and edge servers, the joint offloading and migration problem of each scheduling layer in the model are modeled as a Stackelberg game with multiple leaders and multiple followers. Finally, in order to achieve Stackelberg equilibrium, an offloading algorithm based on the Q value and a distributed iterative migration algorithm are proposed to solve the model. The simulation results show that compared with the baseline algorithms, the proposed algorithm reduces the system completion delay by 44. 1% and 63. 2% respectively in the scenarios of users and edge servers with different scales. Further experiments show that compared with the traditional solutions, the proposed model reduces the system completion delay by 20. 1% and 6. 7% respectively in the scenarios of users and edge servers with different scales, and effectively guarantees the quality of service.

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