电子测量与仪器学报

杨文杰,巨涛,杨阳,火久元.面向边缘计算的人工鱼群搜索任务调度[J].电子测量与仪器学报,2022,36(11):149-159

面向边缘计算的人工鱼群搜索任务调度

Artificial fish swarm search task scheduling for edge computing

DOI：

英文关键词:edge computing task scheduling artificial fish algorithm Gaussian distribution function taboo search algorithm

基金项目:国家自然科学基金（61862037, 62262038）、兰州交通大学天佑创新团队项目（TY202002）、兰州市人才创新创业项目(2021 RC 40)资助

作者	单位
杨文杰	1.兰州交通大学电子信息与工程学院
巨涛	1.兰州交通大学电子信息与工程学院
杨阳	1.兰州交通大学电子信息与工程学院
火久元	1.兰州交通大学电子信息与工程学院

Author	Institution
Yang Wenjie	1.School of Electronics and Information Engineering, Lanzhou Jiaotong University
Ju Tao	1.School of Electronics and Information Engineering, Lanzhou Jiaotong University
Yang Yang	1.School of Electronics and Information Engineering, Lanzhou Jiaotong University
Huo Jiuyuan	1.School of Electronics and Information Engineering, Lanzhou Jiaotong University

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中文摘要:

如何将计算任务分配到合适的边缘计算资源上进行计算,以满足边缘计算环境下用户的计算需求、提高用户任务请求的服务质量,是边缘计算中面临的关键问题。本文提出一种基于人工鱼群搜索的边缘计算任务调度方法(AFETSA)。将人工鱼群搜索算法和边缘计算任务调度模型相结合,采用非线性递减函数动态地调整人工鱼的视野范围和步长,以提高启发式任务调度算法的全局搜索能力,降低任务的计算时延;同时与禁忌搜索算法进行融合,通过引入忌禁表,在每一次迭代中防止算法陷入局部最优,提高算法的寻优能力。 CloudSim3. 0 仿真平台实验评测结表明,本文所提 AFETSA 方法和已有的 AFSA、ACO 和 PSO 这 3 种调度算法相比,在任务执行时间、算法稳定性、负载均衡方面都有明显的提升,可充分利用边缘服务器计算资源,提升计算任务的计算性能,有效解决边缘计算中任务调度不均导致的时延过高和负载不均衡问题。

英文摘要:

Allocating computing tasks to appropriate edge computing resources to meet the computing needs of users and improve the quality of service for user task requests is a key problem in edge computing. This paper proposes an edge computing task scheduling method (AFETSA) based on artificial fish swarm search. For improving the global search ability of the heuristic task scheduling algorithm and reducing the computation time delay, the artificial fish search algorithm was combined with the edge computing task scheduling model, and the field of view and step size of the artificial fish were dynamically adjusted by the nonlinear decreasing function. At the same time, for improving the optimization ability of the algorithm, the tabu search algorithm is fused, and the tabu list is introduced to prevent the algorithm from falling into local optimal in each iteration. The experimental evaluation results on the CloudSim3. 0 simulation platform, show that compared with the existing task scheduling algorithms AFSA, ACO and PSO, the proposed task scheduling method in this paper has significant improvement in task execution time, algorithm stability and load balance. It can make full use of the computing resources of edge servers to improve the computing performance of computing tasks, and effectively solve the problem of high delay and load imbalance caused by uneven task scheduling in edge computing.

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