[1]张云飞,高 岭,丁彩玲,等.边缘计算环境下改进蚁群算法的任务调度算法[J].计算机技术与发展,2021,31(09):86-91.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 015]
　ZHANG Yun-fei,GAO Ling,DING Cai-ling,et al.Improved Task Scheduling Algorithm of Ant Colony Algorithm in Edge Computing[J].,2021,31(09):86-91.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 015]
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边缘计算环境下改进蚁群算法的任务调度算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年09期
页码:
86-91
栏目:
系统工程
出版日期:
2021-09-10

文章信息/Info

Title:
Improved Task Scheduling Algorithm of Ant Colony Algorithm in Edge Computing
文章编号:
1673-629X(2021)09-0086-06
作者:
张云飞1 ; 高 岭1 ; 丁彩玲2 ; 赵 辉2 ; 金 帅2 ; 高全力1
1. 西安工程大学 计算机科学学院,陕西 西安 710048;
2. 山东如意毛纺服装集团股份有限公司,山东 济宁 272044
Author(s):
ZHANG Yun-fei1 ; GAO Ling1 ; DING Cai-ling2 ; ZHAO Hui2 ; JING Shuai2 ; GAO Quan-li1
1. School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China;
2. Shandong Jining Ruyi Woolen Textile Co. ,Ltd. ,Jining 272044,China
关键词:
边缘计算; 任务调度; 负载均衡; 蚁群算法; 多目标
Keywords:
edge computing; task scheduling; load balance; ant colony algorithm; multi-objective
分类号:
TP393
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 09. 015
摘要:
针对边缘计算环境下边缘节点间资源差距过大且任务分配的负载不均衡等问题,提出了一种基于蚁群优化算法的任务调度方法。 方法以不同任务对于 CPU、内存、带宽等计算资源的需求情况的差异作为任务选择边缘节点的约束条件,以边缘云达到整体的负载均衡为目标,通过改进启发式因子、信息素的更新等条件提高算法的整体计算效率,降低计算时间,最后通过利用蚁群算法实现任务在边缘环境下的合理分配得出最优分配方式。 方法能够避免相同类型的任务部署在同一节点中以提高任务执行效率和运算资源利用率。 仿真实验结果表明,该算法在相同的节点数量下可以分配更多的任务,而且相同的任务数量下边缘节点整体具有较低的负载不均衡度,提高了资源的利用率,降低了任务整体的计算时间。
Abstract:
In the edge computing environment, the resource gap between edge nodes is too large and the load is unbalanced in task allocation,therefore,a task scheduling method based on ant colony optimization algorithm is proposed. The differences in the demands of different tasks on CPU,memory,bandwidth and other computing resources are taken as the constraints for task selection of edge nodes,and the overall load balance of edge cloud is taken as the goal. The overall computing efficiency of the algorithm is improved and the computing time is reduced by improving the conditions such as heuristic factors and pheromone update. Finally,the optimal allocation method is obtained by using the ant colony algorithm to realize the reasonable allocation of tasks in the edge environment. The method can avoid the deployment of tasks of the same type in the same node to improve task execution efficiency and computing resource utilization. The simulation experiment shows that the proposed algorithm can allocate more tasks under the same number of nodes,and the overall edge nodes have lower load imbalance under the same number of tasks,which improves the utilization of resources and reduces the overall task calculating time.

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更新日期/Last Update: 2021-09-10