[1]王宏杰,徐胜超.基于改进遗传算法的云计算任务调度方法[J].计算机技术与发展,2024,34(02):40-45.[doi:10. 3969 / j. issn. 1673-629X. 2024. 02. 006]
　WANG Hong-jie,XU Sheng-chao.Cloud Computing Task Scheduling Method Based on Improved Genetic Algorithm[J].,2024,34(02):40-45.[doi:10. 3969 / j. issn. 1673-629X. 2024. 02. 006]

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基于改进遗传算法的云计算任务调度方法()

分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

34

期数:

2024年02期

页码:

40-45

栏目:

大数据与云计算

出版日期:

2024-02-10

文章信息/Info

Title:

Cloud Computing Task Scheduling Method Based on Improved Genetic Algorithm

文章编号:

1673-629X(2024)02-0040-06

作者:

王宏杰; 徐胜超

广州华商学院 数据科学学院,广东 广州 511300

Author(s):

WANG Hong-jie; XU Sheng-chao

School of Data Science,Guangzhou Huashang College,Guangzhou 511300,China

关键词:

改进遗传算法; 云计算; 任务调度; 适应度; 目标函数

Keywords:

improved genetic algorithm; cloud computing; task scheduling; fitness; objective function

分类号:

TP393. 4

DOI:

10. 3969 / j. issn. 1673-629X. 2024. 02. 006

摘要:

云计算环境中可能存在大量的计算节点与不确定性因素,需要进行大规模的任务调度和管理,增加了调度的复杂度和难度。 为了满足任务调度的实时性需求,降低过程中产生的能耗,提出一种基于改进遗传算法的云计算任务调度方法。 对不同的任务属性进行结合,重新设定各个云计算节点的任务属性,并计算节点的综合属性值。 根据计算结果以全部任务完成时间最小化作为调

度目标,构建云计算任务调度模型。 改进传统遗传算法,优化种群的初始形成方式,通过改进后的遗传算法求解调度模型,判断获取的解是否满足终止条件,如果满足直接输出最优云计算任务调度方案,实现云计算任务优化调度。 由实验结果可知,该方法的任务调度完成时间较低,其调度时间最高值仅为 16 min,说明该方法能够满足任务调度的实时性需求,且能耗较低,能够实现任务的高效执行和资源的合理利用。

Abstract:

There may be a large number of computing nodes and uncertain factors in the cloud computing environment,requiring large-scale task scheduling and management,which increases?

the complexity and difficulty of scheduling. In order to meet the real-time requirements of task scheduling and reduce energy consumption during the process,a cloud computing task scheduling method based on improved genetic algorithm is proposed. Combine different task attributes, reset the task attributes of each cloud computing node, andcalculate the comprehensive attribute values of the nodes. Based on the calculation results,a cloud computing task scheduling model isconstructed with the goal of minimizing the completion time?

of all tasks. The traditional genetic algorithm is improved to optimize theinitial formation mode of the population,and the scheduling model is solved by the improved genetic algorithm to determine whether theobtained solution meets the termination condition. If the optimal cloud computing task scheduling scheme can be directly output, theoptimized scheduling of cloud computing tasks can be realized. According to the experimental results, it can be seen that the taskscheduling completion time of the proposed method is relatively low,with a maximum scheduling time of only 16 minutes. It is indicatedthat the proposed method can meet the real-time requirements of task scheduling and has low energy consumption,achieving efficient taskexecution and reasonable resource utilization.

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