云计算中基于共享机制和群体智能优化算法的任务调度方案

摘要/Abstract

摘要： 为了提高云计算中虚拟机(VM)的利用率并降低任务的完成时间,提出了一种融合共享机制的混合群智能优化算法,实现云任务的动态调度。首先,将虚拟机调度编码为蜜蜂、蚂蚁和遗传个体。然后,利用人工蜂群算法(ABC)、蚁群算法(ACO)和遗传算法(GA)分别在各自邻域内寻找最优解。最后,通过一个共享机制使3种算法定期交流各自搜索到的解,并将获得的最佳解作为当前最优解进行下一次迭代过程,以此来加速算法收敛并提高收敛精度。通过CloudSim进行了一个云任务调度的仿真实验,结果表明提出的混合算法能够合理有效地调度任务,在任务完成时间和稳定性方面具有优越的性能。

关键词: 共享机制, 人工蜂群算法, 任务调度, 遗传算法, 蚁群算法, 云计算

Abstract: In order to improve the utilization rate of virtual machine (VM) in cloud computing and reduce the completion time of tasks,a hybrid intelligent optimization algorithm of fusion sharing mechanism was proposed to realize dynamic scheduling of cloud tasks.First,the virtual machine scheduling is encoded as bees,ants and genetic individuals.Then,using artificial bee colony (ABC),ant colony optimization (ACO) and genetic algorithm (GA),the optimal solutionis found in each neighborhood.Finally,by a mechanism of sharing,three algorithms regularly exchange their solutions and obtain the optimal solution as the current optimal solution for the next iteration process,in order to accelerate the algorithm convergence and enhance the accuracy of convergence.Through the CloudSim,the results of cloud task scheduling simulation experiment show that the proposed hybrid algorithm can reasonable scheduling tasks effectively,and has the superior performance in the task completion time and stability.

Key words: Ant colony optimization, Artificial bee colony, Cloud computing, Genetic algorithm, Sharing mechanism, Task scheduling

中图分类号:

TP309

符晓. 云计算中基于共享机制和群体智能优化算法的任务调度方案[J]. 计算机科学, 2018, 45(6A): 290-294. https://doi.org/

FU Xiao. Task Scheduling Scheme Based on Sharing Mechanism and Swarm Intelligence
Optimization Algorithm in Cloud Computing[J]. Computer Science, 2018, 45(6A): 290-294. https://doi.org/

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