基于改进的布谷鸟算法在云计算资源的研究

ResearchSonSImpore CuckooSSearchSinSCloudSComputingResourceSScheduling

摘要:

如何进行更好地资源调度一直都是云计算研究的热点,本文在云计算资源算法中引入布谷鸟算法,针对布谷鸟算法中出现的收敛速度快,容易局部震荡等现象,本文首先引入高斯变异算子来处理每一个阶段中的鸟窝最佳位置的选择,然后通过自适应动态因子来调整不同阶段中的鸟窝位置的选择,使得改进后的算法收敛精度提高,通过适应度函数的平衡以及遗传算法中的三种操作,使得本文算法能够有效的提高云计算环境下的资源分配效率,降低了网络消耗。在Cloudsim平台仿真实验中,通过三个方面的比较,本文算法在性能上、资源调度效率和任务调度方面都有很大改进,有效提高了云计算系统的资源调度能力。

Abstract:

How to make better resource scheduling has always been a hot topic in the research on cloud computing. In this paper, the Cuckoo Search algorithm is introduced into cloud computing resources algorithm. In view of the phenomena such as fast convergence and frequent local oscillation in the application of Cuckoo Search algorithm, first the Gauss mutation operator is introduced in this paper to handle the selection of optimal nest position in every stage, and then the self-adaptive dynamic factor is used to adjust the selection of nest position in different stages. In this way, the improved algorithm is more precise in convergence. Through the balance of the fitness function and the three kinds of operation in Genetic Algorithm, the algorithm presented in this paper can effectively improve the efficiency of resource allocation in the cloud computing environment, and thus reduce the network consumption. In the simulation experiment on the Cloudsim platform, through the comparison in three aspects, the algorithm of this paper has been improved greatly in performance, efficiency of resource scheduling and task scheduling, which effectively improves the resource scheduling ability of the cloud computing system.