摘要
针对云计算虚拟机调度中存在的资源分配不均衡、蝙蝠算法收敛速度慢、寻优精度不高等缺点,文章提出了一种融合负载均衡和蝙蝠算法的云计算任务调度算法。利用负载均衡对蝙蝠种群数据进行初始化,提高初始样本数据解的质量;利用Powell局部搜索算法对当前最优解进行局部搜索,提高收敛速度和精度;利用改进蝙蝠算法对虚拟机进行分配时,充分利用物理机上的资源,达到了最优化目标。仿真实验表明,与其他标准蝙蝠算法和粒子优化算法相比,本文改进的算法有较快的收敛速度和较高的寻优精度。
For cloud computing resource allocation imbalance exists in the virtual machine scheduling, bat algorithm slow convergence speed and optimization accuracy is not high shortcomings, a method is proposed task scheduling of cloud computing based on fusion of load balancing and bat algorithm. Algorithm using load balancing to bat population data, improve the quality of the initial solution of the sample data; By Powell local search algorithm for the optimal solution for the current local search and improve the convergence speed and accuracy; when using the improved bat algorithm to allocate the virtual machine, algorithm make full use of the resources on the physical machine to achieve the optimization goal. Simulation results show that the improved algorithm has faster convergence speed and higher searching accuracy compared with other standard bat algorithm and particle swarm optimization algorithm.
出处
《信息网络安全》
CSCD
2017年第1期23-28,共6页
Netinfo Security
基金
国家自然科学基金[61402032]
安徽省高等学校自然科学研究一般项目[KJ2015B1105918]
