多种群协同进化算法优化的云存储仿真分析

云平台下大数据的极速增长,使得传统的数据存储由于时间响应慢、负载不均衡等因素,成为阻碍大数据云存储的关键技术,为了解决云平台下大数据的存储问题,提出了多种群协同进化优化算法的存储方法.该方法首先将存储分布区分割成若干个环区域,同时标记每个存储区的存储访问时间,然后将大数据的存储访问抽象为最优解问题.通过改进协同进化算法,防止粒子群早熟,采用该优化算法对大数据存储过程中的任务调度粒子群分别编码,根据微粒群不断进化和变异,迭代得到最优解,从而满足云平台下大数据存储的实际需求.利用Cloudsim搭建仿真平台,对提出的新型大数据存储方法加以评估验证,结果表明该方法不仅具有更快的响应速度,而且降低了系统能耗,提高了负载均衡度.

Multiple population co-evolution algorithm simulation of cloud storage optimization

The rapid growth of big data cloud platform, making the traditional data storage due to slow response time, load imbalance and other factors, become the key technology to hinder large data cloud storage, In order to solve the problem of large data storage in the cloud platform, the storage method of multi population co evolution optimization algorithm is proposed. In this method, the storage area is divided into several ring regions, and the storage access time of each storage area is marked. Through the improvement of particle swarm cooperative evolutionary algorithm, to prevent premature, the optimization algorithm of encoding task scheduling particle swarm large data storage process, based on particle swarm evolution and variation, iterative optimal solution, In order to meet the actual needs of large data storage cloud platform. Using Cloudsim to build simulation platform, evaluated and verified in the new type data storage method is proposed. The results show that this method not only has faster response speed, but also reduces the energy consumption of the system, improve the load balance degree.