云计算系统中数据中心分类区的确定模型仿真

发布时间：2018-06-06 01:27

本文选题：云计算 + 中心分类区　；参考：《科技通报》2015年08期

【摘要】：对云计算系统中的漂移数据中心分类区的准确确定,可以提高云计算系统的扩展能力和缓存能力。传统方法采用线性差分智能群识别方法实现中心分类区的确定,导致漂移数据的群辨识效果不好。提出一种基于粒子群聚类的云计算系统中数据中心分类区的确定算法模型,并进行仿真实现。构建云计算系统网络激励传播机制,实现资源负载均衡,把云计算系统的存储数据结构定义为一个四叉树,构建云计算网格环境下空间资源调度框架体系,进行云计算系统的漂移数据特征提取,使得资源负载均衡达到最优值,实现算法改进。实验得出,模型对云计算系统的漂移数据中心分类区确定准确,数据融合精度较高,聚类性好,云计算系统的扩展指数提高13%,缓存执行能力提高20.5%,展示较好的性能。
[Abstract]:The accurate determination of the classification area of drift data center in cloud computing system can improve the scalability and cache ability of cloud computing system. The traditional linear differential intelligent swarm identification method is used to determine the central classification area, which leads to the poor group identification effect of drift data. An algorithm model for determining the classification area of data center in cloud computing system based on particle clustering is proposed and simulated. Construct cloud computing system network incentive propagation mechanism, realize resource load balance, define the storage data structure of cloud computing system as a quadtree, construct spatial resource scheduling framework system under cloud computing grid environment. By extracting the drift data feature of cloud computing system, the resource load balance can reach the optimal value, and the algorithm can be improved. The experimental results show that the model can determine the classification area of the drift data center of cloud computing system accurately, the data fusion accuracy is higher, clustering is good, the expansion index of cloud computing system is improved by 13 percent, and the cache execution ability is improved by 20.5. the model shows better performance.
【作者单位】：郑州财经学院;
【分类号】：TP308

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