采用奇异值分解移动学习云计算静态重删系统

Cloud Computing Static Deduplication Data Removing System Based on Singular Value Decomposition and Mobile Learning

在云存储服务中,为使用户可以随时验证存储在云存储服务器上数据的完整性,需要对云计算数据进行移动学习,在移动学习过程中,产生大量的重复数据。需要设计云计算静态重删系统,对重复数据有效及时删除。传统方法采用虚拟化云平台分类层次重删模型,需要修改内核代码或者以模块的形式动态植入内核代码,重删效果不好。本文提出一种基于奇异值分解移动学习的云计算静态重删系统设计方案,进行云计算存储系统设计与重删数据特征分析,对云计算静态重复数据的尺度伸缩分解,把重复数据宽带互模糊度函数映射为一个检测统计量特征分解问题,构建一个参数未知多重假设检验,对云计算静态重复数据进行奇异值尺度伸缩分解,对分解后的奇异值特征进行状态空间重组和移动学习,得到重删系统模型改进。仿真结果表明,该算法对云计算静态重复数据检测性能较高,重删性能优越,抗干扰能力强,具有较好的应用前景。

In the cloud storage service, the integrity of data and cloud storage server is important for users, mobile learning on cloud computing data is taken, in a mobile learning process, it produces large amounts of duplicate data. Design of cloud computing static duplicate data removing system is researched. The traditional method uses virtual cloud platform classification hierarchy removing model, it needs to modify the kernel code or dynamic implant kernel code module, the removing effect is not good. A cloud computing static deduplication data removing system is proposed based on singular value decomposition and mobile learning, the data characteristics are analyzed, data storage system is designed. Cloud computing scale static data decomposition is taken, data broadband cross ambiguity function is mapped as a test statistic eigenvalue decomposition problem. A parameter unknown multiple hypothesis testing is constructed, the singular value decomposition is taken in scale of cloud computing data. The singular value features of state space are reconstructed, and mobile learning is taken after decomposition. The simulation results show that the algorithm has high detection performance of deduplication data, it has strong anti-interference ability, and has better application prospect.