计算机工程2017，Vol.43Issue(1)：13-19,7.DOI:10.3969/j.issn.1000-3428.2017.01.003
基于Gibbs采样与概率分布估计的移动云数据存储
Mobile Cloud Data Storage Based on Gibbs Sampling and Probability Distribution Estimation
摘要
Abstract
In order to improve the computing and storage capacity of mobile cloud data storage remote server,this paper proposes an improved mobile cloud data storage algorithm.Firstly,it constructs resampling expected propagation time calculation model by considering node failure probability with the voting data distribution and voting data processing framework,and establishes the dynamic voting network integrating energy efficiency and fault tolerance.It uses the probability distribution estimation method to optimize the storage routes of dynamic network model.At the same time,it uses Gibbs sampling to solve the problems of high-dimensional coupling and unsupervised training of sample data and non supervision training.Experimental results show that compared with the greedy algorithm,random placement algorithm and Estimation of Distribution Algorithms (EDAs),the proposed algorithm has high energy efficiency and storage reliability.关键词
Gibbs采样/分布估计/重采样/移动云/数据存储Key words
Gibbs sampling/distribution estimation/resampling/mobile cloud/data storage分类
信息技术与安全科学引用本文复制引用
李又玲,常致全..基于Gibbs采样与概率分布估计的移动云数据存储[J].计算机工程,2017,43(1):13-19,7.
