CAJ | 학술논문

移动云计算成为一种新兴的数字信息处理技术,是移动通信数据信息处理的重要工具.在移动云计算中,对路由转发分簇簇间的波动离散数据进行量化处理是提高云计算并行数据分析效率的关键.传统方法采用时延估计方法进行量化处理,当用户信道分配出现时滞时,量化性能较差.提出一种基于贝叶斯粗糙集估计的移动云计算簇间波动离散数据均匀量化算法.构建移动云计算数据分簇模型和信道模型,进行波动离散数据采集,按照Logistic模式选择路径,得到移动云计算簇间波动离散数据均匀量化的最优概率密度,构建贝叶斯粗糙集均匀量化优化目标函数,实现算法改进.仿真结果表明,采用该算法能有效实现对移动云计算簇间波动离散数据的特征分类,对波动离散数据的量化效果较好,从而提高了云计算的并行计算效率.
이동운계산성위일충신흥적수자신식처리기술,시이동통신수거신식처리적중요공구.재이동운계산중,대로유전발분족족간적파동리산수거진행양화처리시제고운계산병행수거분석효솔적관건.전통방법채용시연고계방법진행양화처리,당용호신도분배출현시체시,양화성능교차.제출일충기우패협사조조집고계적이동운계산족간파동리산수거균균양화산법.구건이동운계산수거분족모형화신도모형,진행파동리산수거채집,안조Logistic모식선택로경,득도이동운계산족간파동리산수거균균양화적최우개솔밀도,구건패협사조조집균균양화우화목표함수,실현산법개진.방진결과표명,채용해산법능유효실현대이동운계산족간파동리산수거적특정분류,대파동리산수거적양화효과교호,종이제고료운계산적병행계산효솔.
Mobile cloud computing is an emerging digital information processing technology, is an important tool for mobile communication data information processing. In mobile cloud computing, the route forwarding branch wave discrete data be-tween clusters were quantified is the key to improve the parallel efficiency of cloud computing data analysis. The traditional method of using time delay estimation method for quantitative processing, when the user channel assignment delay, quanti-tative performance is poor. Presented a method for calculating the inter cluster volatility discrete data uniform quantization algorithm for mobile cloud Bayesian Rough Set Based on the estimation of. The construction of mobile cloud computing da-ta clustering model and channel model of discrete data acquisition, wave, select the path according to the Logistic model, the optimal probability density fluctuations between clusters of discrete data of uniform mobile cloud computing, Bayesian Rough set to optimize the objective function of uniform quantization, improved algorithm. The simulation results show that, using this algorithm can effectively implement on mobile cloud computing cluster between the fluctuation characteristics of classification of discrete data, better quantitative effect on fluctuation of the discrete data, so as to improve the efficiency of parallel computing of cloud computing.