摘要
为对云环境下动态数据进行管理,防止数据损坏甚至丢失,方便后续利用,需要对云计算环境下动态数据进行聚集;但目前大多数算法都是基于线性时间概率计数的数据聚集算法,这种方法存在占用的存储空间较大,且不能保证动态数据聚集的准确性的问题;为此,提出一种基于粒子群优化算法的云计算环境下动态数据聚集算法,该算法首先对云计算环境中的动态数据结构模型进行分析,完成对云计算环境下动态数据的离散样本频谱特征的计算,实现云计算环境下动态数据聚集样本的特征提取和信息模型构建;针对粒子群算法收敛速度慢的问题,提出利用混沌映射方法对其进行优化,通过生成混沌序列,解决粒子群算法存在的问题,利用粒子群优化算法进行特征聚集,从而完成云计算环境下动态数据聚集算法;实验结果表明,所提算法能够有效提高动态数据聚集的可靠性和稳定性,降低聚集时间,减少所占内存空间,具有较强的实践性,为该领域的发展创造条件。
In order to manage dynamic data in the cloud environment and prevent data damage or even loss,and facilitate the subsequent use,dynamic data collection in the cloud computing environment is needed.But most of the algorithms are based on linear time probabilistic counting algorithm,which needs large storage space and can not guarantee the accuracy of dynamic data aggregation.Therefore,this paper proposes a method to calculate the dynamic data aggregation algorithm under the environment of cloud based on particle swarm optimization algorithm,the algorithm first carries on the analysis to the dynamic data structure model of cloud computing environment,complete the calculation of the discrete sample spectrum characteristics of dynamic data environment of cloud,the cloud computing to construct feature extraction and information aggregation model sample the dynamic data environment.In order to solve the problem of particle swarm algorithm with slow convergence,put forward to optimize the chaotic mapping method by generating chaotic sequences,solve the problems of particle swarm algorithm,particle swarm optimization algorithm for feature aggregation,thus completing the cloud computing environment dynamic data aggregation algorithm.The experimental results show that the proposed algorithm can effectively improve the reliability and stability of dynamic data aggregation,reduce accumulation time,reduce the memory space occupied,with a strong practical,to create conditions for the development of the field.
出处
《计算机测量与控制》
2018年第1期177-180,共4页
Computer Measurement &Control
关键词
云计算
动态数据
数据聚集算法
cloud computing
dynamic data
data aggregation algorithm
