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dc.contributor.author谭乾
dc.contributor.author江弋
dc.contributor.author林凡
dc.date.accessioned2016-05-17T06:50:46Z
dc.date.available2016-05-17T06:50:46Z
dc.date.issued2013-12-26
dc.identifier.citation计算机工程与应用,2014,(8):39-43
dc.identifier.issn1002-8331
dc.identifier.otherJSGG201408009
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/118813
dc.description.abstract通过对SWIfT云存储中PrOXy nOdE的负载因素研究,提出结合层次分析法(AHP)和混合递阶遗传训练的rbf神经网络实现对SWIfT云存储负载情况的预测,其中使用AHP构造对云存储系统的负载层次化模式,提高负载预测的综合精度,设计了rbf神经网络预测模型,用混合递阶遗传算法(HHgA)确定rbf神经网络的参数和结构。仿真实验结果表明,对SWIfT云存储负载的预测具有可行性,能为系统动态负载均衡决策提供依据。
dc.description.abstractThrough the study of Proxy Node load factors in Swift cloud storage, a method which combines Analytic Hierarchy Process(AHP)and Hybrid Hierarchical Genetic Algorithm for training of Radial Basis Function Neural Network(HHGA-RBFNN)is proposed to predict Swift cloud storage load.This paper uses AHP to construct load hierarchy model of the system for raising comprehensive accuracy of load prediction of the system, designs RBFNN prediction model, and uses hybrid hierarchical genetic algorithm to train RBFNN's parameters and configuration.From the experimental results, this method is effective, and can be a selection for Swift cloud system load balancing decision.
dc.description.sponsorship国家自然科学基金(No.61001143)
dc.language.isozh_CN
dc.subjectSwift
dc.subject混合递阶遗传算法
dc.subject径向基函数(RBF)神经网络
dc.subject层次分析法
dc.subject负载
dc.subjectSwift
dc.subjecthybrid hierarchical genetic algorithm
dc.subjectRadial Basis Function(RBF)neural networks
dc.subjectAnalytic Hierarchy Process(AHP)
dc.subjectload
dc.title基于AHP-RBF的Swift云存储负载预测
dc.title.alternativeLoad prediction of Swift cloud storage based on AHP-RBF
dc.typeArticle


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