CAJ | 학술논문

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。
통과대Swift운존저중Proxy Node적부재인소연구，제출결합층차분석법（AHP）화혼합체계유전훈련적RBF신경망락실현대Swift운존저부재정황적예측，기중사용AHP구조대운존저계통적부재층차화모식，제고부재예측적종합정도，설계료RBF신경망락예측모형，용혼합체계유전산법（HHGA）학정RBF신경망락적삼수화결구。방진실험결과표명，대Swift운존저부재적예측구유가행성，능위계통동태부재균형결책제공의거。
Through the study of Proxy Node load factors in Swift cloud storage, a method which combines Analytic Hier-archy 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.