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Swift cloud storage load forecasting based on AHP-RBF

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Keywords Cloud storage cloud storage
Tags based cloud cloud storage designs network process proxy storage
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Swift cloud storage load forecasting based on AHP-RBF

Tan Hu Jiang Lin

Based on the study of the load factors of proxy node in Swift cloud storage, this paper proposes a RBF neural network combining analytic Hierarchy process (AHP) and hybrid hierarchical genetic training to predict the load of swift cloud storage system, in which the load-layered model of cloud storage systems is constructed by using AHP, To improve the comprehensive accuracy of load forecasting, the RBF neural network predictive model is designed, and the parameters and structure of RBF neural network are determined by the hybrid hierarchical genetic Algorithm (HHGA). The simulation results show that the prediction of Swift cloud storage load is feasible and can provide the basis for dynamic load balancing decision of the system.


Swift cloud storage load forecasting based on AHP-RBF

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