On demand resource forecasting in cloud computing is an crucial guarantee for achieving effective management of all virtualized resources and reducing data center energy consumption. According to single forecasting model cannot integrate all the valid information which leads to the decline in prediction accuracy. This paper proposed an optimal combination prediction model for cloud computing resource requirement. This model is based on generalized Dice coefficient and the induced ordered weighted geometric mean (IOWGA) operator, as well as improved Elman neural network and grey forecasting model. It is able to accurately reflect the random information and trend information in cloud computing load thus will enhance the overall prediction accuracy. The experiment results show this method is feasible and effective.
Li and Zhang REF proposed an optimal combination prediction method for resource demands, which combines the induced ordered weighted geometry averaging operator and the generalized dice coefficient with the improved Elman neural network and gray model to enhance the prediction accuracy.
Resource Demand Optimization Combined Prediction under Cloud Computing Environment Based On IOWGA Operator
International Journal of Grid and Distributed Computing
Computer Science