Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite *** order to accurately describe the dynamic change trend of such loads,this study designs a load...
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Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite *** order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resourcescheduling model for mobile cloud computing of ***,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are ***,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the ***,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of *** shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud *** with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computingresources.
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