A New Algorithm to Improve Efficiency of Resource Scheduling in Clouding Computing Based on Extended Support Vector Machine
A high effective scheduling in cloud computing environment is significant to guarantee quality of cloud service. In this paper, Weighted Least Squares Support Vector Machine (WLS SVM) is introduced to reflect finished time and cost of assignments in cloud computing and can obtain robust estimates for regression through the limited observation. There is a simple and efficient approach to model parameters selection. Some significant parameters of support vector machine are defined to further improve convergence performance of algorithm. Moreover, high effective scheduling will be acquired and service cost may be generated in cloud computing environment. Through the simulation experiment, the validity of the proposed model is demonstrated. The results show that the method has more superior performance than other methods like Least Squares Support Vector Machine (LS SVM)