Computer Science ›› 2019, Vol. 46 ›› Issue (5): 290-297.doi: 10.11896/j.issn.1002-137X.2019.05.045

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Improved Learning Model for Cloud Computing Swarm Optimization Time Efficiency

JIAN Cheng-feng, KUANG Xiang, ZHANG Mei-yu   

  1. (Computer Science and Technology College,Zhejiang University of Technology,Hangzhou 310023,China)
  • Published:2019-05-15

Abstract: Aiming at the time-consuming problem when the traditional task scheduling models of cloud computing deal with the tasks,this paper proposed an ONBA algorithm combining DE (Differential Evolution) to get the scheduling data of task.Then,the obtained scheduling data are used to train the improved IDBN model.By adjusting the learning rate and training times,the time efficiency can be improved,thus achieving fast and accurate prediction of cloud computing scheduling results.The experimental results show that the improved IDBN model trained by this method can effectively shorten the actual scheduling time on the premise of ensuring precise prediction results and make up for the defect of long running time in traditional swarm optimization models.

Key words: Cloud computing, Scheduling forecast, Deep learning, Learning rate

CLC Number: 

  • TP391
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