Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (12): 3151-3160.doi: 10.23940/ijpe.19.12.p6.31513160

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A Cloud Computing Load Algorithm

Yao Yao   

  1. Zhengzhou Institute of Technology, Zhengzhou, 450044, China
  • Submitted on ; Revised on ; Accepted on
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  • About author:Yao Yao is currently an associate professor in the school of information Engineering, Zhengzhou Institute of Technology. She received her Masters of computer application engineering degree from the department of information engineering, University of Zhengzhou, China in 2008. Her research interests include high-performance computing and Web mining.

Abstract: For the issue of resource load prediction in cloud computing, a modified artificial bee colony algorithm and SVM are combined to construct a predictive model. First, by using reverse learning to initialize the population, differential evolution selects the individual population. The point strategy is used to construct the honey source selection route of the algorithm. The feedback mechanism reduces the shortcomings of the algorithm falling into the local optimum. Second, the parameters in the SVM prediction model are optimized and the best ones are found by using the improved bee colony algorithm. In the final simulation experiment, the proposed IABC algorithm has better prediction accuracy than the SVM, the LSSVM and other prediction algorithms, and so it has a certain promotional value.

Key words: cloud computing, resource loading, Artificial Bee Colony Algorithm, SVM