Applied Intelligence

, Volume 48, Issue 11, pp 4072–4083 | Cite as

A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine

  • Wei Zhong
  • Yi Zhuang
  • Jian Sun
  • Jingjing Gu


In order to reduce the energy consumption in the cloud data center, it is necessary to make reasonable scheduling of resources in the cloud. The accurate prediction for cloud computing load can be very helpful for resource scheduling to minimize the energy consumption. In this paper, a cloud load prediction model based on weighted wavelet support vector machine(WWSVM) is proposed to predict the host load sequence in the cloud data center. The model combines the wavelet transform and support vector machine to combine the advantages of them, and assigns weight to the sample, which reflects the importance of different sample points and improves the accuracy of load prediction. In order to find the optimal combination of the parameters, we proposed a parameter optimization algorithm based on particle swarm optimization(PSO). Finally, based on the WWSVM model, a load prediction algorithm is proposed for cloud computing using PSO-based weighted support vector machine. The Google cloud computing data set is used to verify the algorithm proposed in this paper by experiments. The experiment results indicate that comparing with the wavelet support vector machine, autoregressive integrated moving average, adaptive network-based fuzzy inference system and tuned support vector regression, the proposed algorithm is superior to the other four prediction algorithms in prediction accuracy and efficiency.


Cloud computing Weighted Wavelet transform Support vector machine Particle swarm optimization Load prediction 



This work is supported by the National Natural Science Foundation of China (General Program) under Grant No. 61572253, ”13th Five-Year Plan” Equipment Pre-Research Projects Fund under Grant No. 61402420101HK02001, Aviation Science Fund under No. 61202351.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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