Journal of Northeastern University Natural Science ›› 2017, Vol. 38 ›› Issue (2): 209-213.DOI: 10.12068/j.issn.1005-3026.2017.02.012

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Load Prediction Approach for Cloud Application Based on Deep Belief Networks

MA An-xiang, ZHANG Chang-sheng, ZHANG Bin, ZHANG Xiao-hong   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2015-09-16 Revised:2015-09-16 Online:2017-02-15 Published:2017-03-03
  • Contact: ZHANG Bin
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Abstract: To implement the adaptive optimization to ensure the performance of cloud application, it is necessary to accurately predict the load for cloud application. According to the feature of load prediction in cloud application, an approach is proposed for load prediction based on deep belief networks. Explicit and implicit features for load data are given. Load prediction model is defined. Then, the algorithm of load prediction based on deep belief networks is designed and implemented. This approach is evaluated and compared with some related load prediction algorithms, which reveals very encouraging results in terms of the prediction quality.

Key words: cloud computing, cloud application, deep belief network, load prediction, adaptive optimization

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