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Hot Spot Data Prediction Model Based on Wavelet Neural Network

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  • Ming Zhang
  • Wei Chen

Abstract

The novel hybrid multilevel storage system will be popular with SSD being integrated into traditional storage systems. To improve the performance of data migration between solid-state hard disk and hard disk according to the characteristics of each storage device, identifying the hot data block is significant issue. The hot data block prediction model based on wavelet neural network is built and trained by using historical data. This prediction model can overcome the cumulative effect of traditional statistical methods and has strong sensitivity to I/O loads with random variations. The experimental results show that the proposed model has better accuracy and faster learning speed than BP neural network model. In addition, it has less dependence on sample data and has better generalization ability and robustness. This model can be applied to the data migration of distributed hybrid storage systems to improve performance.

Suggested Citation

  • Ming Zhang & Wei Chen, 2018. "Hot Spot Data Prediction Model Based on Wavelet Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, October.
    • Handle: RePEc:hin:jnlmpe:3719564
    DOI: 10.1155/2018/3719564
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