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Prediction Model of Wavelet Neural Network for Hybrid Storage System

  • Haifeng Wang
  • Ming Zhang
  • Yunpeng CaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

The Hybrid storage system needs to distinguish the data state to manage data migration. The frequently data may be placed solid-state hard disk to improve the accessing performance. Here a novel prediction model of the frequently accessing data that is called hot access data is proposed. This model extracts the workload features and is built based on the wavelet neural network to identify the data state. The prediction model is trained by the sampling data from historical workloads and can be applied in the hybrid storage system. The experimental results show that the proposed model has better accuracy and faster learning speed than BP neural network model. Additionally, it has better independent on training data and generalization ability to adapt to various storage workloads.

Keywords

Hybrid storage system Wavelet Neural Network Prediction model Data migration 

Notes

Acknowledgement

This project is supported by Shandong Provincial Natural Science Foundation, China (No. ZR2017MF050), Project of Shandong Province Higher Educational Science and technology program (No. J17KA049), Shandong Province Key Research and Development Program of China (Nos. 2018GGX101005, 2017CXGC0701, 2016GGX109001) and Shandong Province Independent Innovation and Achievement Transformation, China (No. 2014ZZCX02702).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLinyi UniversityLinyiChina

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