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Workload time series prediction in storage systems: a deep learning based approach

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Abstract

Storage workload prediction is a critical step for fine-grained load balancing and job scheduling in realtime and adaptive cluster systems. However, how to perform workload time series prediction based on a deep learning method has not yet been thoroughly studied. In this paper, we propose a storage workload prediction method called CrystalLP based on deep learning. CrystalLP includes workload collecting, data preprocessing, time series prediction, and data post-processing phase. The time series prediction phase is based on a long short-term memory network (LSTM). Furthermore, to improve the efficiency of LSTM, we study the sensitivity of the hyperparameters in LSTM. Extensive experimental results show that CrystalLP can obtain performance improvement compared with three classic time series prediction algorithms.

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  1. http://traces.cs.umass.edu/index.php/Storage/Storage.

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Acknowledgements

This work is by supported by the National Key R&D Program of China under Grant No. 2017YFB0202004, the National Science Foundation of China under Grant No. 61772053, the fund of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2020ZX-15.

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Li Ruan, Yu Bai, Shaoning Li & Limin Xiao

  2. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Li Ruan, Yu Bai, Shaoning Li & Limin Xiao

  3. College of Computer Science and Technology, Zhejiang University, Zhejiang, 310027, China

    Shuibing He

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  1. Li Ruan
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  2. Yu Bai
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  3. Shaoning Li
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Correspondence to Li Ruan.

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Ruan, L., Bai, Y., Li, S. et al. Workload time series prediction in storage systems: a deep learning based approach. Cluster Comput 26, 25–35 (2023). https://doi.org/10.1007/s10586-020-03214-y

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