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.

存储工作负载预测是实时和自适应集群系统中细粒度负载平衡和作业调度的关键步骤。然而，如何基于深度学习方法进行工作量时间序列预测尚未得到充分研究。在本文中，我们提出了一种基于深度学习的存储负荷预测方法CrystalLP。CrystalLP包括工作负载收集，数据预处理，时间序列预测和数据后处理阶段。时间序列预测阶段基于长短期存储网络（LSTM）。此外，为了提高LSTM的效率，我们研究了LSTM中超参数的敏感性。大量的实验结果表明，与三种经典的时间序列预测算法相比，CrystalLP可以提高性能。