EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||6|
|Section||4 - Data Organisation, Management and Access|
|Published online||16 November 2020|
Heat Prediction of High Energy Physical Data Based on LSTM Recurrent Neural Network
IHEP computing center, 19B Yuquan Road, Beijing 100049, China
2 University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
3 Tianfu Cosmic Ray Research Center, IHEP, 9 Renmin South Road, Chengdu 620500, Sichuan, China
Published online: 16 November 2020
High-energy physics computing is a typical data-intensive calculation. Each year, petabytes of data needs to be analyzed, and data access performance is increasingly demanding. The tiered storage system scheme for building a unified namespace has been widely adopted. Generally, data is stored on storage devices with different performances and different prices according to different access frequency. When the heat of the data changes, the data is then migrated to the appropriate storage tier. At present, heuristic algorithms based on artificial experience are widely used in data heat prediction. Due to the differences in computing models of different users, the accuracy of prediction is low. A method for predicting future access popularity based on file access characteristics with the help of LSTM deep learning algorithm is proposed as the basis for data migration in hierarchical storage. This paper uses the real data of high-energy physics experiment LHAASO as an example for comparative testing. The results show that under the same test conditions, the model has higher prediction accuracy and stronger applicability than existing prediction models.
© The Authors, published by EDP Sciences, 2020
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