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Volume 35 Issue 1
January  2020
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Yu-Qi Li, Li-Quan Xiao, Jing-Hua Feng, Bin Xu, Jian Zhang. AquaSee: Predict Load and Cooling System Faults of Supercomputers Using Chilled Water Data[J]. Journal of Computer Science and Technology, 2020, 35(1): 221-230. DOI: 10.1007/s11390-019-1951-7
Citation: Yu-Qi Li, Li-Quan Xiao, Jing-Hua Feng, Bin Xu, Jian Zhang. AquaSee: Predict Load and Cooling System Faults of Supercomputers Using Chilled Water Data[J]. Journal of Computer Science and Technology, 2020, 35(1): 221-230. DOI: 10.1007/s11390-019-1951-7
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AquaSee: Predict Load and Cooling System Faults of Supercomputers Using Chilled Water Data

  • 1 National Supercomputer Center in Tianjin, Tianjin 300450, China;
    2 College of Computer, National University of Defense Technology, Changsha 410073, China

Funds: The work was supported by the National Key Research and Development Program Program of China under Grant No. 2016YFB0201800.
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  • Author Bio:

    Yu-Qi Li got his Bachelor's degree in computer science from Nanchang University, Nanchang, in 2012, and got his Master's degree in software engineer from Nankai University, Tianjin, in 2017. He has been worked as an engineer in NSCC (National Supercomputer Center in Tianjin), Tianjin, for six years. His main research interests are high performance computing (HPC), machine learning, and supercomputer R&D (research and development) and monitoring.

  • Received Date: May 20, 2019
  • Revised Date: August 18, 2019
  • Published Date: January 04, 2020
    Abstract
  • An analysis of real-world operational data of Tianhe-1A (TH-1A) supercomputer system shows that chilled water data not only can reflect the status of a chiller system but also are related to supercomputer load. This study proposes AquaSee, a method that can predict the load and cooling system faults of supercomputers by using chilled water pressure and temperature data. This method is validated on the basis of real-world operational data of the TH-1A supercomputer system at the National Supercomputer Center in Tianjin. Datasets with various compositions are used to construct the prediction model, which is also established using different prediction sequence lengths. Experimental results show that the method that uses a combination of pressure and temperature data performs more effectively than that only consisting of either pressure or temperature data. The best inference sequence length is two points. Furthermore, an anomaly monitoring system is set up by using chilled water data to help engineers detect chiller system anomalies.
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    • References (33)
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