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View all- Liu YWang HZhou KLi CWu R(2022)A survey on AI for storageCCF Transactions on High Performance Computing10.1007/s42514-022-00101-34:3(233-264)Online publication date: 23-May-2022
Predicting file lifetimes for data placement in multi-tiered storage systems for HPC
Authors: 
Luis Thomas, Sebastien Gougeaud, Stéphane Rubini, Philippe Deniel, 
Jalil BoukhobzaAuthors Info & Claims
Pages 99 - 107
Abstract
The emergence of Exascale machines in HPC will have the foreseen consequence of putting more pressure on the storage systems in place, not only in terms of capacity but also bandwidth and latency. With limited budget we cannot imagine using only storage class memory, which leads to the use of a heterogeneous tiered storage hierarchy. In order to make the most efficient use of the high performance tier in this storage hierarchy, we need to be able to place user data on the right tier and at the right time. In this paper, we assume a 2-tier storage hierarchy with a high performance tier and a high capacity archival tier. Files are placed on the high performance tier at creation time and moved to capacity tier once their lifetime expires (that is once they are no more accessed). The main contribution of this paper lies in the design of a file lifetime prediction model solely based on its path based on the use of Convolutional Neural Network. Results show that our solution strikes a good trade-off between accuracy and under-estimation. Compared to previous work, our model made it possible to reach an accuracy close to previous work (around 98.60% compared to 98.84%) while reducing the underestimations by almost 10x to reach 2.21% (compared to 21.86%). The reduction in underestimations is crucial as it avoids misplacing files in the capacity tier while they are still in use.
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Published: 06 June 2021
Published in SIGOPS Volume 55, Issue 1
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View all- Liu YWang HZhou KLi CWu R(2022)A survey on AI for storageCCF Transactions on High Performance Computing10.1007/s42514-022-00101-34:3(233-264)Online publication date: 23-May-2022
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