Reducing Garbage Collection Overhead in SSD Based on Workload Prediction

Authors: 

Pan Yang, Ni Xue, Yuqi Zhang, Yangxu Zhou, Li Sun, Wenwen Chen, Zhonggang Chen, Wei Xia, Junke Li, and Kihyoun Kwon, Samsung R&D Institute China Xi'an, Samsung Electronics

Abstract: 

In solid-state drives (SSDs), garbage collection (GC) plays a key role in making free NAND blocks for newly coming data. The data copied from one block to another by GC affects both the performance and lifetime of SSD significantly. Placing the data with different “temperature” into different NAND blocks can reduce data copy overhead in GC. This paper proposes a scheme to place data according to its predicted future temperature. A neural network called LSTM is applied to increase the accuracy of temperature prediction in both temporal and spatial dimensions. And it also uses K-Means to do clustering and automatically dispatch similar “future temperature” data to the same NAND blocks. The results obtained show that performance and write amplification factor (WAF) are improved in various applications. In the best case, the WAF and 99.99% of the write latency are reduced by up to 43.5% and 79.3% respectively.

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BibTeX
@inproceedings {234765,
author = {Pan Yang and Ni Xue and Yuqi Zhang and Yangxu Zhou and Li Sun and Wenwen Chen and Zhonggang Chen and Wei Xia and Junke Li and Kihyoun Kwon},
title = {Reducing Garbage Collection Overhead in {SSD} Based on Workload Prediction},
booktitle = {11th {USENIX} Workshop on Hot Topics in Storage and File Systems (HotStorage 19)},
year = {2019},
address = {Renton, WA},
url = {https://www.usenix.org/conference/hotstorage19/presentation/yang},
publisher = {{USENIX} Association},
month = jul,
}