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Tuesday, July 9 • 4:00pm - 4:30pm
Reducing Garbage Collection Overhead in SSD Based on Workload Prediction

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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.

Speakers
PY

Pan Yang

Samsung R&D Institute China Xi'an, Samsung Electronics
NX

Ni Xue

Samsung R&D Institute China Xi'an, Samsung Electronics
YZ

Yuqi Zhang

Samsung R&D Institute China Xi'an, Samsung Electronics
YZ

Yangxu Zhou

Samsung R&D Institute China Xi'an, Samsung Electronics
LS

Li Sun

Samsung R&D Institute China Xi'an, Samsung Electronics
WC

Wenwen Chen

Samsung R&D Institute China Xi'an, Samsung Electronics
ZC

Zhonggang Chen

Samsung R&D Institute China Xi'an, Samsung Electronics
WX

Wei Xia

Samsung R&D Institute China Xi'an, Samsung Electronics
JL

Junke Li

Samsung R&D Institute China Xi'an, Samsung Electronics
KK

Kihyoun Kwon

Samsung R&D Institute China Xi'an, Samsung Electronics


Tuesday July 9, 2019 4:00pm - 4:30pm PDT
HotStorage: Grand Ballroom I–III