Swift: Reliable and Low-Latency Data Processing at Cloud Scale

Author(s):  
Bo Wang ◽  
Zhenyu Hou ◽  
Yangyu Tao ◽  
Yifeng Lu ◽  
Chao Li ◽  
...  
Author(s):  
Andrey Brito ◽  
Andre Martin ◽  
Thomas Knauth ◽  
Stephan Creutz ◽  
Diogo Becker ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 2134-2144 ◽  
Author(s):  
Chengyuan Huang ◽  
Jiao Zhang ◽  
Tao Huang

2014 ◽  
Vol 31 (8) ◽  
pp. 085012 ◽  
Author(s):  
Carlos Filipe Da Silva Costa ◽  
César Augusto Costa ◽  
Odylio Denys Aguiar

2016 ◽  
Vol 82 ◽  
pp. 1-12 ◽  
Author(s):  
Ting Wang ◽  
Zhiyang Su ◽  
Yu Xia ◽  
Bo Qin ◽  
Mounir Hamdi

2021 ◽  
Vol 17 (3) ◽  
pp. 1-26
Author(s):  
Baoquan Zhang ◽  
David H. C. Du

Computer systems utilizing byte-addressable Non-Volatile Memory ( NVM ) as memory/storage can provide low-latency data persistence. The widely used key-value stores using Log-Structured Merge Tree ( LSM-Tree ) are still beneficial for NVM systems in aspects of the space and write efficiency. However, the significant write amplification introduced by the leveled compaction of LSM-Tree degrades the write performance of the key-value store and shortens the lifetime of the NVM devices. The existing studies propose new compaction methods to reduce write amplification. Unfortunately, they result in a relatively large read amplification. In this article, we propose NVLSM, a key-value store for NVM systems using LSM-Tree with new accumulative compaction. By fully utilizing the byte-addressability of NVM, accumulative compaction uses pointers to accumulate data into multiple floors in a logically sorted run to reduce the number of compactions required. We have also proposed a cascading searching scheme for reads among the multiple floors to reduce read amplification. Therefore, NVLSM reduces write amplification with small increases in read amplification. We compare NVLSM with key-value stores using LSM-Tree with two other compaction methods: leveled compaction and fragmented compaction. Our evaluations show that NVLSM reduces write amplification by up to 67% compared with LSM-Tree using leveled compaction without significantly increasing the read amplification. In write-intensive workloads, NVLSM reduces the average latency by 15.73%–41.2% compared to other key-value stores.


Author(s):  
D. J. Richardson ◽  
Y. Chen ◽  
N. V. Wheeler ◽  
J. R. Hayes ◽  
T. Bradley ◽  
...  

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