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Write Pattern Format Algorithm for Reliable NAND-Based SSDs
This brief presents and evaluates a pre-coding algorithm to reduce power consumption and improve data retention in NAND-based solid-state drives. Compared to the state-of-the-art asymmetric coding and stripe pattern elimination algorithm, the proposed write pattern format algorithm (WPFA) achieves better data retention while consuming less power. The hardware for WPFA is simpler and requires less circuitry. The performance of WPFA is evaluated by both computer simulations and field-programmable gate array implementation
LSM-tree based Database System Optimization using Application-Driven Flash Management
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2019. 8. ์ผํ์.Modern data centers aim to take advantage of high parallelism in storage de-
vices for I/O intensive applications such as storage servers, cache systems, and
key-value stores. Key-value stores are the most typical applications that should
provide a highly reliable service with high-performance. To increase the I/O
performance of key-value stores, many data centers have actively adopted next-
generation storage devices such as Non-Volatile Memory Express (NVMe) based
Solid State Devices (SSDs). NVMe SSDs and its protocol are characterized to
provide a high degree of parallelism. However, they may not guarantee pre-
dictable performance while providing high performance and parallelism. For
example, heavily mixed read and write requests can result in performance degra-
dation of throughput and response time due to the interference between the
requests and internal operations (e.g., Garbage Collection (GC)).
To minimize the interference and provide higher performance, this paper
presents IsoKV, an isolation scheme for key-value stores by exploiting internal
parallelism in SSDs. IsoKV manages the level of parallelism of SSD directly by
running application-driven flash management scheme. By storing data with dif-
ferent characteristics in each dedicated internal parallel units of SSD, IsoKV re-
duces interference between I/O requests. We implement IsoKV on RocksDB and
evaluate it using Open-Channel SSD. Our extensive experiments have shown
that IsoKV improves overall throughput and response time on average 1.20ร
and 43% compared with the existing scheme, respectively.์ต์ ๋ฐ์ดํฐ ์ผํฐ๋ ์คํ ๋ฆฌ์ง ์๋ฒ, ์บ์ ์์คํ
๋ฐ Key-Value stores์ ๊ฐ์ I/O
์ง์ฝ์ ์ธ ์ ํ๋ฆฌ์ผ์ด์
์ ์ํ ์คํ ๋ฆฌ์ง ์ฅ์น์ ๋์ ๋ณ๋ ฌ์ฑ์ ํ์ฉํ๋ ๊ฒ์
๋ชฉํ๋ก ํ๋ค. Key-value stores๋ ๊ณ ์ฑ๋ฅ์ ๊ณ ์ ๋ขฐ ์๋น์ค๋ฅผ ์ ๊ณตํด์ผ ํ๋ ๊ฐ์ฅ
๋ํ์ ์ธ ์์ฉํ๋ก๊ทธ๋จ์ด๋ค. Key-value stores์ I/O ์ฑ๋ฅ์ ๋์ด๊ธฐ ์ํด ๋ง์ ๋ฐ
์ดํฐ ์ผํฐ๊ฐ ๋นํ๋ฐ์ฑ ๋ฉ๋ชจ๋ฆฌ ์ต์คํ๋ ์ค(NVMe) ๊ธฐ๋ฐ SSD(Solid State Devices)
์ ๊ฐ์ ์ฐจ์ธ๋ ์คํ ๋ฆฌ์ง ์ฅ์น๋ฅผ ์ ๊ทน์ ์ผ๋ก ์ฑํํ๊ณ ์๋ค. NVMe SSD์ ๊ทธ ํ
๋กํ ์ฝ์ ๋์ ์์ค์ ๋ณ๋ ฌ์ฑ์ ์ ๊ณตํ๋ ๊ฒ์ด ํน์ง์ด๋ค. ๊ทธ๋ฌ๋ NVMe SSD๊ฐ
๋ณ๋ ฌ์ฑ์ ์ ๊ณตํ๋ฉด์๋ ์์ธก ๊ฐ๋ฅํ ์ฑ๋ฅ์ ๋ณด์ฅํ์ง๋ ๋ชปํ ์ ์๋ค. ์๋ฅผ ๋ค์ด
์ฝ๊ธฐ ๋ฐ ์ฐ๊ธฐ ์์ฒญ์ด ๋ง์ด ํผํฉ๋๋ฉด ์์ฒญ๊ณผ ๋ด๋ถ ์์
(์: GC) ์ฌ์ด์ ๊ฐ์ญ์ผ๋ก
์ธํด ์ฒ๋ฆฌ๋ ๋ฐ ์๋ต ์๊ฐ์ ์ฑ๋ฅ ์ ํ๊ฐ ๋ฐ์ํ ์ ์๋ค.
๊ฐ์ญ์ ์ต์ํํ๊ณ ์ฑ๋ฅ์ ํฅ์์ํค๊ธฐ ์ํด ๋ณธ ์ฐ๊ตฌ์์๋ Key-value stores๋ฅผ
์ํ ๊ฒฉ๋ฆฌ ๋ฐฉ์์ธ IsoKV๋ฅผ ์ ์ํ๋ค. IsoKV๋ ์ ํ๋ฆฌ์ผ์ด์
์ค์ฌ ํ๋์ ์ ์ฅ์ฅ
์น ๊ด๋ฆฌ ๋ฐฉ์์ ํตํด SSD์ ๋ณ๋ ฌํ ์์ค์ ์ง์ ๊ด๋ฆฌํ๋ค. IsoKV๋ SSD์ ๊ฐ ์ ์ฉ
๋ด๋ถ ๋ณ๋ ฌ ์ฅ์น์ ์๋ก ๋ค๋ฅธ ํน์ฑ์ ๊ฐ์ง ๋ฐ์ดํฐ๋ฅผ ์ ์ฅํจ์ผ๋ก์จ I/O ์์ฒญ ๊ฐ์
๊ฐ์ญ์ ์ค์ธ๋ค. ๋ํ IsoKV๋ SSD์ LSM ํธ๋ฆฌ ๋ก์ง๊ณผ ๋ฐ์ดํฐ ๊ด๋ฆฌ๋ฅผ ๋๊ธฐํํ
์ฌ GC๋ฅผ ์ ๊ฑฐํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ RocksDB๋ฅผ ๊ธฐ๋ฐ์ผ๋ก IsoKV๋ฅผ ๊ตฌํํ์์ผ๋ฉฐ,
Open-Channel SSD๋ฅผ ์ฌ์ฉํ์ฌ ์ฑ๋ฅํ๊ฐํ์๋ค.. ๋ณธ ์ฐ๊ตฌ์ ์คํ ๊ฒฐ๊ณผ์ ๋ฐ๋ฅด๋ฉด
IsoKV๋ ๊ธฐ์กด์ ๋ฐ์ดํฐ ์ ์ฅ ๋ฐฉ์๊ณผ ๋น๊ตํ์ฌ ํ๊ท 1.20ร ๋น ๋ฅด๊ณ ๋ฐ 43% ๊ฐ์๋
์ฒ๋ฆฌ๋๊ณผ ์๋ต์๊ฐ ์ฑ๋ฅ ๊ฐ์ ๊ฒฐ๊ณผ๋ฅผ ์ป์๋ค. ๊ด์ ์์ 43% ๊ฐ์ํ์๋ค.Abstract
Introduction 1
Background 8
Log-Structured Merge tree based Database 8
Open-Channel SSDs 9
Preliminary Experimental Evaluation using oc bench 10
Design and Implementation 14
Overview of IsoKV 14
GC-free flash storage management synchronized with LSM-tree logic 15
I/O type Isolation through Application-Driven Flash Management 17
Dynamic Arrangement of NAND-Flash Parallelism 19
Implementation 21
Evaluation 23
Experimental Setup 23
Performance Evaluation 25
Related Work 31
Conclusion 34
Bibliography 35
์ด๋ก 40Maste
Understanding and Optimizing Flash-based Key-value Systems in Data Centers
Flash-based key-value systems are widely deployed in todayโs data centers for providing high-speed data processing services. These systems deploy flash-friendly data structures, such as slab and Log Structured Merge(LSM) tree, on flash-based Solid State Drives(SSDs) and provide efficient solutions in caching and storage scenarios. With the rapid evolution of data centers, there appear plenty of challenges and opportunities for future optimizations.
In this dissertation, we focus on understanding and optimizing flash-based key-value systems from the perspective of workloads, software, and hardware as data centers evolve. We first propose an on-line compression scheme, called SlimCache, considering the unique characteristics of key-value workloads, to virtually enlarge the cache space, increase the hit ratio, and improve the cache performance. Furthermore, to appropriately configure increasingly complex modern key-value data systems, which can have more than 50 parameters with additional hardware and system settings, we quantitatively study and compare five multi-objective optimization methods for auto-tuning the performance of an LSM-tree based key-value store in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. Last but not least, we conduct an in-depth, comprehensive measurement work on flash-optimized key-value stores with recently emerging 3D XPoint SSDs. We reveal several unexpected bottlenecks in the current key-value store design and present three exemplary case studies to showcase the efficacy of removing these bottlenecks with simple methods on 3D XPoint SSDs. Our experimental results show that our proposed solutions significantly outperform traditional methods. Our study also contributes to providing system implications for auto-tuning the key-value system on flash-based SSDs and optimizing it on revolutionary 3D XPoint based SSDs
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