Tiered Storage in Modern Key-Value Stores: Performance, Storage-Efficiency, and Cost-Efficiency Considerations

被引:1
|
作者
Jaranilla, Charles [1 ]
Shin, Hojin [1 ]
Yoo, Seehwan [1 ]
Cho, Seong-Je [1 ]
Choi, Jongmoo [1 ]
机构
[1] Dankook Univ, Dept Software, Yongin, South Korea
基金
新加坡国家研究基金会;
关键词
Tiered storage; SSD; key-value store; compression; analysis;
D O I
10.1109/BigComp60711.2024.00032
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The emergence of big data has experienced a significant increase in both the variety and volume of data which prompted the development of faster, more reliable storage solutions. The primary objective of this study is to investigate how varying latencies of diverse storage media impact the performance of a representative KVS in RocksDB and to ascertain whether employing lower-cost storage media at the bottom levels can lead to efficient space utilization and cost savings. We implement tiered SSD storage into RocksDB to analyze the trade-offs in employing a diversified setup. Our findings reveal that, despite differences in latency, RocksDB demonstrates remarkable resilience in mitigating the impact of latency variations. Furthermore, to facilitate better storage utilization, we take advantage of RocksDB's flexibility by utilizing different compression techniques in tiered storage while considering their characteristics. Results show that while our strategy does not overcome the performance of Optane SSD, it has a negligible difference with NVMe SSD in terms of write and read throughput. With hybrid compression, compared to SATA SSD, the tiered storage demonstrates a difference of 13.8 MBps in write and 3.86 MBps in read workloads. It also provides a cost-efficient storage option with cost similar to that of SATA SSD but with performance comparable to NVMe SSD. This research offers valuable guidance for organizations aiming to optimize their storage infrastructure and reduce storage upgrade costs while maintaining database efficiency.
引用
收藏
页码:151 / 158
页数:8
相关论文
共 31 条
  • [1] Improving Performance of Key-Value Stores for High-Performance Storage Devices
    Kim, Sunggon
    Kim, Hwajung
    APPLIED SCIENCES-BASEL, 2024, 14 (17):
  • [2] Benchmarking Key-Value Stores on High-Performance Storage and Interconnects for Web-Scale Workloads
    Shankar, Dipti
    Lu, Xiaoyi
    Wasi-ur-Rahman, Md.
    Islam, Nusrat
    Panda, Dhabaleswar K.
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2015, : 539 - 544
  • [3] Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores
    Lin, Heng
    Wang, Zhiyong
    Qi, Shipeng
    Zhu, Xiaowei
    Hong, Chuntao
    Chen, Wenguang
    Luo, Yingwei
    BIG DATA MINING AND ANALYTICS, 2024, 7 (01): : 156 - 170
  • [4] Scaling Persistent In-Memory Key-Value Stores Over Modern Tiered, Heterogeneous Memory Hierarchies
    Cai, Miao
    Shen, Junru
    Yuan, Yifan
    Qu, Zhihao
    Ye, Baoliu
    IEEE TRANSACTIONS ON COMPUTERS, 2025, 74 (02) : 495 - 509
  • [5] Design Considerations of A Novel Distributed Key-Value Store for New Storage
    Liu, Ruicheng
    Jin, Peiquan
    Wang, Xiaoliang
    Luo, Yongping
    Chu, Zhaole
    2022 IEEE 42ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2022), 2022, : 1276 - 1277
  • [6] PRISM: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices
    Song, Yongju
    Kim, Wook-Hee
    Monga, Sumit Kumar
    Min, Changwoo
    Eom, Young Ik
    PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON ARCHITECTURAL SUPPORT FOR PROGRAMMING LANGUAGES AND OPERATING SYSTEMS, VOL 2, ASPLOS 2023, 2023, : 588 - 602
  • [7] TreeLine: An Update-In-Place Key-Value Store for Modern Storage
    Yu, Geoffrey X.
    Markakis, Markos
    Kipf, Andreas
    Larson, Per-Ake
    Minhas, Umar Farooq
    Kraska, Tim
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2022, 16 (01): : 99 - 112
  • [8] A DHT Key-Value Storage System with Carrier Grade Performance
    Shi, Guangyu
    Chen, Jian
    Gong, Hao
    Fan, Lingyuan
    Xue, Haiqiang
    Lu, Qingming
    Liang, Liang
    EURO-PAR 2009: PARALLEL PROCESSING, PROCEEDINGS, 2009, 5704 : 361 - +
  • [9] Power-optimized Deployment of Key-value Stores Using Storage Class Memory
    Kassa, Hiwot Tadese
    Akers, Jason
    Ghosh, Mrinmoy
    Cao, Zhichao
    Gogte, Vaibhav
    Dreslinski, Ronald
    ACM TRANSACTIONS ON STORAGE, 2022, 18 (02)
  • [10] Cloud Storage with Key-Value Stores over Content-Centric Networking Architecture
    Ito, Daiki
    Mohri, Masami
    Shiraishi, Yoshiaki
    Morii, Masakatu
    2016 IEEE 7TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS MOBILE COMMUNICATION CONFERENCE (UEMCON), 2016,