TrickleKV: A High-Performance Key-Value Store on Disaggregated Storage With Low Network Traffic

被引:0
|
作者
Zhan, Ling [1 ]
Lu, Kai [2 ]
Xiong, Yiqin [2 ]
Wan, Jiguang [2 ]
Yang, Zixuan [3 ]
机构
[1] Wenhua Coll, Fac Informat Sci & Technol, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China
[3] Fujian Normal Univ, Sch Big Data & Artificial Intelligence, Fuzhou 350007, Peoples R China
来源
IEEE ACCESS | 2024年 / 12卷
基金
中国国家自然科学基金;
关键词
Disaggregated storage; key-value store; NVMe over fabrics; remote direct memory access; NEURAL-NETWORKS; CLASSIFICATION; AREA;
D O I
10.1109/ACCESS.2024.3496880
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Disaggregated storage (DS) based on remote direct memory access (RDMA) network decouples compute and storage resources, thereby significantly improving resource utilization. While building key-value (KV) stores on DS benefits from these merits, existing fast KV stores suffer from network bandwidth contention and high latency under DS due to the non-negligible network amplification and high-overhead I/O stack. In this paper, we propose TrickleKV, a high-performance persistent KV store designed for DS. TrickleKV reduces network amplification and latency in three approaches: 1) TrickleKV proposes an efficient storage-side data filtering mechanism and a two-level cache structure with different granularities to reduce network traffic in the read process. 2) TrickleKV presents an efficient write buffer structure that includes asynchronous flushing and queue scheduling mechanisms to reduce network traffic in the write process. 3) TrickleKV designs a read-write decoupled user-space I/O stack and lightweight storage space management to reduce access latency. Evaluation results show that TrickleKV achieves 1.2x - 7x higher throughput and 30%- 7.4x lower latency compared to state-of-the-art KV stores under DS.
引用
收藏
页码:167596 / 167612
页数:17
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