Buffer Management With Append-Only Data Isolation for Improving SSD Performance
- Authors
- Jeong, Joonyong; Lee, Gyeongyong; Lee, Jungkeol; Choi, Jungwook; Song, Yong Ho
- Issue Date
- Dec-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Buffer storage; Ash; Compaction; Bars; Random access memory; Performance evaluation; Licenses; Buffer management; log-structured merge tree; multi-application; NAND flash storage
- Citation
- IEEE ACCESS, v.9, pp.157681 - 157698
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 9
- Start Page
- 157681
- End Page
- 157698
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138600
- DOI
- 10.1109/ACCESS.2021.3130278
- ISSN
- 2169-3536
- Abstract
- The number of applications that can access a storage device simultaneously has increased as a result of the increase in storage capacity and the emergence of hyperscale environments. In multi-application environments, the request for append-only data to storage from applications such as log-structured merge-tree-based key-value (LSMKV) stores can negatively affect the storage-internal buffer hit ratio of other applications. This is because frequently re- accessed data can be evicted from the buffer via the intensive requests of append-only data that are rarely re- accessed. This degradation in the buffer hit ratio increases the storage access latency of applications. Herein, we propose a buffer management method to increase the buffer hit ratio of non-append-only data (or applications) in multi-application environments. The proposed method (1) defines large-sequential writes (that are not overwritten) and all reads on them as append-only input/output (I/O), (2) detects I/O, matching the access pattern of append-only data of LSMKVs, (3) allocates the append-only read/write requests into separate small buffer spaces, and (4) evicts the append-only data from the buffer when free buffer space is required. Because the proposed method stores append-only data of an LSMKV in buffer spaces with a limited size, it can increase the buffer hit ratio of applications that frequently re- access its data. Experimental results show that the proposed method can increase the buffer hit ratio of hot-data-intensive applications and the total buffer hit ratio by 31.72% and 20.06%, on average, respectively, in comparison to the existing buffer management techniques.
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