Design and implementation of an efficient flushing scheme for cloud key-value storage
- Authors
- Son, Y.; Yeom, H.Y.; Han, H.
- Issue Date
- Dec-2017
- Publisher
- Springer New York LLC
- Keywords
- Flash-based SSD; Key-value storage; Write-ahead logging
- Citation
- Cluster Computing, v.20, no.4, pp 3551 - 3563
- Pages
- 13
- Journal Title
- Cluster Computing
- Volume
- 20
- Number
- 4
- Start Page
- 3551
- End Page
- 3563
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60828
- DOI
- 10.1007/s10586-017-1101-3
- ISSN
- 1386-7857
1573-7543
- Abstract
- A key-value store is an essential component that is increasingly demanded in many scale-out environments, including social networks, online retail environments, and cloud services. Modern key-value storage engines provide many features, including transaction, versioning, and replication. In storage engines, transaction processing provides atomicity and durability by using write-ahead logging (WAL), which flushes log data before the data page is written to persistent storage in synchronous commit. However, according to our observation, WAL is a performance bottleneck in key-value storage engines since the flushing of log data to persistent storage incurs a significant overhead of lock contention and fsync() calls, even with the various optimizations in the existing scheme. In this article, we propose an approach to improve the performance of key-value storage by optimizing the existing flushing scheme combined with group commit and consolidate array. Our scheme aggregates the multiple flushing of log data into a large request on the fly and completes the request early. This scheme is an efficient group commit that reduces the number of frequent lock acquisitions and fsync() calls in the synchronous commit while supporting the same transaction level that the existing scheme provides. Furthermore, we integrate our flushing scheme into the replication system and evaluate it by using multiple nodes. We implement our scheme on the WiredTiger storage engine. The experimental results show that our scheme improves the performance of the key-value workload compared to the existing scheme.
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