电子科技大学学报2016，Vol.45Issue(2)：221-226,6.DOI:10.3969/j.issn.1001-0548.2016.03.011
MUSE：一种面向云存储系统的高性能元数据存储引擎
MUSE：A High-Performance Metadata Storage Engine for Cloud Storage System
摘要
Abstract
In the cloud storage systems, the accesses of massive small files metadata will generate a large number of random disk I/O requests, which will become the performance bottleneck of the entire storage system. In this paper, metadata unit storage engine (MUSE), a kind of metadata storage engine for cloud storage system, is proposed to support massive small files storage with high performance. Firstly, LevelDB, a high speed key-value storage engine based on LSM-tree, is used as underlying physical storage module. Secondly, LevelDB is enhanced by introducing multiple buffer tables and multiple compaction threads, which take full advantages of memory and multi-core processor. Thirdly, a new metadata accesses scheduling mechanism on multiple I/O channels is proposed. Channel is an independent data storage pipe formed by binding the independent thread to the independent physical disk. In this way, the access operations are isolated between channels, and then the aggregation of multiple channels can provide high concurrency random I/O. In addition, MUSE proposes two namespace management strategies: Split-path mapping strategy and absolute path mapping strategy, aimed to make trade-off according to different application scenarios by users. Benchmarks show that MUSE can support the massive small files storage scene and outperform other metadata storage systems.关键词
I/O/LSM-tree/海量/元数据/性能/小文件/存储引擎Key words
I/O/LSM-tree/massive/metadata/performance/small file/storage engine分类
信息技术与安全科学引用本文复制引用
段翰聪,向小可,吕鹏程..MUSE：一种面向云存储系统的高性能元数据存储引擎[J].电子科技大学学报,2016,45(2):221-226,6.基金项目
国家重大科技专项(2012ZX03002-004-004) （2012ZX03002-004-004）
