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Boafft: Distributed Deduplication for Big Data Storage in the Cloud

机译:BOAFFT:云中的大数据存储分发重复数据删除

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As data progressively grows within data centers, the cloud storage systems continuously facechallenges in saving storage capacity and providing capabilities necessary to move big data within an acceptable time frame. In this paper, we present the Boafft, a cloud storage system with distributed deduplication. The Boafft achieves scalable throughput and capacity usingmultiple data servers to deduplicate data in parallel, with a minimal loss of deduplication ratio. Firstly, the Boafft uses an efficient data routing algorithm based on data similarity that reduces the network overhead by quickly identifying the storage location. Secondly, the Boafft maintains an in-memory similarity indexing in each data server that helps avoid a large number of random disk reads and writes, which in turn accelerates local data deduplication. Thirdly, the Boafft constructs hot fingerprint cache in each data server based on access frequency, so as to improve the data deduplication ratio. Our comparative analysis with EMC's stateful routing algorithm reveals that the Boafft can provide a comparatively high deduplication ratio with a low network bandwidth overhead. Moreover, the Boafft makes better usage of the storage space, with higher read/write bandwidth and good load balance.
机译:随着数据逐渐在数据中心中增长,云存储系统在节省存储容量中连续地识别,并提供在可接受的时间帧内移动大数据所需的能力。在本文中,我们介绍了具有分布式重复数据删除的云存储系统。 BOAFFT实现了可扩展的吞吐量和能力,使用多数据服务器并行重复数据删除数据,具有最小的重复数据删除率。首先,BOAFFT基于数据相似性使用高效的数据路由算法,通过快速识别存储位置来减少网络开销。其次,BOAFFT在每个数据服务器中维护内存中的相似性索引,有助于避免大量随机磁盘读取和写入,这反过来加速了本地数据重复数据删除。第三,BOAFFT基于访问频率在每个数据服务器中构建热指纹缓存,以提高数据重复数据删除比。我们与EMC的状态路由算法的比较分析表明,BOAFFT可以提供与低网络带宽开销的相对高的重复数据删除比。此外,BOAFFT更好地使用了存储空间,具有更高的读/写带宽和良好的负载平衡。

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