EDM:基于SSD存储集群负载均衡的持久性数据迁移方案

Jiaxin Ou, J. Shu, Youyou Lu, Letian Yi, Wei Wang
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引用次数: 35

摘要

数据迁移方案是实现存储集群负载均衡和性能提升的关键。然而,随着基于NAND闪存的SSD硬盘在存储系统中的广泛应用,延长SSD存储集群的寿命成为数据迁移的新挑战。然而,先前为HDD存储集群设计的方法由于数据迁移过程中过度的写放大而效率低下,这大大降低了SSD存储集群的寿命。为了克服这个问题,我们提出了EDM,这是一种耐力感知的数据迁移方案,通过仔细的数据放置和移动来最小化迁移的数据,从而在提高性能的同时限制ssd的磨损。由于SSD的磨损速度主要影响性能下降,而磨损速度又受存储利用率和写强度的影响,因此设计了两种互补的数据迁移策略,以探索SSD存储集群的吞吐量、迁移响应时间和生命周期之间的权衡。此外,我们设计了SSD磨损模型,定量计算迁移的数据量以及迁移的来源和目的地,以减少迁移带来的写放大。在真实存储集群上使用真实跟踪的结果表明,EDM比现有的基于HDD的迁移技术性能更好,可将集群范围内的总擦除计数减少多达40%。与此同时,与基线系统相比,它平均提高了25%的性能,基线系统实现了与以前的迁移技术几乎相同的性能改进效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EDM: An Endurance-Aware Data Migration Scheme for Load Balancing in SSD Storage Clusters
Data migration schemes are critical to balance the load in storage clusters for performance improvement. However, as NAND flash based SSDs are widely deployed in storage systems, extending the lifespan of SSD storage clusters becomes a new challenge for data migration. Prior approaches designed for HDD storage clusters, however, are inefficient due to excessive write amplification during data migration, which significantly decrease the lifespan of SSD storage clusters. To overcome this problem, we propose EDM, an endurance aware data migration scheme with careful data placement and movement to minimize the data migrated, so as to limit the worn-out of SSDs while improving the performance. Based on the observation that performance degradation is dominated by the wear speed of an SSD, which is affected by both the storage utilization and the write intensity, two complementary data migration policies are designed to explore the trade-offs among throughput, response time during migration, and lifetime of SSD storage clusters. Moreover, we design an SSD wear model and quantitatively calculate the amount of data migrated as well as the sources and destinations of the migration, so as to reduce the write amplification caused by migration. Results on a real storage cluster using real-world traces show that EDM performs favorably versus existing HDD based migration techniques, reducing cluster-wide aggregate erase count by up to 40%. In the meantime, it improves the performance by 25% on average compared to the baseline system which achieves almost the same effectiveness of performance improvement as previous migration techniques.
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