Correlation-Aware Stripe Organization for Efficient Writes in Erasure-Coded Storage: Algorithms and Evaluation

Correlation-Aware Stripe Organization for Efficient Writes in Erasure-Coded Storage: Algorithms and Evaluation
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用于纠删码存储中高效写入的相关感知条带组织:算法和评估

DOI:
10.1109/tpds.2018.2890635
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发表时间:
2019-07
影响因子:
5.3
通讯作者:
Wenzhong Guo
Wenzhong Guo
中科院分区:
计算机科学2区
文献类型:
--
作者:
Zhirong Shen;Patrick P.C. Lee;Jiwu Shu;Wenzhong Guo

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Erasure编码已被广泛应用于生产存储系统的数据可用性保护,以保持低程度的数据冗余。然而,如何减轻在擦除编码存储系统中部分条带写入的奇偶校验更新开销仍然是一个关键问题。本文从数据关联和条带组织两个新的角度对这一问题进行了研究。我们提出了一种关联感知的条带组织算法$\mathsf{CASO}$CASO,它捕获数据访问流的数据相关性,并利用数据相关性特征进行条带组织。它将相关数据打包到少量的条带中,以减少部分条带写入时产生的I/ o,并进一步将不相关的数据组织到条带中,以便在以后的访问中利用空间局部性。我们在Reed-Solomon代码和Azure的本地重建代码上实现了$\mathsf{CASO}$CASO,并通过广泛的跟踪驱动评估显示,$\mathsf{CASO}$CASO减少了29.8%的奇偶更新,减少了46.7%的写时间。
Erasure coding has been extensively employed for data availability protection in production storage systems by maintaining a low degree of data redundancy. However, how to mitigate the parity update overhead of partial stripe writes in erasure-coded storage systems is still a critical concern. In this paper, we study this problem from two new perspectives: data correlation and stripe organization. We propose $\mathsf{CASO}$CASO, a correlation-aware stripe organization algorithm, which captures data correlation of a data access stream and uses the data correlation characteristics for stripe organization. It packs correlated data into a small number of stripes to reduce the incurred I/Os in partial stripe writes, and further organizes uncorrelated data into stripes to leverage the spatial locality in later access. We implement $\mathsf{CASO}$CASO over Reed-Solomon codes and Azure's Local Reconstruction Codes, and show via extensive trace-driven evaluation that $\mathsf{CASO}$CASO reduces up to 29.8 percent of parity updates and reduces the write time by up to 46.7 percent.
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