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
复制标题
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
中科院分区:
文献类型:
--
作者:
Zhirong Shen;Patrick P.C. Lee;Jiwu Shu;Wenzhong Guo
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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