Correlation-Aware Stripe Organization for Efficient Writes in Erasure-Coded Storage Systems

Zhirong Shen, P. Lee, J. Shu, Wenzhong Guo
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引用次数: 9

Abstract

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 reconsider this problem from two new perspectives: data correlation and stripe organization, and propose CASO, a correlation-aware stripe organization algorithm. CASO captures data correlation of a data access stream. 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 accesses. By differentiating correlated and uncorrelated data in stripe organization, we show via extensive trace-driven evaluation that CASO reduces up to 25.1% of parity updates and accelerates the write speed by up to 28.4%.
基于关联感知的条带组织在擦除编码存储系统中的高效写
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重新考虑了这一问题,提出了一种关联感知的条带组织算法CASO。CASO捕获数据访问流的数据相关性。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。通过区分条带组织中的相关和不相关数据,我们通过广泛的跟踪驱动评估表明,CASO减少了多达25.1%的奇偶更新,并将写入速度提高了高达28.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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