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PDS: An I/O-Efficient Scaling Scheme for Parity Declustered Data Layout

机译:PDS:平价下降数据布局的I / O高效缩放方案

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Parity declustering is widely deployed in erasure coded storage systems so as to provide fast recovery and high data availability. However, to perform scaling on such RAIDs, it is necessary to preserve the parity declustered data layout so as to guarantee the RAID performance after scaling. Unfortunately, existing scaling algorithms fail to achieve this goal so they can not be applied for scaling RAIDs which have deployed parity declustering. To address this challenge, we develop an efficient scaling algorithm called PDS (Parity Declustering Scaling). In particular, we first employ an auxiliary Balanced Incomplete Block Design (BIBD) to define the data migrations during scaling so as to preserve parity declustered data layout, and then define the addressing algorithm in the scaled system based on the migrations. We provide theoretical proofs to show that PDS preserves the parity declustered data layout, which is the basis for scaling RAIDs with parity declustering, and also theoretically prove that PDS achieves the even distribution of data/parity blocks after scaling and requires only the minimal data migrations. To show the performance of PDS, we implement it in MD in Linux Kernel, and conduct experiments with real-world traces. Results show PDS can reduce 89.70% of data migration time and 24.44% of user response time during scaling on average, compared with the round-robin scheme.
机译:奇偶校验下降广泛部署在擦除编码存储系统中,以便提供快速恢复和高数据可用性。但是,要在这种RAID上执行缩放,有必要保留奇偶校验的数据布局,以便在缩放后保证RAID性能。不幸的是,现有的缩放算法无法实现这一目标,因此他们不能应用于缩放突击队,该突击队员已经部署了奇偶校验差异。为了解决这一挑战,我们开发了一个称为PDS的高效缩放算法(奇偶校验崩溃缩放)。特别是,我们首先使用辅助平衡不完整的块设计(bibd)来定义缩放期间的数据迁移,以便保留平等的分析数据布局,然后根据迁移定义缩放系统中的寻址算法。我们提供理论证据,以表明PDS保留了奇偶校验的数据布局,这是缩放突出竞争性解冻的突袭的基础,并且理论上还证明了PDS在缩放后达到数据/奇偶校验块的均匀分布,并且只需要最小的数据迁移。为了显示PD的性能,我们在Linux内核中的MD中实现它,并与现实世界的迹线进行实验。结果表明,与循环方案相比,PDS可以减少89.70%的数据迁移时间和用户响应时间的24.44%。

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