Rethinking RAID-5 Data Layout for Better Scalability

Zhang, Guangyan; Zheng, Weimin; Li, Keqin

doi:10.1109/tc.2013.143

Cited by 32 publications

(16 citation statements)

References 20 publications

(27 reference statements)

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“…MiPiL, for instance, minimise data migration while maintaining uniform data and the parity distribution of RAID-5 [15]. Moreover, a diagonal coding scheme is introduced for system-level wear levelling which prevents rapid wear out due to updating dependencies between actual and parity data [16].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Reliability and performance enhancements for SSD RAID

McEwan

Komsul

2017

Microprocessors and Microsystems

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NAND based solid state storage devices are almost ubiquitously used in safety-critical embedded devices, and recent advances have demonstrated RAID architectures specific to solid state storage devices resulting in increased data reliability, with architectural enhancements to solve the age convergence problem. However, these techniques require devices to be taken off-line while components are replaced-consequently these devices are of limited use in hard real time systems. There are further real time issues in that the conventional architectures ignore other characteristics of solid state devices such as garbage collection and meta data management. In this paper we investigate techniques that support the replacement of aged devices in the array in such a way that we provide continuous system reliability. We also improve the performance overhead of the reconstruction process using a novel data migration policy. The techniques are implemented and tested in a trace-driven simulator, and results demonstrate that average I/O response time is improved by up to 39% with improvement by up to 45% in its standard deviation, overheads in terms of device replacement time are negligible, and read performance is improved by an average of 8%.

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Section: Motivation and Related Workmentioning

confidence: 99%

Reliability and performance enhancements for SSD RAID

McEwan

Komsul

2017

Microprocessors and Microsystems

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“…Existing RAID scaling approaches focus on RAID-0 [25], [26], RAID-4 [31], RAID-5 [27], [28], [32], [33], [34], [35], and RAID-6 [29], [30], [36]. Different from RAID-0 scaling solutions that only address data migrations, cluster scaling must handle data migrations as well as parity updates.…”

Section: Challenges and Strategiesmentioning

confidence: 99%

“…Existing scaling schemes designed for RAIDs include SLAS [26], SCADDAR [47], FastScale [25], McPod [31], ALV [32], MiPiL [33], MDM [28], GA [34], GSR [27], PBM [35], SDM [29], and so on. Data redistribution in scaling RScoded chunk groups consists of data migration and parity update; the data migration can be completed using RAID-0 scaling approaches (e.g., Round-Robin, FastScale, etc.).…”

Section: Storage Scaling Schemesmentioning

confidence: 99%

Scale-RS: An Efficient Scaling Scheme for RS-Coded Storage Clusters

Huang

Liang

Qin

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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It is indispensable to scale erasure-coded storage clusters to meet requirements of increased storage capacity and I/O performance. In this study, we propose an efficient scaling scheme for Reed-Solomon-coded storage clusters called Scale-RS, which has three salient features. First, Scale-RS achieves uniform data distribution by equally placing data blocks among old and new chunks using a transposed data layout. Second, Scale-RS minimizes data movement incurred in the procedures of data redistribution and parity update. Scale-RS not only reaches the lower bound of data migration traffic by transferring necessary data blocks from old data chunks to new chunks, but it also reduces update traffic via generating parity difference blocks from data blocks stored in an individual data chunk. Third, Scale-RS improves the I/O performance of scaled storage clusters in terms of read parallelism and write throughput. We implement Scale-RS along with two alternative scaling schemes in a Reed-Solomon-coded storage cluster, on which real-world I/O traces are replayed. Experimental results demonstrate that Scale-RS achieves the highest read performance among the three scaling schemes after data redistribution. When it comes to scaling from six data chunks to nine, Scale-RS can outperform the other two scaling schemes in terms of aggregate write throughput by a factor of 2.85 and 3.05 under online filling and offline filling, respectively. We also show that user response time is slightly enlarged during data redistribution due to bandwidth competition between migration and user I/Os.

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“…However, for longer sync intervals (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), the accumulated IB may need two or more scattered writes and is outperformed by the merged IL. The basic IL records a log at every cache synchronization and causes severe performance degradation due to frequent synchronization.…”

Section: Synchronous Writesmentioning

confidence: 99%

“…Research topics on RAID include access method [2,3], reconstruction [4][5][6][7], scrubbing [8][9][10], scaling [11][12][13][14][15], data layout [16][17][18][19][20][21], erasure code [22][23][24][25][26], and resynchronization [27][28][29]. This paper is focused on resynchronization for software RAID systems.…”

Section: Introductionmentioning

confidence: 99%