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Reparo: A Fast RAID Recovery Scheme for Ultra-large SSDs

Title
Reparo: A Fast RAID Recovery Scheme for Ultra-large SSDs
Author(s)
Hong, DuwonHa, KeonsooKo, MinseokChun, MyoungjunKim, YoonaLee, SungjinKim, Jihong
DGIST Authors
Hong, DuwonHa, KeonsooKo, MinseokChun, MyoungjunKim, YoonaLee, SungjinKim, Jihong
Issued Date
2021-08
Type
Article
Author Keywords
Die failureultra-large SSDRAIDstorage system
Keywords
HIGH-PERFORMANCE
ISSN
1553-3077
Abstract
A recent ultra-large SSD (e.g., a 32-TB SSD) provides many benefits in building cost-efficient enterprise storage systems. Owing to its large capacity, however, when such SSDs fail in a RAID storage system, a long rebuild overhead is inevitable for RAID reconstruction that requires a huge amount of data copies among SSDs. Motivated by modern SSD failure characteristics, we propose a new recovery scheme, called reparo, for a RAID storage system with ultra-large SSDs. Unlike existing RAID recovery schemes, reparo repairs a failed SSD at the NAND die granularity without replacing it with a new SSD, thus avoiding most of the inter-SSD data copies during a RAID recovery step. When a NAND die of an SSD fails, reparo exploits a multi-core processor of the SSD controller in identifying failed LBAs from the failed NAND die and recovering data from the failed LBAs. Furthermore, reparo ensures no negative post-recovery impact on the performance and lifetime of the repaired SSD. Experimental results using 32-TB enterprise SSDs show that reparo can recover from a NAND die failure about 57 times faster than the existing rebuild method while little degradation on the SSD performance and lifetime is observed after recovery. © 2021 Association for Computing Machinery.
URI
http://hdl.handle.net/20.500.11750/16127
DOI
10.1145/3450977
Publisher
Association for Computing Machinary, Inc.
Related Researcher
  • 이성진 Lee, Sungjin
  • Research Interests Computer System; System Software; Storage System; Non-volatile Memory; Flash-based SSD; Distributed Storage Systems
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Appears in Collections:
Department of Electrical Engineering and Computer Science Data-Intensive Computing Systems Laboratory 1. Journal Articles

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