TY - JOUR
T1 - Ripple-RAID
T2 - A high-performance and energy-efficient raid for continuous data storage
AU - Sun, Zhi Zhuo
AU - Zhang, Quan Xin
AU - Tan, Yu An
AU - Li, Yuan Zhang
N1 - Publisher Copyright:
© Copyright 2015, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - The applications, such as video surveillance, backup and archiving, have inherent workload characteristic and I/O access pattern, and require specialized optimization for storage energy saving. Partial parallelism in RAID is beneficial to storage energy saving, but generally makes RAID perform small writes, which heavily deteriorates the performance. A high-performance and energy-efficient RAID, Ripple-RAID, is proposed for these applications. A new partial-parallel data layout is presented, and by comprehensively employing strategies such as address mapping, out-place update, generating parity data progressively based on pipeline, and segmented data recovery, Ripple-RAID not only obtains energy efficiency of partial parallelism but also eliminates the small writes incurred by partial parallelism while providing single disk fault tolerance. When write workload is 80% sequential and transfer request size is 512KB, the write performance of Ripple-RAID is 3.9 times that of S-RAID 5, 1.9 times that of Hibernator and MAID, and 0.49 times that of PARAID and eRAID 5; meanwhile its energy consumption is 20% less than S-RAID 5, 33% less than Hibernator and MAID, 70% less than eRAID 5, and 72% less than PARAID.
AB - The applications, such as video surveillance, backup and archiving, have inherent workload characteristic and I/O access pattern, and require specialized optimization for storage energy saving. Partial parallelism in RAID is beneficial to storage energy saving, but generally makes RAID perform small writes, which heavily deteriorates the performance. A high-performance and energy-efficient RAID, Ripple-RAID, is proposed for these applications. A new partial-parallel data layout is presented, and by comprehensively employing strategies such as address mapping, out-place update, generating parity data progressively based on pipeline, and segmented data recovery, Ripple-RAID not only obtains energy efficiency of partial parallelism but also eliminates the small writes incurred by partial parallelism while providing single disk fault tolerance. When write workload is 80% sequential and transfer request size is 512KB, the write performance of Ripple-RAID is 3.9 times that of S-RAID 5, 1.9 times that of Hibernator and MAID, and 0.49 times that of PARAID and eRAID 5; meanwhile its energy consumption is 20% less than S-RAID 5, 33% less than Hibernator and MAID, 70% less than eRAID 5, and 72% less than PARAID.
KW - Archiving
KW - Continuous data storage
KW - Energy efficiency
KW - High performance
KW - RAID
KW - Video surveillance
UR - http://www.scopus.com/inward/record.url?scp=84938322070&partnerID=8YFLogxK
U2 - 10.13328/j.cnki.jos.004606
DO - 10.13328/j.cnki.jos.004606
M3 - Article
AN - SCOPUS:84938322070
SN - 1000-9825
VL - 26
SP - 1824
EP - 1839
JO - Ruan Jian Xue Bao/Journal of Software
JF - Ruan Jian Xue Bao/Journal of Software
IS - 7
ER -