Fast Erasure Coding for Data Storage: A Comprehensive Study of the Acceleration Techniques. Tianli Zhou & Chao Tian Texas A&M University
|
|
|
- Jean Foster
- 5 years ago
- Views:
Transcription
1 Fast Erasure Coding for Data Storage: A Comprehensive Study of the Acceleration Techniques Tianli Zhou & Chao Tian Texas A&M University
2 2 Contents Motivation Background and Review Evaluating Individual Techniques Find the Best Strategy under Optimized Bitmatrix Proposed Coding Procedure and Evaluation Conclusion
3 3 Contents Motivation Background and Review Evaluating Individual Techniques Find the Best Strategy under Optimized Bitmatrix Proposed Coding Procedure and Evaluation Conclusion
4 Data Reliability 4
5 4 Data Reliability Disaster happens
6 4 Data Reliability Disaster happens Disk failure Server malfunction and more...
7 5 Replication - Erasure Code Erasure Code Computational Overhead Replication Space Overhead
8 Replication - Erasure Code Minimum space overhead Much more computational load Erasure code relies on Galois Field (finite field) ops Erasure Code Computational Overhead Replication Space Overhead 5
9 Replication - Erasure Code Minimum space overhead Much more computational load Erasure code relies on Galois Field (finite field) ops How to reduce it Erasure Code Computational Overhead Replication Space Overhead 5
10 6 Acceleration Techniques Optimize Coding Matrix Schedule Computing Order Covert to XOR operations Apply SIMD Specially Designed Code Optimize for CPU Cache
11 7 Acceleration Techniques Fast GF Coding Covert to XOR operations Optimize Coding Matrix Schedule Computing Order Coding Theory Specially Designed Code Computation Resource Apply SIMD Optimize for CPU Cache
12 7 Acceleration Techniques Fast GF Coding Covert to XOR operations Optimize Coding Matrix Schedule Computing Order? Coding Theory Specially Designed Code Computation Resource Apply SIMD Optimize for CPU Cache
13 8 Question to Answer Most effective technique(s)? Utilize together? Components to be optimized? Problem is still important Reduce energy consumption Virtualized environment Mobile / embedded system Future I/O technology
14 9 Contents Motivation Background and Review Evaluating Individual Techniques Find the Best Strategy under Optimized Bitmatrix Proposed Coding Procedure and Evaluation Conclusion
15 10 Erasure Code storage nodes Store coded data to multiple nodes
16 11 Erasure Code storage nodes Recover even nodes failures happen
17 12 Erasure Code storage nodes Encode k=3 segments n=5 coded nodes
18 12 Erasure Code storage nodes Encode k=3 segments n=5 coded nodes
19 13 Erasure Code storage nodes k=3 segments MDS code: recover data from any k surviving nodes
20 14 Erasure Code storage nodes k=3 segments MDS code: recover data from any k surviving nodes
21 15 Erasure Code storage nodes k=3 segments k=3 systematic nodes m=2 parity nodes
22 15 Erasure Code storage nodes k=3 segments k=3 systematic nodes m=2 parity nodes
23 16 Encoding Matrix multiplication on finite field I P X =
24 16 Encoding Matrix multiplication on finite field I P X = All elements and ops in GF(2 w ) Galois Field (GF) Finite field
25 17 Decoding I P
26 17 Decoding I P
27 17 Decoding Submatrix corresponding to surviving data I P
28 Decoding Submatrix corresponding to surviving data Invert I P -1 17
29 Decoding Submatrix corresponding to surviving data Invert Multiplication I P -1 X = 17
30 18 Cauchy Reed-Solomon Codes Direct assign P to Cauchy matrix I P
31 Cauchy Reed-Solomon Codes 19
32 19 Cauchy Reed-Solomon Codes All elements and ops in GF(2 w )
33 20 Cauchy Reed-Solomon Codes Direct assign P to Cauchy matrix I P
34 21 Fast GF Ops Binary Representation Binary representation of GF elements P All coefficients and messages are in GF(2 w )
35 22 Fast GF Ops Binary Representation Message bit-vector representation P
36 22 Fast GF Ops Binary Representation Message bit-vector representation P
37 22 Fast GF Ops Binary Representation Message bit-vector representation P 1 by w bit-vector
38 23 Fast GF Ops Binary Representation Bitmatrix representation P
39 24 Fast GF Ops Binary Representation Bitmatrix representation P
40 24 Fast GF Ops Binary Representation Bitmatrix representation P w by w bitmatrix
41 25 Fast GF Ops Binary Representation Matrix multiplication on GF -> XORs P
42 25 Fast GF Ops Binary Representation Matrix multiplication on GF -> XORs P
43 25 Fast GF Ops Binary Representation Matrix multiplication on GF -> XORs P
44 25 Fast GF Ops Binary Representation Matrix multiplication on GF -> XORs P
45 26 Contents Motivation Background and Review Evaluating Individual Techniques Find the Best Strategy under Optimized Bitmatrix Proposed Coding Procedure and Evaluation Conclusion
46 Techniques Tiers 27
47 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
48 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
49 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
50 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
51 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
52 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
53 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
54 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
55 Bitmatrix Normalization (BN) Normalize by each element, find less bit 1s I P James S. Plank, Scott Simmerman, and Catherine D. Schuman. Jerasure: A library in C/C++ facilitating erasure coding for storage applications-version 1.2. Technical Report CS , University of Tennessee, 2008.
56 Smart Scheduling (SS) Utilize both message and computed parities James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
57 Smart Scheduling (SS) Utilize both message and computed parities 3 XORs 3 XORs James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
58 Smart Scheduling (SS) Utilize both message and computed parities 3 XORs 3 XORs James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
59 Smart Scheduling (SS) Utilize both message and computed parities 3 XORs 3 XORs James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
60 Smart Scheduling (SS) Utilize both message and computed parities 3 XORs 3 XORs James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
61 Smart Scheduling (SS) Utilize both message and computed parities 3 XORs 3 XORs James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
62 Smart Scheduling (SS) Utilize both message and computed parities 3 XORs 3 XORs 2 XORs James S. Plank. The RAID-6 liberation code. In Proceedings of the 6th Usenix Conference on File and Storage Technologies, pages ,
63 Matching (UM, WM) Common XORs of pair message bits Unweighted/weighted greedy algorithm P Cheng Huang, Jin Li, and Minghua Chen. On optimizing XOR-based codes for fault-tolerant storage applications. In Proceedings of Information Theory Workshop, pages ,
64 Matching (UM, WM) Common XORs of pair message bits Unweighted/weighted greedy algorithm P Cheng Huang, Jin Li, and Minghua Chen. On optimizing XOR-based codes for fault-tolerant storage applications. In Proceedings of Information Theory Workshop, pages ,
65 Matching (UM, WM) Common XORs of pair message bits Unweighted/weighted greedy algorithm P Cheng Huang, Jin Li, and Minghua Chen. On optimizing XOR-based codes for fault-tolerant storage applications. In Proceedings of Information Theory Workshop, pages ,
66 Matching (UM, WM) Common XORs of pair message bits Unweighted/weighted greedy algorithm P Cheng Huang, Jin Li, and Minghua Chen. On optimizing XOR-based codes for fault-tolerant storage applications. In Proceedings of Information Theory Workshop, pages ,
67 Scheduling - Cache Optimization (S-CO) Reduce cache miss penalty Cache Memory Jianqiang Luo, Mochan Shrestha, Lihao Xu, and James S. Plank. Efficient encoding schedules for XOR-based erasure codes. IEEE Transactions on Computers, 63(9): ,
68 Scheduling - Cache Optimization (S-CO) Reduce cache miss penalty Cache Memory Jianqiang Luo, Mochan Shrestha, Lihao Xu, and James S. Plank. Efficient encoding schedules for XOR-based erasure codes. IEEE Transactions on Computers, 63(9): ,
69 Scheduling - Cache Optimization (S-CO) Reduce cache miss penalty Cache Memory Jianqiang Luo, Mochan Shrestha, Lihao Xu, and James S. Plank. Efficient encoding schedules for XOR-based erasure codes. IEEE Transactions on Computers, 63(9): ,
70 Scheduling - Cache Optimization (S-CO) Reduce cache miss penalty Cache Memory Jianqiang Luo, Mochan Shrestha, Lihao Xu, and James S. Plank. Efficient encoding schedules for XOR-based erasure codes. IEEE Transactions on Computers, 63(9): ,
71 Vectorization Using SIMD ISA perform multiple bits operation SSE: 128 bits AVX2: 256 bits AVX512: 512 bits 35
72 Vectorization Using SIMD ISA perform multiple bits operation SSE: 128 bits V-XOR AVX2: 256 bits AVX512: 512 bits 35
73 Vectorization Using SIMD ISA perform multiple bits operation SSE: 128 bits V-XOR AVX2: 256 bits AVX512: 512 bits V-GF X 5 X
74 Vectorization Using SIMD ISA perform multiple bits operation SSE: 128 bits V-XOR AVX2: 256 bits AVX512: 512 bits V-GF X 5 X Jerasure
75 Vectorization Using SIMD ISA perform multiple bits operation SSE: 128 bits AVX2: 256 bits V-XOR v.s AVX512: 512 bits V-GF X 5 X Jerasure
76 Vectorization Using SIMD ISA perform multiple bits operation SSE: 128 bits AVX2: 256 bits V-XOR v.s AVX512: 512 bits V-GF X 5 X Jerasure
77 Scheduling - Cache Optimization (S-CO) Reduce cache miss penalty Cache Memory Jianqiang Luo, Mochan Shrestha, Lihao Xu, and James S. Plank. Efficient encoding schedules for XOR-based erasure codes. IEEE Transactions on Computers, 63(9): ,
78 Scheduling - Cache Optimization (S-CO) Reduce cache miss penalty u 0 p 0,p 1,p 2... Cache u 1 p 2,p 3,p 5... u 2 p 0,p 1,p 4... u 3 p 2,p 6,p 8... Memory Jianqiang Luo, Mochan Shrestha, Lihao Xu, and James S. Plank. Efficient encoding schedules for XOR-based erasure codes. IEEE Transactions on Computers, 63(9): ,
79 Question to Answer Most effective technique(s)? Utilize together? Components to be optimized? 37
80 Individual Techniques 38
81 38 Individual Techniques XOR-based Vectorization
82 38 Individual Techniques XOR-based Vectorization Bitmatrix Normalization Unweighted Matching Weighted Matching
83 38 Individual Techniques XOR-based Vectorization Bitmatrix Normalization Smart Scheduling Unweighted Matching Weighted Matching
84 38 Individual Techniques Cache Optimization XOR-based Vectorization Bitmatrix Normalization Smart Scheduling Unweighted Matching Weighted Matching
85 Optimization Tiers 39
86 Optimization Tiers 39
87 40 Optimization Tiers Generate Coding Schedule Run Coding Schedule
88 41 Optimization Tiers Generate Coding Schedule Run Coding Schedule
89 41 Optimization Tiers By-pass By-pass Generate Coding Schedule Run Coding Schedule
90 41 Optimization Tiers Unweighted Matching (UM) Weighted Matching (WM) By-pass By-pass Generate Coding Schedule Run Coding Schedule
91 Question to Answer Most effective technique(s)? Utilize together? Components to be optimized? 42
92 43 Combinations (i,j)-strategy By-pass Normalization (BN) By-pass Smart Scheduling (SS) Unweighted Matching (UM) Weighted Matching (WM)
93 43 Combinations (i,j)-strategy By-pass Normalization (BN) i 0,1 By-pass Smart Scheduling (SS) Unweighted Matching (UM) Weighted Matching (WM) j 0,3
94 43 Combinations (i,j)-strategy Example: strategy-(1,1) By-pass Normalization (BN) i 0,1 By-pass Smart Scheduling (SS) Unweighted Matching (UM) Weighted Matching (WM) j 0,3
95 43 Combinations (i,j)-strategy Example: strategy-(1,1) By-pass Normalization (BN) i 0,1 By-pass Smart Scheduling (SS) Unweighted Matching (UM) Weighted Matching (WM) j 0,3
96 44 Contents Motivation Background and Review Evaluating Individual Techniques Find the Best Strategy under Optimized Bitmatrix Proposed Coding Procedure and Evaluation Conclusion
97 Question to Answer Most effective technique(s)? Utilize together? Components to be optimized? 45
98 46 Choice of Cauchy Matrix Different X,Y array yields different Cauchy Matrix
99 46 Choice of Cauchy Matrix Different X,Y array yields different Cauchy Matrix
100 46 Choice of Cauchy Matrix Different X,Y array yields different Cauchy Matrix
101 46 Choice of Cauchy Matrix Different X,Y array yields different Cauchy Matrix
102 47 Choice of Cauchy Matrix Different Cauchy Matrix affect coding chain By-pass Normalization (BN) X,Y array By-pass Smart Scheduling (SS) Unweighted Matching (UM) Weighted Matching (WM) Coding schedule, (memcpy, XOR)
103 Choice of Cauchy Matrix Different Cauchy Matrix affect coding chain By-pass Normalization (BN) X,Y array By-pass Smart Scheduling (SS) Unweighted Matching (UM) Weighted Matching (WM) Coding schedule, (memcpy, XOR) Cost of given X,Y array 47
104 Bitmatrix Optimization Individual bitmatrix optimization for all (i,j)-strategies Simulated Annealing and Genetic Algorithm # of operations as cost 48
105 [no_opt by SA by GA ], # of ops in schedule 49
106 [no_opt by SA by GA ], # of ops in schedule 49
![[no_opt by SA by GA ],](/docs-images/94/120378236/images/107-1.jpg "# of ops in schedule")
107 [no_opt by SA by GA ], # of ops in schedule 49
108 Strategies-(i,j) with Optimized Bitmatrices strategy-(1,3) is the best strategy 50
109 Optimized Bitmatrix Reduced Costs 51
110 Cost Function Improvement Memcpy is 1.5x faster than XOR Cost functions: Total # of XORs in schedule Total # of ops in schedule Weighted sum of ops in schedule Table 4: Encoding throughput (GB/s) using bitmatrices obtained by the genetic algorithm under different cost functions 52
111 53 Contents Motivation Background and Review Evaluating Individual Techniques Find the Best Strategy under Optimized Bitmatrix Proposed Coding Procedure and Evaluation Conclusion
112 54 Proposed Coding Procedure Pre-optimized Bitmatrix WM
113 54 Proposed Coding Procedure Pre-optimized Bitmatrix Coding Schedule WM
114 Testing Setup Ryzen 3.4Ghz (8C/16T) 16GB DDR4 Ubuntu bit, GCC Jerasure 1.2A/ Jerasure 2.0: XOR-based Cauchy RS code (BN, SS applied) GF-based RS code Raid-6 Other codes: EVENODD RDP STAR 55
115 56 Encoding v.s. Efficient RS/CRS code Table 5: Encoding throughput (GB/s) for methods that allow general (n, k) parameters and w = 8 Outperform vectorized XOR-based CRS code for m>2
116 57 Encoding v.s. Efficient RS/CRS code Table 5: Encoding throughput (GB/s) for methods that allow general (n, k) parameters and w = 8 Outperform vectorized XOR-based CRS code for m>2 Outperform vectorized GF-based RS code with big margin (~ 2x faster)
117 58 Encoding v.s. Three Parities Codes Similar or better performance than specially designed codes
118 59 Encoding v.s. Two Parities Codes Similar or better performance than specially designed codes
119 Overall Encoding Improvement 60
120 Decoding 61
121 61 Decoding Direct read in most cases
122 61 Decoding Direct read in most cases Degraded read (decoding) only if systematic nodes fail
123 61 Decoding Direct read in most cases Degraded read (decoding) only if systematic nodes fail Single disk failure rate : 1% In (10,6) erasure code storage system 1 systematic node failure: ~ systematic node failure: ~ systematic node failure: ~ 1.8x systematic node failure: ~ 1.4x10-7
124 61 Decoding Direct read in most cases Degraded read (decoding) only if systematic nodes fail Single disk failure rate : 1% In (10,6) erasure code storage system 1 systematic node failure: ~ systematic node failure: ~ systematic node failure: ~ 1.8x systematic node failure: ~ 1.4x10-7 Decoding performance should be viewed as secondary importance
125 Decoding Throughput 62
126 63 Contents Motivation Background and Review Individual Techniques and Combinations Bitmatrix Optimization Proposed Coding Procedure and Evaluation Conclusion
127 Conclusion Comprehensive study of acceleration techniques Combine existing techniques and jointly optimize the bitmatrix Proposed approach outperforms most existing approaches in encoding throughput. 64
128 Conclusion : Key Finding Vectorization at XOR-level is much better choice than vectorization of finite field operations Higher throughput (~ 2x faster) Easy migration to newer SIMD ISA SSE: 128 bit AVX2: 256 bit AVX-512: 512 bit 65
129 Thank you! Questions? Tianli Zhou: Chao Tian: Source code will be released soon 66
A Performance Evaluation of Open Source Erasure Codes for Storage Applications
A Performance Evaluation of Open Source Erasure Codes for Storage Applications James S. Plank Catherine D. Schuman (Tennessee) Jianqiang Luo Lihao Xu (Wayne State) Zooko Wilcox-O'Hearn Usenix FAST February
EC-Bench: Benchmarking Onload and Offload Erasure Coders on Modern Hardware Architectures
EC-Bench: Benchmarking Onload and Offload Erasure Coders on Modern Hardware Architectures Haiyang Shi, Xiaoyi Lu, and Dhabaleswar K. (DK) Panda {shi.876, lu.932, panda.2}@osu.edu The Ohio State University
Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions. James S. Plank USENIX FAST. University of Tennessee
Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions James S. Plank University of Tennessee USENIX FAST San Jose, CA February 15, 2013. Authors Jim Plank Tennessee Kevin Greenan EMC/Data
Rethinking Erasure Codes for Cloud File Systems: Minimizing I/O for Recovery and Degraded Reads
Rethinking Erasure Codes for Cloud File Systems: Minimizing I/O for Recovery and Degraded Reads Osama Khan, Randal Burns, James S. Plank, William Pierce and Cheng Huang FAST 2012: 10 th USENIX Conference
Modern Erasure Codes for Distributed Storage Systems
Modern Erasure Codes for Distributed Storage Systems Storage Developer Conference, SNIA, Bangalore Srinivasan Narayanamurthy Advanced Technology Group, NetApp May 27 th 2016 1 Everything around us is changing!
A Comprehensive Study on RAID-6 Codes: Horizontal vs. Vertical
2011 Sixth IEEE International Conference on Networking, Architecture, and Storage A Comprehensive Study on RAID-6 Codes: Horizontal vs. Vertical Chao Jin, Dan Feng, Hong Jiang, Lei Tian School of Computer
A Performance Evaluation and Examination of Open-Source Erasure Coding Libraries For Storage
A Performance Evaluation and Examination of Open-Source Erasure Coding Libraries For Storage James S. Plank Jianqiang Luo Catherine D. Schuman Lihao Xu Zooko Wilcox-O Hearn Appearing in: FAST-9: 7th USENIX
Modern Erasure Codes for Distributed Storage Systems
Modern Erasure Codes for Distributed Storage Systems Srinivasan Narayanamurthy (Srini) NetApp Everything around us is changing! r The Data Deluge r Disk capacities and densities are increasing faster than
All About Erasure Codes: - Reed-Solomon Coding - LDPC Coding. James S. Plank. ICL - August 20, 2004
All About Erasure Codes: - Reed-Solomon Coding - LDPC Coding James S. Plank Logistical Computing and Internetworking Laboratory Department of Computer Science University of Tennessee ICL - August 2, 24
Comparison of RAID-6 Erasure Codes
Comparison of RAID-6 Erasure Codes Dimitri Pertin, Alexandre Van Kempen, Benoît Parrein, Nicolas Normand To cite this version: Dimitri Pertin, Alexandre Van Kempen, Benoît Parrein, Nicolas Normand. Comparison
HADOOP 3.0 is here! Dr. Sandeep Deshmukh Sadepach Labs Pvt. Ltd. - Let us grow together!
HADOOP 3.0 is here! Dr. Sandeep Deshmukh sandeep@sadepach.com Sadepach Labs Pvt. Ltd. - Let us grow together! About me BE from VNIT Nagpur, MTech+PhD from IIT Bombay Worked with Persistent Systems - Life
A Performance Evaluation and Examination of Open-Source Erasure Coding Libraries For Storage
A Performance Evaluation and Examination of Open-Source Erasure Coding Libraries For Storage James S. Plank Jianqiang Luo Catherine D. Schuman Lihao Xu Zooko Wilcox-O Hearn Abstract Over the past five
Storage systems have grown to the point where failures are inevitable,
A Brief Primer JAMES S. P LANK James S. Plank received his BS from Yale University in 1988 and his PhD from Princeton University in 1993. He is a professor in the Electrical Engineering and Computer Science
90A John Muir Drive Buffalo, New York Tel: Fax:
Reed Solomon Coding The VOCAL implementation of Reed Solomon (RS) Forward Error Correction (FEC) algorithms is available in several forms. The forms include pure software and software with varying levels
Performance improvements to peer-to-peer file transfers using network coding
Performance improvements to peer-to-peer file transfers using network coding Aaron Kelley April 29, 2009 Mentor: Dr. David Sturgill Outline Introduction Network Coding Background Contributions Precomputation
Optimize Storage Efficiency & Performance with Erasure Coding Hardware Offload. Dror Goldenberg VP Software Architecture Mellanox Technologies
Optimize Storage Efficiency & Performance with Erasure Coding Hardware Offload Dror Goldenberg VP Software Architecture Mellanox Technologies SNIA Legal Notice The material contained in this tutorial is
Erasure coding and AONT algorithm selection for Secure Distributed Storage. Alem Abreha Sowmya Shetty
Erasure coding and AONT algorithm selection for Secure Distributed Storage Alem Abreha Sowmya Shetty Secure Distributed Storage AONT(All-Or-Nothing Transform) unkeyed transformation φ mapping a sequence
Repair Pipelining for Erasure-Coded Storage
Repair Pipelining for Erasure-Coded Storage Runhui Li, Xiaolu Li, Patrick P. C. Lee, Qun Huang The Chinese University of Hong Kong USENIX ATC 2017 1 Introduction Fault tolerance for distributed storage
Efficient Load Balancing and Disk Failure Avoidance Approach Using Restful Web Services
Efficient Load Balancing and Disk Failure Avoidance Approach Using Restful Web Services Neha Shiraz, Dr. Parikshit N. Mahalle Persuing M.E, Department of Computer Engineering, Smt. Kashibai Navale College
Heuristics for Optimizing Matrix-Based Erasure Codes for Fault-Tolerant Storage Systems. James S. Plank Catherine D. Schuman B.
Heuristics for Optimizing Matrix-Based Erasure Codes for Fault-Tolerant Storage Systems James S. Plank Catherine D. Schuman B. Devin Robison ppearing in DSN 2012: The 42nd nnual IEEE/IFIP International
Close-form and Matrix En/Decoding Evaluation on Different Erasure Codes
UNIVERSITY OF MINNESOTA-TWIN CITIES Close-form and Matrix En/Decoding Evaluation on Different Erasure Codes by Zhe Zhang A thesis submitted in partial fulfillment for the degree of Master of Science in
On the Speedup of Single-Disk Failure Recovery in XOR-Coded Storage Systems: Theory and Practice
On the Speedup of Single-Disk Failure Recovery in XOR-Coded Storage Systems: Theory and Practice Yunfeng Zhu, Patrick P. C. Lee, Yuchong Hu, Liping Xiang, and Yinlong Xu University of Science and Technology
On Data Parallelism of Erasure Coding in Distributed Storage Systems
On Data Parallelism of Erasure Coding in Distributed Storage Systems Jun Li, Baochun Li Department of Electrical and Computer Engineering, University of Toronto, Canada {junli, bli}@ece.toronto.edu Abstract
RAID6L: A Log-Assisted RAID6 Storage Architecture with Improved Write Performance
RAID6L: A Log-Assisted RAID6 Storage Architecture with Improved Write Performance Chao Jin, Dan Feng, Hong Jiang, Lei Tian School of Computer, Huazhong University of Science and Technology Wuhan National
On the Speedup of Recovery in Large-Scale Erasure-Coded Storage Systems (Supplementary File)
1 On the Speedup of Recovery in Large-Scale Erasure-Coded Storage Systems (Supplementary File) Yunfeng Zhu, Patrick P. C. Lee, Yinlong Xu, Yuchong Hu, and Liping Xiang 1 ADDITIONAL RELATED WORK Our work
Building High Speed Erasure Coding Libraries for ARM and x86 Processors. Per Simonsen, CEO, MemoScale May 2017
Building High Speed Erasure Coding Libraries for ARM and x86 Processors Per Simonsen, CEO, MemoScale May 2017 Agenda MemoScale company and team Erasure coding - brief intro MemoScale erasure codes Performance
Pyramid Codes: Flexible Schemes to Trade Space for Access Efficiency in Reliable Data Storage Systems
Pyramid Codes: Flexible Schemes to Trade Space for Access Efficiency in Reliable Data Storage Systems Cheng Huang, Minghua Chen, and Jin Li Microsoft Research, Redmond, WA 98052 Abstract To flexibly explore
Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions
Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions Ethan L. Miller Center for Research in Storage Systems University of California, Santa Cruz (and Pure Storage) Authors Jim Plank Univ.
So you think you can FEC?
So you think you can FEC? Mikhail Misha Fludkov (fludkov) Pexip R&D Erlend Earl Graff (egraff) Pexip R&D 8 th GStreamer Conference 21 October 2017 Prague, Czech Republic Outline Introduction to FEC Designing
Short Code: An Efficient RAID-6 MDS Code for Optimizing Degraded Reads and Partial Stripe Writes
: An Efficient RAID-6 MDS Code for Optimizing Degraded Reads and Partial Stripe Writes Yingxun Fu, Jiwu Shu, Xianghong Luo, Zhirong Shen, and Qingda Hu Abstract As reliability requirements are increasingly
Seek-Efficient I/O Optimization in Single Failure Recovery for XOR-Coded Storage Systems
IEEE th Symposium on Reliable Distributed Systems Seek-Efficient I/O Optimization in Single Failure Recovery for XOR-Coded Storage Systems Zhirong Shen, Jiwu Shu, Yingxun Fu Department of Computer Science
PCM: A Parity-check Matrix Based Approach to Improve Decoding Performance of XOR-based Erasure Codes
15 IEEE th Symposium on Reliable Distributed Systems : A Parity-check Matrix Based Approach to Improve Decoding Performance of XOR-based Erasure Codes Yongzhe Zhang, Chentao Wu, Jie Li, Minyi Guo Shanghai
MODERN storage systems, such as GFS [1], Windows
![MODERN storage systems, such as GFS [1], Windows](/thumbs/92/107866960.jpg "MODERN storage systems, such as GFS [1], Windows")
IEEE TRANSACTIONS ON COMPUTERS, VOL. 66, NO. 1, JANUARY 2017 127 Short Code: An Efficient RAID-6 MDS Code for Optimizing Degraded Reads and Partial Stripe Writes Yingxun Fu, Jiwu Shu, Senior Member, IEEE,
FPGA Implementation of Erasure Codes in NVMe based JBOFs
FPGA Implementation of Erasure Codes in NVMe based JBOFs Manoj Roge Director, Data Center Xilinx Inc. Santa Clara, CA 1 Acknowledgement Shre Shah Data Center Architect, Xilinx Santa Clara, CA 2 Agenda
V 2 -Code: A New Non-MDS Array Code with Optimal Reconstruction Performance for RAID-6
V -Code: A New Non-MDS Array Code with Optimal Reconstruction Performance for RAID-6 Ping Xie 1, Jianzhong Huang 1, Qiang Cao 1, Xiao Qin, Changsheng Xie 1 1 School of Computer Science & Technology, Wuhan
Fast Software Implementations of Finite Field Operations (Extended Abstract)
Fast Software Implementations of Finite Field Operations (Extended Abstract) Cheng Huang Lihao Xu Department of Computer Science & Engineering Washington University in St. Louis, MO 63130 cheng, lihao@cse.wustl.edu
ECFS: A decentralized, distributed and faulttolerant FUSE filesystem for the LHCb online farm
Journal of Physics: Conference Series OPEN ACCESS ECFS: A decentralized, distributed and faulttolerant FUSE filesystem for the LHCb online farm To cite this article: Tomasz Rybczynski et al 2014 J. Phys.:
Efficiency Considerations of Cauchy Reed-Solomon Implementations on Accelerator and Multi-Core Platforms
Efficiency Considerations of Cauchy Reed-Solomon Implementations on Accelerator and Multi-Core Platforms SAAHPC June 15 2010 Knoxville, TN Kathrin Peter Sebastian Borchert Thomas Steinke Zuse Institute
A Complete Treatment of Software Implementations of Finite Field Arithmetic for Erasure Coding Applications
A Complete Treatment of Software Implementations of Finite Field Arithmetic for Erasure Coding Applications James S. Plank Kevin M. Greenan Ethan L. Miller University of Tennessee Technical Report UT-CS-13-717
A RANDOMLY EXPANDABLE METHOD FOR DATA LAYOUT OF RAID STORAGE SYSTEMS. Received October 2017; revised February 2018
International Journal of Innovative Computing, Information and Control ICIC International c 2018 ISSN 1349-4198 Volume 14, Number 3, June 2018 pp. 1079 1094 A RANDOMLY EXPANDABLE METHOD FOR DATA LAYOUT
Encoding-Aware Data Placement for Efficient Degraded Reads in XOR-Coded Storage Systems
Encoding-Aware Data Placement for Efficient Degraded Reads in XOR-Coded Storage Systems Zhirong Shen, Patrick P. C. Lee, Jiwu Shu, Wenzhong Guo College of Mathematics and Computer Science, Fuzhou University
DiskReduce: RAID for Data-Intensive Scalable Computing (CMU-PDL )
Research Showcase @ CMU Parallel Data Laboratory Research Centers and Institutes 11-2009 DiskReduce: RAID for Data-Intensive Scalable Computing (CMU-PDL-09-112) Bin Fan Wittawat Tantisiriroj Lin Xiao Garth
International Journal of Innovations in Engineering and Technology (IJIET)
RTL Design and Implementation of Erasure Code for RAID system Chethan.K 1, Dr.Srividya.P 2, Mr.Sivashanmugam Krishnan 3 1 PG Student, Department Of ECE, R. V. College Engineering, Bangalore, India. 2 Associate
HKBU Institutional Repository
Hong Kong Baptist University HKBU Institutional Repository Open Access Theses and Dissertations Electronic Theses and Dissertations 8-31-2018 ESetStore: an erasure-coding based distributed storage system
Distributed File System Based on Erasure Coding for I/O-Intensive Applications
Distributed File System Based on Erasure Coding for I/O-Intensive Applications Dimitri Pertin 1,, Sylvain David, Pierre Évenou, Benoît Parrein 1 and Nicolas Normand 1 1 LUNAM Université, Université de
Software-defined Storage: Fast, Safe and Efficient
Software-defined Storage: Fast, Safe and Efficient TRY NOW Thanks to Blockchain and Intel Intelligent Storage Acceleration Library Every piece of data is required to be stored somewhere. We all know about
Performance Models of Access Latency in Cloud Storage Systems
Performance Models of Access Latency in Cloud Storage Systems Qiqi Shuai Email: qqshuai@eee.hku.hk Victor O.K. Li, Fellow, IEEE Email: vli@eee.hku.hk Yixuan Zhu Email: yxzhu@eee.hku.hk Abstract Access
High Performance Computing Course Notes High Performance Storage
High Performance Computing Course Notes 2008-2009 2009 High Performance Storage Storage devices Primary storage: register (1 CPU cycle, a few ns) Cache (10-200 cycles, 0.02-0.5us) Main memory Local main
LaRS: A Load-aware Recovery Scheme for Heterogeneous Erasure-Coded Storage Clusters
214 9th IEEE International Conference on Networking, Architecture, and Storage LaRS: A Load-aware Recovery Scheme for Heterogeneous Erasure-Coded Storage Clusters Haibing Luo, Jianzhong Huang, Qiang Cao
P-Code: A New RAID-6 Code with Optimal Properties
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln CSE Conference and Workshop Papers Computer Science and Engineering, Department of 6-2009 P-Code: A New RAID-6 Code with
Distributed File System based on Erasure Coding for I/O Intensive Applications
Distributed File System based on Erasure Coding for I/O Intensive Applications Dimitri Pertin, Sylvain David, Pierre Evenou, Benoît Parrein, Nicolas Normand To cite this version: Dimitri Pertin, Sylvain
Today s Papers. Array Reliability. RAID Basics (Two optional papers) EECS 262a Advanced Topics in Computer Systems Lecture 3
EECS 262a Advanced Topics in Computer Systems Lecture 3 Filesystems (Con t) September 10 th, 2012 John Kubiatowicz and Anthony D. Joseph Electrical Engineering and Computer Sciences University of California,
Matrix Methods for Lost Data Reconstruction in Erasure Codes
Matrix Methods for Lost Data Reconstruction in Erasure Codes James Lee Hafner, Veera Deenadhayalan, and KK Rao John A Tomlin IBM Almaden Research Center Yahoo! Research hafner@almadenibmcom, [veerad,kkrao]@usibmcom
Notices and Disclaimers
Greg Tucker, Intel Notices and Disclaimers Intel technologies features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com,
Facilitating Magnetic Recording Technology Scaling for Data Center Hard Disk Drives through Filesystem-level Transparent Local Erasure Coding
Facilitating Magnetic Recording Technology Scaling for Data Center Hard Disk Drives through Filesystem-level Transparent Local Erasure Coding Yin Li, Hao Wang, Xuebin Zhang, Ning Zheng, Shafa Dahandeh,
Parallel Reed/Solomon Coding on Multicore Processors
/ 8 Parallel Reed/Solomon Coding on Multicore Processors Peter Sobe Institute of Computer Engineering University of Luebeck, Germany sobe@iti.uni-luebeck.de currently at Institute of Computer Science University
EE 6900: FAULT-TOLERANT COMPUTING SYSTEMS
EE 6900: FAULT-TOLERANT COMPUTING SYSTEMS LECTURE 6: CODING THEORY - 2 Fall 2014 Avinash Kodi kodi@ohio.edu Acknowledgement: Daniel Sorin, Behrooz Parhami, Srinivasan Ramasubramanian Agenda Hamming Codes
Efficient Software Implementations of Large Finite Fields GF(2 n ) for Secure Storage Applications
Efficient Software Implementations of Large Finite Fields GF(2 n ) for Secure Storage Applications JIANQIANG LUO Wayne State University KEVIN D. BOWERS, and ALINA OPREA RSA Laboratories LIHAO XU Wayne
Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions
Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions James S. Plank EECS Department University of Tennessee plank@cs.utk.edu Kevin M. Greenan EMC Backup Recovery Systems Division Kevin.Greenan@emc.com
WITH the rapid development of cloud storage, the size
1674 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 7, NO. 6, JUNE 016 HV Code: An All-Around MDS Code for RAID-6 Storage Systems Zhirong Shen, Jiwu Shu, Member, IEEE, and Yingxun Fu Abstract
BCStore: Bandwidth-Efficient In-memory KV-Store with Batch Coding. Shenglong Li, Quanlu Zhang, Zhi Yang and Yafei Dai Peking University
BCStore: Bandwidth-Efficient In-memory KV-Store with Batch Coding Shenglong Li, Quanlu Zhang, Zhi Yang and Yafei Dai Peking University Outline Introduction and Motivation Our Design System and Implementation
HPSS RAIT. A high performance, resilient, fault-tolerant tape data storage class. 1
HPSS RAIT A high performance, resilient, fault-tolerant tape data storage class http://www.hpss-collaboration.org 1 Why RAIT? HPSS supports striped tape without RAIT o Conceptually similar to RAID 0 o
Your Data is in the Cloud: Who Exactly is Looking After It?
Your Data is in the Cloud: Who Exactly is Looking After It? P Vijay Kumar Dept of Electrical Communication Engineering Indian Institute of Science IISc Open Day March 4, 2017 1/33 Your Data is in the Cloud:
Cloud Storage Reliability for Big Data Applications: A State of the Art Survey
Cloud Storage Reliability for Big Data Applications: A State of the Art Survey Rekha Nachiappan a, Bahman Javadi a, Rodrigo Calherios a, Kenan Matawie a a School of Computing, Engineering and Mathematics,
Secure Distributed Storage System
1 Secure Distributed Storage System Snehil Suresh Wakchaure, Simrit Kaur Arora Abstract Secure Distributed Storage techniques are gaining importance with the increase of data centers, mobile devices and
NETWORK CODED STORAGE I/O SUBSYSTEM FOR HPC EXASCALE APPLICATIONS
NETWORK CODED STORAGE I/O SUBSYSTEM FOR HPC EXASCALE APPLICATIONS 1 INTRODUCTION Intel predicts 10 times (4.4ZB to 44 ZB ) data explosion between 2013-2020. Massive data explosion makes legacy storage
Capacity Assurance in Hostile Networks
PhD Dissertation Defense Wednesday, October 7, 2015 3:30 pm - 5:30 pm 3112 Engineering Building Capacity Assurance in Hostile Networks By: Jian Li Advisor: Jian Ren ABSTRACT Linear network coding provides
The comeback of Reed Solomon codes
The comeback of Reed Solomon codes Nir Drucker University of Haifa, Israel, and Amazon Web Services Inc. 1 Shay Gueron University of Haifa, Israel, and Amazon Web Services Inc. 1 Vlad Krasnov CloudFlare,
Intelligent RAID 6 Theory Overview and Implementation
White Paper RAID 6 Intel Storage Building Blocks Intelligent RAID 6 Theory Overview and Implementation www.intel.com/design/ storage/intelligent_raid.htm 2 Abstract RAID 5 systems are commonly deployed
Construction of Efficient OR-based Deletion tolerant Coding Schemes
Construction of Efficient OR-based Deletion tolerant Coding Schemes Peter Sobe and Kathrin Peter University of Luebeck Institute of Computer Engineering {sobe peterka}@itiuni-luebeckde Abstract Fault tolerant
RAID (Redundant Array of Inexpensive Disks)
Magnetic Disk Characteristics I/O Connection Structure Types of Buses Cache & I/O I/O Performance Metrics I/O System Modeling Using Queuing Theory Designing an I/O System RAID (Redundant Array of Inexpensive
Improving Cloud Storage Cost and Data Resiliency with Erasure Codes Michael Penick
Improving Cloud Storage Cost and Data Resiliency with Erasure Codes Michael Penick Commodity Storage Hosting storage FTP backup Goals Inexpensive (use commodity hardware) Resilient to failures Highly available
Locally repairable codes
Locally repairable codes for large-scale storage Presented by Anwitaman Datta Nanyang Technological University, Singapore Joint work with Frédérique Oggier & Lluís Pàmies i Juárez @ IISc 2012 & NetApp
ISA-L Performance Report Release Test Date: Sept 29 th 2017
Test Date: Sept 29 th 2017 Revision History Date Revision Comment Sept 29 th, 2017 1.0 Initial document for release 2 Contents Audience and Purpose... 4 Test setup:... 4 Intel Xeon Platinum 8180 Processor
SE310 Analysis and Design of Software Systems
SE310 Analysis and Design of Software Systems Lecture 3 Systems Requirements January 21, 2015 Sam Siewert Learning Objective Software Engineering Process? Lifecycle Phases feedback SPIRAL WATERFALL XP
Exploration of Erasure-Coded Storage Systems for High Performance, Reliability, and Inter-operability
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2016 Exploration of Erasure-Coded Storage Systems for High Performance, Reliability, and Inter-operability
Parity Logging with Reserved Space: Towards Efficient Updates and Recovery in Erasure-coded Clustered Storage
Parity Logging with Reserved Space: Towards Efficient Updates and Recovery in Erasure-coded Clustered Storage Jeremy C. W. Chan, Qian Ding, Patrick P. C. Lee, and Helen H. W. Chan, The Chinese University
File systems CS 241. May 2, University of Illinois
File systems CS 241 May 2, 2014 University of Illinois 1 Announcements Finals approaching, know your times and conflicts Ours: Friday May 16, 8-11 am Inform us by Wed May 7 if you have to take a conflict
vsan All Flash Features First Published On: Last Updated On:
First Published On: 11-07-2016 Last Updated On: 11-07-2016 1 1. vsan All Flash Features 1.1.Deduplication and Compression 1.2.RAID-5/RAID-6 Erasure Coding Table of Contents 2 1. vsan All Flash Features
WHITE PAPER SINGLE & MULTI CORE PERFORMANCE OF AN ERASURE CODING WORKLOAD ON AMD EPYC
WHITE PAPER SINGLE & MULTI CORE PERFORMANCE OF AN ERASURE CODING WORKLOAD ON AMD EPYC INTRODUCTION With the EPYC processor line, AMD is expected to take a strong position in the server market including
Assessing the Performance of Erasure Codes in the Wide-Area
Assessing the Performance of Erasure Codes in the Wide-Area Rebecca L. Collins James S. Plank Department of Computer Science University of Tennessee Knoxville, TN 37996 rcollins@cs.utk.edu, plank@cs.utk.edu
Code 5-6: An Efficient MDS Array Coding Scheme to Accelerate Online RAID Level Migration
2015 44th International Conference on Parallel Processing Code 5-6: An Efficient MDS Array Coding Scheme to Accelerate Online RAID Level Migration Chentao Wu 1, Xubin He 2, Jie Li 1 and Minyi Guo 1 1 Shanghai
Giza: Erasure Coding Objects across Global Data Centers
Giza: Erasure Coding Objects across Global Data Centers Yu Lin Chen*, Shuai Mu, Jinyang Li, Cheng Huang *, Jin li *, Aaron Ogus *, and Douglas Phillips* New York University, *Microsoft Corporation USENIX
Store, Forget & Check: Using Algebraic Signatures to Check Remotely Administered Storage
Store, Forget & Check: Using Algebraic Signatures to Check Remotely Administered Storage Ethan L. Miller & Thomas J. E. Schwarz Storage Systems Research Center University of California, Santa Cruz What
Candidate MDS Array Codes for Tolerating Three Disk Failures in RAID-7 Architectures
Candidate MDS Array Codes for Tolerating Three Disk Failures in RAID-7 Architectures ABSTRACT Mayur Punekar Dept. of Computer Science and Eng. Qatar University Doha, Qatar mayur.punekar@ieee.org Yongge
Reliable Computing I
Instructor: Mehdi Tahoori Reliable Computing I Lecture 8: Redundant Disk Arrays INSTITUTE OF COMPUTER ENGINEERING (ITEC) CHAIR FOR DEPENDABLE NANO COMPUTING (CDNC) National Research Center of the Helmholtz
4 Exploiting Redundancies and Deferred Writes to Conserve Energy in Erasure-Coded Storage Clusters
4 Exploiting Redundancies and Deferred Writes to Conserve Energy in Erasure-Coded Storage Clusters JIANZHONG HUANG and FENGHAO ZHANG, Huazhong University of Science and Technology XIAO QIN, Auburn University
Optimizing Galois Field Arithmetic for Diverse Processor Architectures and Applications
Optimizing Galois Field Arithmetic for Diverse Processor Architectures and Applications Kevin M. Greenan Univ. of California, Santa Cruz kmgreen@cs.ucsc.edu Ethan L. Miller Univ. of California, Santa Cruz
Data Integrity Protection scheme for Minimum Storage Regenerating Codes in Multiple Cloud Environment
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Data Integrity Protection scheme for Minimum Storage Regenerating Codes in Multiple Cloud Environment Saravana
On Coding Techniques for Networked Distributed Storage Systems
On Coding Techniques for Networked Distributed Storage Systems Frédérique Oggier frederique@ntu.edu.sg Nanyang Technological University, Singapore First European Training School on Network Coding, Barcelona,
In-Memory Checkpointing for MPI Programs by XOR-Based Double-Erasure Codes *
In-Memory Checkpointing for MPI Programs by XOR-Based Double-Erasure Codes * Gang Wang, Xiaoguang Liu, Ang Li, and Fan Zhang Nankai-Baidu Joint Lab, College of Information Technical Science, Nankai University,
Everything You Wanted To Know About Storage (But Were Too Proud To Ask) The Basics
Everything You Wanted To Know About Storage (But Were Too Proud To Ask) The Basics Today s Presenters Bob Plumridge HDS Chief Technology Officer - EMEA Alex McDonald NetApp CTO Office 2 SNIA Legal Notice
Today's forecast: cloudy with some rain Towards secure & reliable Cloud Computing
Department of Computer Science Institute of Systems Architecture Chair of Computer Networks Today's forecast: cloudy with some rain Towards secure & reliable Cloud Computing Dr.-Ing. Stephan Groß
EC-Cache: Load-balanced, Low-latency Cluster Caching with Online Erasure Coding
EC-Cache: Load-balanced, Low-latency Cluster Caching with Online Erasure Coding Rashmi Vinayak UC Berkeley Joint work with Mosharaf Chowdhury, Jack Kosaian (U Michigan) Ion Stoica, Kannan Ramchandran (UC
Finding the most fault-tolerant flat XOR-based erasure codes for storage systems Jay J. Wylie
Finding the most fault-tolerant flat XOR-based erasure codes for storage systems Jay J. Wylie HP Laboratories HPL-2011-218 Abstract: We describe the techniques we developed to efficiently find the most
A Transpositional Redundant Data Update Algorithm for Growing Server-less Video Streaming Systems
A Transpositional Redundant Data Update Algorithm for Growing Server-less Video Streaming Systems T. K. Ho and Jack Y. B. Lee Department of Information Engineering The Chinese University of Hong Kong Shatin,
A Fusion-based Approach for Handling Multiple Faults in Distributed Systems
A Fusion-based Approach for Handling Multiple Faults in Distributed Systems Bharath Balasubramanian, Vijay K. Garg Parallel and Distributed Systems Laboratory, Dept. of Electrical and Computer Engineering,
TRIP: Temporal Redundancy Integrated Performance Booster for Parity-Based RAID Storage Systems
200 6th International Conference on Parallel and Distributed Systems TRIP: Temporal Redundancy Integrated Performance Booster for Parity-Based RAID Storage Systems Chao Jin *, Dan Feng *, Hong Jiang, Lei
ECE Enterprise Storage Architecture. Fall 2018
ECE590-03 Enterprise Storage Architecture Fall 2018 RAID Tyler Bletsch Duke University Slides include material from Vince Freeh (NCSU) A case for redundant arrays of inexpensive disks Circa late 80s..
Erasure Coding in Object Stores: Challenges and Opportunities
Erasure Coding in Object Stores: Challenges and Opportunities Lewis Tseng Boston College July 2018, PODC Acknowledgements Nancy Lynch Muriel Medard Kishori Konwar Prakash Narayana Moorthy Viveck R. Cadambe