The Unwritten Contract of Solid State Drives
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1 The Unwritten Contract of Solid State Drives Jun He, Sudarsun Kannan, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau Department of Computer Sciences, University of Wisconsin - Madison
2 Enterprise SSD revenue is expected to exceed enterprise HDD in 2017 Enterprise HDD Enterprise SSD 15 Revenue (Billion Dollars) 10 5 HDD SSD E 2016E 2017E 2018E 2019E Year Source: Gartner, Stifel Estimates
3 Storage stack is shifting from the HDD era to the SSD era App FS
4 Storage stack is shifting from the HDD era to the SSD era App FS
5 Storage stack is shifting from the HDD era to the SSD era App App FS FS
6 Storage stack is shifting from the HDD era to the SSD era App App? FS FS?
7 Storage stack is shifting from the HDD era to the SSD era App App? FS FS??
8 Storage stack is shifting from the HDD era to the SSD era App App? FS FS??
9 Storage stack is shifting from the HDD era to the SSD era for SSD App App? App for SSD FS FS? FS?
10 Storage stack is shifting from the HDD era to the SSD era for SSD App App? App? FS FS? FS? for SSD?
11 Storage stack is shifting from the HDD era to the SSD era for SSD App App? App? FS FS? FS? for SSD??
12 S. Boboila and. Desnoyers. Write Endurance in Flash Drives: Measurements and Analysis. In roceedings of the 8th USENIX Symposium on File and Storage Technologies (FAST 10), San Jose, California, February 2010 The consequences of misusing SSDs
13 S. Boboila and. Desnoyers. Write Endurance in Flash Drives: Measurements and Analysis. In roceedings of the 8th USENIX Symposium on File and Storage Technologies (FAST 10), San Jose, California, February 2010 The consequences of misusing SSDs erformance degradation
14 S. Boboila and. Desnoyers. Write Endurance in Flash Drives: Measurements and Analysis. In roceedings of the 8th USENIX Symposium on File and Storage Technologies (FAST 10), San Jose, California, February 2010 The consequences of misusing SSDs erformance degradation erformance fluctuation
15 S. Boboila and. Desnoyers. Write Endurance in Flash Drives: Measurements and Analysis. In roceedings of the 8th USENIX Symposium on File and Storage Technologies (FAST 10), San Jose, California, February 2010 The consequences of misusing SSDs erformance degradation erformance fluctuation Early end of device life
16 What is the right way to achieve high performance on SSDs?
17 What is the right way to achieve high performance on SSDs? Block Device Interface: read(range), write(range), discard(range)
18 What is the right way to achieve high performance on SSDs? Block Device Interface: read(range), write(range), discard(range) Unwritten Contract of HDDs Sequential accesses are best Nearby accesses are more efficient than farther ones MEMS-based storage devices and standard disk interfaces: A square peg in a round hole? Steven W. Schlosser, Gregory R. Ganger FAST 04
19 What is the right way to achieve high performance on SSDs? Block Device Interface: read(range), write(range), discard(range) Unwritten Contract of HDDs Sequential accesses are best Nearby accesses are more efficient than farther ones MEMS-based storage devices and standard disk interfaces: A square peg in a round hole? Steven W. Schlosser, Gregory R. Ganger FAST 04 Unwritten Contract of SSDs?
20 What is the right way to achieve high performance on SSDs?
21 What is the right way to achieve high performance on SSDs? Existing studies
22 What is the right way to achieve high performance on SSDs? Existing studies Experience of implementing a detailed SSD simulator
23 What is the right way to achieve high performance on SSDs? Existing studies Experience of implementing a detailed SSD simulator Analysis of experiments
24 What is the right way to achieve high performance on SSDs? Existing studies Experience of implementing a detailed SSD simulator Analysis of experiments The Unwritten Contract of SSDs
25 Do the current apps/fses comply with the unwritten contract of SSDs?
26 Do the current apps/fses comply with the unwritten contract of SSDs? 5 apps LevelDB for SSD RocksDB SQLite (RollBack) SQLite (WAL) Varmail
27 Do the current apps/fses comply with the unwritten contract of SSDs? 5 apps LevelDB for SSD RocksDB SQLite (RollBack) SQLite (WAL) Varmail 3 file systems for SSD ext4 F2FS XFS
28 Do the current apps/fses comply with the unwritten contract of SSDs? 5 apps LevelDB for SSD RocksDB SQLite (RollBack) SQLite (WAL) Varmail 3 file systems for SSD ext4 F2FS XFS Contract Rule 1 Rule 2 Rule 3 Rule 4 Rule 5
29 Do the current apps/fses comply with the unwritten contract of SSDs? 5 apps LevelDB for SSD RocksDB SQLite (RollBack) SQLite (WAL) Varmail 3 file systems for SSD ext4 F2FS XFS Contract Rule 1 Rule 2 Rule 3 Rule 4 Rule 5
30 Do the current apps/fses comply with the unwritten contract of SSDs? 5 apps LevelDB for SSD RocksDB SQLite (RollBack) SQLite (WAL) Varmail 3 file systems To study the contract, we built a sophisticated SSD simulator and workload analyzer for SSD ext4 F2FS XFS Contract Rule 1 Rule 2 Rule 3 Rule 4 Rule 5
31 In the paper
32 In the paper We made 24 detailed observations
33 In the paper We made 24 detailed observations We learned several high-level lessons
34 Outline Overview SSD Unwritten Contract Violations of the Unwritten Contract Conclusions
35 Outline Overview SSD Unwritten Contract Violations of the Unwritten Contract Conclusions
36 SSD Background
37 SSD Background
38 SSD Background Block
39 SSD Background Block
40 SSD Background Channel Block
41 SSD Background Channel Channel Block Block
42 SSD Background Channel Channel Channel Block Block Block
43 SSD Background Channel Channel Channel Block Block Block
44 SSD Background Channel Channel Channel Block Block Block
45 SSD Background Controller Channel Channel Channel Block Block Block
46 SSD Background Block Channel Block Channel Block Channel Controller FTL
47 SSD Background Controller FTL - address mapping - garbage collection - wear-leveling Channel Channel Channel Block Block Block
48 SSD Background Controller RAM FTL - address mapping - garbage collection - wear-leveling Channel Block Channel Block Channel Block
49 SSD Background Controller FTL - address mapping - garbage collection - wear-leveling RAM Mapping Table Data Cache Channel Channel Channel Block Block Block
50 Rules of the Unwritten Contract #1 Request Scale #2 Locality #3 Aligned Sequentiality #4 Grouping by Death Time #5 Uniform Data Lifetime
51 Rule #1: Request Scale SSD clients should issue large data requests or multiple outstanding data requests.
52 Rule #1: Request Scale SSD clients should issue large data requests or multiple outstanding data requests. SSD Channel Channel Channel Channel
53 Rule #1: Request Scale SSD clients should issue large data requests or multiple outstanding data requests. Request SSD Channel Channel Channel Channel
54 Rule #1: Request Scale SSD clients should issue large data requests or multiple outstanding data requests. SSD Channel Channel Channel Channel
55 Rule #1: Request Scale SSD clients should issue large data requests or multiple outstanding data requests. SSD High internal parallelism Channel Channel Channel Channel
56 Rule #1: Request Scale Violation
57 Rule #1: Request Scale Violation SSD Channel Channel Channel Channel
58 Rule #1: Request Scale Violation SSD Channel Channel Channel Channel
59 Rule #1: Request Scale Violation SSD Channel Channel Channel Channel Wasted
60 Rule #1: Request Scale Violation If you violate the rule: SSD - Low internal parallelism Channel Channel Channel Channel Wasted
61 Rule #1: Request Scale Violation If you violate the rule: SSD - Low internal parallelism Channel Channel Channel Channel erformance impact: 18x read bandwidth 10x write bandwidth Wasted F. Chen, R. Lee, and X. Zhang. Essential Roles of Exploit- ing Internal arallelism of Flash Memory Based Solid State Drives in High-speed Data rocessing. In roceedings of the 17th International Symposium on High erformance Computer Architecture (HCA-11), pages , San Antonio, Texas, February 2011.
62 Rule 2: Locality SSD clients should access with locality SSD RAM Translation Cache FLASH Logical to hysical Mapping Table
63 Rule 2: Locality SSD clients should access with locality SSD RAM Translation Cache FLASH Logical to hysical Mapping Table
64 Rule 2: Locality SSD clients should access with locality SSD RAM Translation Cache FLASH Logical to hysical Mapping Table
65 Rule 2: Locality SSD clients should access with locality SSD RAM Hit Translation Cache FLASH Logical to hysical Mapping Table
66 Rule 2: Locality SSD clients should access with locality SSD RAM Hit Translation Cache FLASH Logical to hysical Mapping Table High Cache Hit Ratio
67 Rule 2: Locality Violation SSD SSD clients should access with locality RAM Logical to hysical Mapping Table FLASH
68 Rule 2: Locality Violation SSD SSD clients should access with locality RAM Logical to hysical Mapping Table FLASH
69 Rule 2: Locality Violation SSD SSD clients should access with locality If you violate the rule: RAM - Translation cache misses - More translation entry reads/writes Logical to hysical Mapping Table FLASH
70 Rule 2: Locality Violation SSD SSD clients should access with locality If you violate the rule: RAM - Translation cache misses - More translation entry reads/writes FLASH Logical to hysical Mapping Table erformance impact: 2.2x average response time Y. Zhou, F. Wu,. Huang, X. He, C. Xie, and J. Zhou. An Efficient age-level FTL to Optimize Address Translation in Flash Memory. In roceedings of the EuroSys Conference (EuroSys 15), Bordeaux, France, April 2015.
71 Rule 3: Aligned Sequentiality Details in the paper
72 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block.
73 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block. 1pm 2pm 1pm 2pm 1pm 2pm 1pm 2pm
74 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block. 1pm 2pm 1pm 2pm 1pm 2pm Time 1pm 2pm 1pm 2pm
75 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block. 1pm 2pm 1pm 2pm 1pm 2pm Time 1pm 2pm 1pm 2pm
76 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block. 1pm 2pm 1pm 2pm 1pm 2pm Time 1pm 2pm 1pm 2pm
77 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block. 1pm 2pm 1pm 2pm 1pm 2pm Time 1pm 2pm 1pm 2pm
78 Rule 4: Grouping By Death Time Data with similar death times should be placed in the same block. 1pm 2pm 1pm 2pm 1pm 2pm Time 1pm 2pm 1pm 2pm
79 Rule 4: Grouping By Death Time Violation 1pm 1pm 1pm 1pm 2pm 2pm Time 1pm 2pm 2pm 2pm
80 Rule 4: Grouping By Death Time Violation 1pm 1pm 1pm 1pm 2pm 2pm Time 1pm 2pm 2pm 2pm
81 Rule 4: Grouping By Death Time Violation 1pm 1pm 1pm 1pm 2pm 2pm Time 1pm 2pm 2pm 2pm
82 Rule 4: Grouping By Death Time Violation 1pm 1pm 1pm 1pm 2pm 2pm Time 1pm 2pm 2pm 2pm
83 Rule 4: Grouping By Death Time Violation 1pm 1pm 2pm 1pm 1pm 2pm 2pm 2pm Time 1pm 2pm 2pm 2pm
84 Rule 4: Grouping By Death Time Violation 1pm 2pm 1pm 2pm 2pm Time 1pm 2pm 2pm
85 Rule 4: Grouping By Death Time Violation Data movement!!! 1pm 2pm 1pm 2pm 2pm Time 1pm 2pm 2pm
86 Rule 4: Grouping By Death Time Violation Data movement!!! 1pm 2pm 1pm 2pm 2pm Time 1pm 2pm 2pm
87 Rule 4: Grouping By Death Time Violation If you violate the rule: Data movement!!! - erformance penalty - Write amplification 1pm 2pm 1pm 2pm 2pm Time 1pm 2pm 2pm
88 Rule 4: Grouping By Death Time Violation If you violate the rule: Data movement!!! - erformance penalty - Write amplification 1pm 2pm 1pm 2pm Time 1pm 2pm erformance impact: 2pm 4.8x write bandwidth 1.6x throughput 2pm 1.8x block erasure count C. Lee, D. Sim, J.-Y. Hwang, and S. Cho. F2FS: A New File System for Flash Storage. In roceedings of the 13th USENIX Conference on File and Storage Technologies (FAST 15), Santa Clara, California, February J.-U. Kang, J. Hyun, H. Maeng, and S. Cho. The Multi- streamed Solid-State Drive. In 6th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 14), hiladelphia, A, June Y. Cheng, F. Douglis,. Shilane, G. Wallace,. Desnoyers, and K. Li. Erasing Belady s Limitations: In Search of Flash Cache Offline Optimality. In 2016 USENIX Annual Technical Conference (USENIX ATC 16), pages , Denver, CO, USENIX Association.
89 Rule 5: Uniform Data Lifetime Clients of SSDs should create data with similar lifetimes
90 Rule 5: Uniform Data Lifetime Clients of SSDs should create data with similar lifetimes Lifetime 1 Day
91 Rule 5: Uniform Data Lifetime Clients of SSDs should create data with similar lifetimes Lifetime 1 Day SSD Usage Count:
92 Rule 5: Uniform Data Lifetime Clients of SSDs should create data with similar lifetimes Lifetime 1 Day SSD Usage Count:
93 Rule 5: Uniform Data Lifetime Clients of SSDs should create data with similar lifetimes Lifetime 1 Day No wear-leveling needed SSD Usage Count:
94 Rule 5: Uniform Data Lifetime Violation Lifetime 1 Day 1000 Years SSD Usage Count:
95 Rule 5: Uniform Data Lifetime Violation Lifetime 1 Day 1000 Years SSD Usage Count: 365* *10001
96 Rule 5: Uniform Data Lifetime Violation Lifetime 1 Day 1000 Years Some blocks wear out sooner Frequent wear-leveling needed!!! SSD Usage Count: 365* *10001
97 Rule 5: Uniform Data Lifetime Violation If you violate the rule: Lifetime 1 Day - erformance penalty - Write amplification 1000 Years Some blocks wear out sooner Frequent wear-leveling needed!!! SSD Usage Count: 365* *10001
98 Rule 5: Uniform Data Lifetime Violation If you violate the rule: Lifetime 1 Day - erformance penalty - Write amplification 1000 Years Some blocks wear out sooner erformance impact: Frequent wear-leveling needed!!! 1.6x write latency SSD S. Boboila and. Desnoyers. Write Endurance in Flash Drives: Measurements and Analysis. In roceedings of the 8th USENIX Symposium on File and Storage Technologies (FAST 10), San Jose, California, February Usage Count: 365* *10001
99 Outline Overview SSD Unwritten Contract Violations of the Unwritten Contract Conclusions
100 Do applications/file systems comply with the unwritten contract?
101 We conduct vertical analysis to find violations of SSD contract
102 We conduct vertical analysis to find violations of SSD contract App + FS SSD
103 We conduct vertical analysis to find violations of SSD contract App + FS Block Trace SSD
104 We conduct vertical analysis to find violations of SSD contract App + FS Block Trace SSD SSD Simulator: WiscSim
105 We conduct vertical analysis to find violations of SSD contract App + FS Block Trace Analyzer: SSD WiscSee Rule violation? SSD Simulator: WiscSim
106 We conduct vertical analysis to find violations of SSD contract App + Root Cause FS Block Trace Analyzer: SSD WiscSee Rule violation? SSD Simulator: WiscSim
107 2 of Our 24 Observations 1. Linux page cache limits request scale 2. F2FS incurs more GC overhead than traditional file systems
108 2 of Our 24 Observations 1. Linux page cache limits request scale 2. F2FS incurs more GC overhead than traditional file systems
109 We evaluate request scale by request size and number of concurrent requests ext4 f2fs xfs ext4 f2fs xfs leveldb rocksdb sqlite rb sqlite wal varmail leveldb rocksdb sqlite rb sqlite wal varmail read write Num of Concurrent Requests read write 0 0 seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix small large mix seq rand mix seq rand mix small large mix Request Size (KB)
110 We evaluate request scale by request size and number of concurrent requests ext4 f2fs xfs ext4 f2fs xfs leveldb rocksdb sqlite rb sqlite wal varmail leveldb rocksdb sqlite rb sqlite wal varmail read write Num of Concurrent Requests read write 0 0 seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix small large mix seq rand mix seq rand mix small large mix Request Size (KB)
111 We evaluate request scale by request size and number of concurrent requests ext4 f2fs xfs ext4 f2fs xfs leveldb rocksdb sqlite rb sqlite wal varmail leveldb rocksdb sqlite rb sqlite wal varmail 1200 LevelDB & RocksDB: read write read write seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix seq rand Num of Concurrent Requests mix small large mix seq rand mix seq rand mix Request Size (KB) small large mix Insertions Background Compactions
112 We evaluate request scale by request size and number of concurrent requests ext4 f2fs xfs ext4 f2fs xfs leveldb rocksdb sqlite rb sqlite wal varmail leveldb rocksdb sqlite rb sqlite wal varmail 1200 LevelDB & RocksDB: 30 Request Size (KB) read write read write seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix small large mix Num of Concurrent Requests seq rand mix seq rand mix small large mix Insertions Background Compactions Median: ~100KB
113 We evaluate request scale by request size and number of concurrent requests ext4 f2fs xfs ext4 f2fs xfs leveldb rocksdb sqlite rb sqlite wal varmail leveldb rocksdb sqlite rb sqlite wal varmail 1200 LevelDB & RocksDB: 30 Request Size (KB) read write read write seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix seq rand mix small large mix Num of Concurrent Requests seq rand mix seq rand mix small large mix Insertions Background Compactions Median: ~100KB Median: ~2
114 LevelDB and RocksDB can access files in large sizes. Why was the request scale low?
115 Buffered read(): age cache implementation splits and serializes user requests App age Cache Block Layer SSD
116 Buffered read(): age cache implementation splits and serializes user requests App read() 2MB age Cache Block Layer SSD
117 Buffered read(): age cache implementation splits and serializes user requests App read() 2MB age Cache 128KB 128KB 128KB Block Layer SSD
118 Buffered read(): age cache implementation splits and serializes user requests App read() 2MB age Cache 128KB 128KB 128KB One request at a time Block Layer SSD
119 Buffered read(): age cache implementation splits and serializes user requests App read() 2MB age Cache 128KB 128KB 128KB One request at a time Surprise! Even reading 2MB in your Block Layer app will not utilize SSD well. SSD
120 Cause of Violation Large reads are throttled by small prefetching (readahead).
121 2 of Our 24 Observations 1. Linux page cache limits request scale 2. F2FS incurs more GC overhead than traditional file systems
122 2 of Our 24 Observations 1. Linux page cache limits request scale 2. F2FS incurs more GC overhead than traditional file systems
123 We study GC (rule #3: grouping by death time) by zombie curves
124 We study GC (rule #3: grouping by death time) by zombie curves What s a zombie curve??
125 We study GC (rule #3: grouping by death time) by zombie curves What s a zombie curve?
126 We study GC (rule #3: grouping by death time) by zombie curves What s a zombie curve? Run workloads with infinite space over-provisioning
127 We study GC (rule #3: grouping by death time) by zombie curves What s a zombie curve? Run workloads with infinite space over-provisioning
128 We study GC (rule #3: grouping by death time) by zombie curves What s a zombie curve? Run workloads with infinite space over-provisioning Valid
129 We study GC (rule #3: grouping by death time) by zombie curves What s a zombie curve? Run workloads with infinite space over-provisioning Invalid Valid
130 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
131 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
132 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
133 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
134 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
135 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
136 We study GC (rule #3: grouping by death time) by zombie curves Valid ratio
137 What s a good zombie curve? Over-provisioned Over-provisioned
138 What s a good zombie curve? Ready to be used Ready to be used Over-provisioned Over-provisioned
139 What s a bad zombie curve? Over-provisioned
140 What s a bad zombie curve? Move data before use Over-provisioned
141 BTW, zombie curve helps you choose over-provisioning ratio Over-provisioned
142 BTW, zombie curve helps you choose over-provisioning ratio Over-provisioned
143 F2FS incurs a worse zombie curve (higher GC overhead) than ext4 for SQLite 1.0 Over-rovisioned Valid Ratio Animations cannot be displayed in DF. lease see the animations at Flash Space
144 F2FS incurs a worse zombie curve (higher GC overhead) than ext4 for SQLite 1.0 Over-rovisioned Valid Ratio Animations cannot be displayed in DF. lease see the animations at Flash Space
145 F2FS incurs a worse zombie curve (higher GC overhead) than ext4 for SQLite 1.0 Over-rovisioned Valid Ratio ext Animations cannot be displayed in DF. lease see the animations at Flash Space
146 F2FS incurs a worse zombie curve (higher GC overhead) than ext4 for SQLite 1.0 F2FS Over-rovisioned Valid Ratio ext Animations cannot be displayed in DF. lease see the animations at Flash Space
147 F2FS incurs a worse zombie curve (higher GC overhead) than ext4 for SQLite 1.0 F2FS Over-rovisioned Valid Ratio ext Animations cannot be displayed in DF. lease see the animations at Stable-state curves characterize workloads Flash Space
148 F2FS incurs a worse zombie curve (higher GC overhead) than ext4 for SQLite 1.0 F2FS Over-rovisioned Valid Ratio ext Animations cannot be displayed in DF. lease see the animations at Stable-state curves characterize workloads Flash Space
149 Why did F2FS incur a worse zombie curve (GC overhead)?
150 Why did F2FS incur a worse zombie curve (GC overhead)? SQLite fragmented F2FS
151 Why did F2FS incur a worse zombie curve (GC overhead)? SQLite fragmented F2FS F2FS did not discard data that was deleted by SQLite
152 Why did F2FS incur a worse zombie curve (GC overhead)? SQLite fragmented F2FS F2FS did not discard data that was deleted by SQLite F2FS was not able to stay log-structured for SQLite s I/O pattern
153 More Observations
154 More Observations Legacy file system allocation policies break locality
155 More Observations Legacy file system allocation policies break locality Application log structuring does not reduce GC
156 More Observations Legacy file system allocation policies break locality Application log structuring does not reduce GC 24 observations in the paper
157 Lessons Learned
158 Lessons Learned The SSD contract is multi-dimensional Optimizing for one dimension is not enough We need more sophisticated tools to analyze workloads
159 Lessons Learned The SSD contract is multi-dimensional Optimizing for one dimension is not enough We need more sophisticated tools to analyze workloads Although not perfect, traditional file systems perform surprisingly well upon SSDs
160 Lessons Learned The SSD contract is multi-dimensional Optimizing for one dimension is not enough We need more sophisticated tools to analyze workloads Although not perfect, traditional file systems perform surprisingly well upon SSDs Myths spread if the unwritten contract is not clarified Random writes increase GC overhead
161 Conclusions
162 Conclusions Understanding the unwritten contract is crucial for designing high performance application and file systems
163 Conclusions Understanding the unwritten contract is crucial for designing high performance application and file systems System designing demands more vertical analysis
164 Conclusions Understanding the unwritten contract is crucial for designing high performance application and file systems System designing demands more vertical analysis WiscSee (analyzer) and WiscSim (SSD simulator) are available at:
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