HashCache: Cache Storage for the Next Billion
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1 HashCache: Cache Storage for the Next Billion Anirudh Badam KyoungSoo Park Vivek S. Pai Larry L. Peterson Princeton University 1
2 Next Billion Internet Users 2
3 Next Billion Internet Users Schools, urban middle class in developing regions 2
4 Next Billion Internet Users Schools, urban middle class in developing regions $200 Affordable hardware OLPC and Intel Classmate 2
5 Next Billion Internet Users Schools, urban middle class in developing regions $200 Affordable hardware OLPC and Intel Classmate Expensive Internet $1500+ per month per Mbps $1500 per month Unlikely to improve in the near future 2
6 Bandwidth Saving 3
7 Bandwidth Saving Connectivity is a precious resource 3
8 Bandwidth Saving Cache Connectivity is a precious resource Internet Saving bandwidth important Disk caches reduce network bandwidth requirement 3
9 Bandwidth Saving Cache Connectivity is a precious resource Internet Saving bandwidth important Disk caches reduce network bandwidth requirement Good news - disk is very cheap $100/TB 3
10 Why Large Caches? 4
11 Why Large Caches? Larger bandwidth savings Refreshes cheaper than re-fetches Overnight prefetch, content push from peers 4
12 Why Large Caches? Larger bandwidth savings Good offline behavior Preload websites Enables local search Refreshes cheaper than re-fetches Overnight prefetch, content push from peers 4
13 Why Large Caches? Larger bandwidth savings Good offline behavior Preload websites Enables local search Refreshes cheaper than re-fetches Overnight prefetch, content push from peers Save even on dynamic content WAN Acceleration = packet caching 4
14 What is the Cost? 5
15 What is the Cost? 70 seeks per sec In-memory data structures Hash table avoids seeks for misses Cache replacement (LRU, etc) 5
16 What is the Cost? 70 seeks per sec In-memory data structures Hash table avoids seeks for misses Cache replacement (LRU, etc) RAM index size per TB Open Source (Squid) - 10 GB Commercial (Tiger) - 5 GB 5
17 What is the Cost? 70 seeks per sec In-memory data structures Hash table avoids seeks for misses Cache replacement (LRU, etc) RAM index size per TB Open Source (Squid) - 10 GB Commercial (Tiger) - 5 GB x 20 Can not use laptops for cache 2 servers, $2K each = 20 laptops 5
18 Our Solution 6
19 Our Solution HashCache: storage engine w/ plug-in indexing 6 schemes in paper, 3 shown here New Web proxy using HashCache engine 6
20 Our Solution HashCache: storage engine w/ plug-in indexing 6 schemes in paper, 3 shown here New Web proxy using HashCache engine Efficiency and Performance: Range of policies trading speed and memory 20-50x less RAM for Squid-like speed 6-10x less RAM vs Tiger (Commercial) 6
21 Our Solution HashCache: storage engine w/ plug-in indexing 6 schemes in paper, 3 shown here New Web proxy using HashCache engine Efficiency and Performance: Range of policies trading speed and memory 20-50x less RAM for Squid-like speed 6-10x less RAM vs Tiger (Commercial) Can now use cheap laptops vs servers Even for TB-sized caches 6
22 4 Our Solution 3 HashCache Current Better Gigabytes/Dollar Squid Tiger Performance: req/sec/disk 6
23 A Brief History of Cache 7
24 A Brief History of Cache URL 7
25 A Brief History of Cache URL ddddhhhhbbbb 7
26 A Brief History of Cache URL ddddhhhhbbbb } } } Directory Level 1 Directory Level 2 Actual File 7
27 A Brief History of Cache URL ddddhhhhbbbb } } } In-memory Hashtable Directory Level 1 Directory Level 2 Actual File 7
28 A Brief History of Cache URL Squid ddddhhhhbbbb } } } In-memory Hashtable Directory Level 1 Directory Level 2 Actual File 7
29 A Brief History of Cache URL 7
30 A Brief History of Cache URL Filesystem Circular Log 7
31 A Brief History of Cache URL hash_value Filesystem Circular Log 7
32 A Brief History of Cache URL hash_value hash_value Filesystem Circular Log offset Memory 7
33 A Brief History of Cache URL Tiger hash_value hash_value Filesystem Circular Log offset Memory 7
34 A Brief History of Cache Open Source Implementation - Squid Multiple seeks for hit, miss and write Dependent on default filesystems Commercial/High Performance - Tiger One seek for hit Custom file layout 7
35 Index Element Sizes 8
36 Index Element Sizes Functionality Existence Identification Replacement Policy Location Information Other 8
37 Index Element Sizes Functionality Existence Identification Replacement Policy Location Information Other Implementation Choice Hashtable Chaining Pointers Hash LRU List Pointers Disk Offset, Version Number, etc Expiration Date, Size, HTTP header info etc Total 8
38 Index Element Sizes Functionality Implementation Choice Existence Identification Replacement Policy Location Information Other Hashtable Chaining Pointers Hash LRU List Pointers Disk Offset, Version Number, etc Expiration Date, Size, HTTP header info etc Total 8 Focusing mainly on reducing the size of the index
39 Index Element Sizes Functionality Implementation Choice Squid (Bits) Existence Identification Hashtable Chaining Pointers 96 Hash 160 Replacement Policy LRU List Pointers 64 Location Information Other Disk Offset, Version Number, etc Expiration Date, Size, HTTP header info etc Total 560 8
40 Index Element Sizes Functionality Implementation Choice Squid (Bits) Tiger (Bits) Existence Identification Hashtable Chaining Pointers Hash Replacement Policy LRU List Pointers Location Information Other Disk Offset, Version Number, etc Expiration Date, Size, HTTP header info etc Total
41 Revisiting the Index... 9
42 Revisiting the Index... Application Cache Manager In-memory Index Reliable Filesystem 9
43 Revisiting the Index... Application Cache Manager Cache size limited by memory size In-memory Index Reliable Filesystem 9
44 Revisiting the Index... Application Cache Manager Cache size limited by memory size In-memory Index Reliable Filesystem Performance depends on disk seeks 9
45 Revisiting the Index... Application Cache Manager Cache size limited by memory size reduce the dependency In-memory Index Reliable Filesystem Performance depends on disk seeks 9
46 Revisiting the Index... Application Cache Manager Cache size limited by memory size reduce the dependency In-memory Index Reliable Filesystem Performance depends on disk seeks optimize for seeks 9
47 Revisiting the Index... Application Cache Eliminate(?) in-memory index Manager In-memory Index Reliable Filesystem 9
48 Revisiting the Index... Application Cache Manager Eliminate(?) in-memory index Need membership and location information In-memory Index Reliable Filesystem 9
49 Revisiting the Index... Application Cache Manager In-memory Index Eliminate(?) in-memory index Need membership and location information Use disk as hash table Reliable Filesystem 9
50 Revisiting the Index... Application Cache Manager In-memory Index Reliable Filesystem Eliminate(?) in-memory index Need membership and location information Use disk as hash table On disk data structures for key lookup Store the object as values for the keys 9
51 Revisiting the Index... Application Cache Manager Application Application Cache Manager Logic In-memory Index In-memory Index Reliable Filesystem HashCache Reliable Filesystem Engine 9
52 Revisiting the Index... Application Cache Manager Application Logic In-memory Index In-memory Index Reliable Filesystem HashCache Reliable Filesystem Engine 9
53 Revisiting the Index... Application Cache Manager Application Logic In-memory Index In-memory Index Reliable Filesystem HashCache Engine 9
54 Revisiting the Index... Application Cache Manager Application Logic In-memory Index Reliable Filesystem In-memory Index HashCache Engine 9
55 Revisiting the Index... Application Cache Manager Application Logic In-memory Index Reliable Filesystem In-memory Index HashCache Engine 9
56 HashCache: Basic Policy 10
57 HashCache: Basic Policy URL 10
58 HashCache: Basic Policy URL data 10
59 HashCache: Basic Policy URL hash_value H Bits data 10
60 HashCache: Basic Policy URL hash_value H Bits data N contiguous blocks (Disk Table) Filesystem 10
61 HashCache: Basic Policy URL hash_value t % N H Bits data N contiguous blocks (Disk Table) Filesystem 10
62 HashCache: Basic Policy URL hash_value % N H Bits t t th block data N contiguous blocks (Disk Table) Filesystem 10
63 HashCache: Basic Policy URL hash_value % N H Bits t t th block head data N contiguous blocks (Disk Table) 10 Filesystem Circular Log
64 HashCache: Basic Policy URL hash_value % N H Bits t t th block head N contiguous blocks (Disk Table) 10 Filesystem Circular Log
65 HashCache: Basic Policy URL hash_value % N H Bits t t th block head N contiguous blocks (Disk Table) 10 Filesystem Circular Log
66 HashCache: Basic Policy Advantages No index memory needed Tuned for one seek for most objects 10
67 HashCache: Basic Policy Advantages No index memory needed Tuned for one seek for most objects Disadvantages One seek per miss No collison control No cache replacement policy 10
68 Collision Control 11
69 Collision Control 11
70 Collision Control Chaining 11
71 Collision Control Does not transition well to disk-based Multiple seeks per operation Chaining Walking hash bin list Global replacement policy crosses bins 11
72 Collision Control Chaining 11
73 Collision Control Fixed locations where each object can be found Allocated contiguously, read together Set Associativity T-Ways 11
74 Collision Control Fixed locations where each object can be found Allocated contiguously, read together Seek time dominates short read Set Associativity T-Ways 11
75 Collision Control Fixed locations where each object can be found Allocated contiguously, read together Seek time dominates short read Set Associativity Eliminate global cache replacement policies T-Ways 11
76 Reducing Seeks 12
77 Reducing Seeks Bin Pointers 32 Chaining Pointers 64 In-memory hash table Too much memory for pointers Hash 32 Total (bits) 128 In-memory Hash Table 12
78 Reducing Seeks In-memory hash table Too much memory for pointers Disk is already a hash table Pointers not needed Large bitmap with the same layout as the disk Disk Table 12
79 Reducing Seeks H Bits Disk Block In-memory Bitmap Disk Table In-memory hash table Too much memory for pointers Disk is already a hash table Pointers not needed Large bitmap with the same layout as the disk Just store hash per URL 12
80 Reducing Seeks H Bits Disk Block In-memory Bitmap Disk Table In-memory hash table Too much memory for pointers Disk is already a hash table Pointers not needed Large bitmap with the same layout as the disk Just store hash per URL 12
81 Reducing Seeks 64 Original Hash Original hash of the URL: 64 bits 12
82 Reducing Seeks 64 Original Hash log(s) Original hash of the URL: 64 bits Eliminate bits for (same) bin # (2 28 objs, 8-way, #bins=2 25 (S)) 12
83 Reducing Seeks 64 Original Hash log(s) 8 low FP hash Original hash of the URL: 64 bits Eliminate bits for (same) bin # (2 28 objs, 8-way, #bins=2 25 (S)) Shrink hash size: Just to eliminate most false positives (8 bits) 12
84 Cache Replacement 64 Original Hash log(s) 8 low FP hash 13
85 Cache Replacement Large disks: million objects 64 Original Hash log(s) Global caching relevant when disk size working set When disk >> working set, local policies global policies 8 low FP hash 13
86 Cache Replacement Large disks: million objects 64 Original Hash log(s) 8 low FP hash 11 hash + rank Global caching relevant when disk size working set When disk >> working set, local policies global policies Local replacement benefits 3 bits per URL Performed on contiguous objects False positives limited by set size 13
87 HashCache: SetMem Policy 14
88 HashCache: SetMem Policy URL 14
89 HashCache: SetMem Policy URL data 14
90 HashCache: SetMem Policy URL hash_value % S t data 14
91 HashCache: SetMem Policy URL hash_value % S t head data Memory Filesystem 14
92 HashCache: SetMem Policy URL hash_value % S 11 Bits t th set t data t th set head Memory Filesystem 14
93 HashCache: SetMem Policy URL hash_value % S 11 Bits t th set t data t th set head Memory Filesystem 14
94 HashCache: SetMem Policy URL hash_value % S 11 Bits t th set t t th set head Memory Filesystem 14
95 HashCache: SetMem Policy URL LRU hash_value % S LRU 11 Bits t th set t t th set head Memory Filesystem 14
96 HashCache: SetMem Policy Advantages No seeks for most misses 1 seek per read, 1 seek per write Good hash + replacement in 11 bits 14
97 HashCache: SetMem Policy Advantages No seeks for most misses 1 seek per read, 1 seek per write Good hash + replacement in 11 bits Disadvantages Writes still need seeks 14
98 Further Reducing Seeks Disk Table 15
99 Further Reducing Seeks Storing objects by hash can produce random reads & writes Disk Table 15
100 Further Reducing Seeks Storing objects by hash can produce random reads & writes Restructure on-disk table Disk Table 15
101 Further Reducing Seeks Storing objects by hash can produce random reads & writes Restructure on-disk table 11 hash rank Store only hash, rank, offset 15
102 Further Reducing Seeks Storing objects by hash can produce random reads & writes Restructure on-disk table 11 hash rank Store only hash, rank, offset Move all data to log 43 hash offset rank 15
103 Further Reducing Seeks Storing objects by hash can produce random reads & writes Restructure on-disk table 11 hash rank 43 hash rank offset 15 Store only hash, rank, offset Move all data to log Benefits Group writes amortize seeks Scheduling related writes enables read prefetch Both reads & writes < 1 seek
104 Further Reducing Seeks 0 HC-Basic 11 HC-SetMem 43 HC-Log Storing objects by hash can produce random reads & writes Restructure on-disk table Move all data to log Store only hash, rank, offset Benefits Group writes amortize seeks Scheduling related writes enables read prefetch Both reads & writes < 1 seek 15
105 Further Reducing Seeks 0 HC-Basic 11 HC-SetMem 43 HC-Log 232 Tiger 560 Squid Storing objects by hash can produce random reads & writes Restructure on-disk table Move all data to log Benefits Store only hash, rank, offset Group writes amortize seeks Scheduling related writes enables read prefetch Both reads & writes < 1 seek 15
106 HashCache: Log Policy 16
107 HashCache: Log Policy URL 16
108 HashCache: Log Policy URL data 16
109 HashCache: Log Policy URL hash_value % S t data 16
110 HashCache: Log Policy hash_value URL LRU % S head t data Circular Log Memory Filesystem 16
111 HashCache: Log Policy hash_value URL LRU % S head t t th set 43 Bits data Circular Log Memory Filesystem 16
112 HashCache: Log Policy hash_value URL LRU % S head t t th set 43 Bits data Circular Log Memory Filesystem 16
113 Implementation 17
114 Implementation HashCache Storage Engine with plug-in policies 17
115 Implementation HashCache Storage Engine with plug-in policies HashCache Web proxy using storage engine 17
116 Implementation HashCache Storage Engine with plug-in policies HashCache Web proxy using storage engine Multiple apps on same box, sharing memory 17
117 Implementation HashCache Storage Engine with plug-in policies HashCache Web proxy using storage engine Multiple apps on same box, sharing memory 20,000 lines C code for the proxy and 1000 lines for the indexing policies 17
118 Implementation HashCache Storage Engine with plug-in policies Event Driven implementation with non-blocking I/O HashCache Web proxy using storage engine Multiple apps on same box, sharing memory 20,000 lines C code for the proxy and 1000 lines for the indexing policies 17
119 Implementation HashCache Storage Engine with plug-in policies HashCache Web proxy using storage engine Multiple apps on same box, sharing memory Event Driven implementation with non-blocking I/O Design similar to that of Flash Web Server. Helpers for I/O and DNS lookups 20,000 lines C code for the proxy and 1000 lines for the indexing policies 17
120 Implementation HashCache Storage Engine with plug-in policies HashCache Web proxy using storage engine Multiple apps on same box, sharing memory 20,000 lines C code for the proxy and 1000 lines for the indexing policies Event Driven implementation with non-blocking I/O Design similar to that of Flash Web Server. Helpers for I/O and DNS lookups Balances load across multiple disks easily and makes scaling obvious 17
121 Evaluation - Web Polygraph De-facto feature and performance testing tool for web proxies Compare all variants of HashCache with Squid and Tiger 18
122 Evaluation - Web Polygraph De-facto feature and performance testing tool for web proxies Compare all variants of HashCache with Squid and Tiger Experiment Name Setting Configuration Comparision 18
123 Evaluation - Web Polygraph De-facto feature and performance testing tool for web proxies Compare all variants of HashCache with Squid and Tiger Experiment Name Setting Configuration Comparision Low End Small School using Laptop 1.4 GHz 256 MB 60 GB SATA HashCache vs Squid vs Tiger 18
124 Evaluation - Web Polygraph De-facto feature and performance testing tool for web proxies Compare all variants of HashCache with Squid and Tiger Experiment Name Setting Configuration Comparision Low End Small School using Laptop 1.4 GHz 256 MB 60 GB SATA HashCache vs Squid vs Tiger High End ISP with High-End Server 2 GHz 3.5 GB 5x18 GB SCSI HashCache-Log vs Squid vstiger 18
125 Evaluation - Web Polygraph De-facto feature and performance testing tool for web proxies Compare all variants of HashCache with Squid and Tiger Experiment Name Setting Configuration Comparision Low End Small School using Laptop 1.4 GHz 256 MB 60 GB SATA HashCache vs Squid vs Tiger High End ISP with High-End Server 2 GHz 3.5 GB 5x18 GB SCSI HashCache-Log vs Squid vstiger Large Disk Large School with Mini-Tower 1.4 GHz 2 GB 2x1TB USB HashCache-Log vs HashCache-SetMem 18
126 Hit Rate Comparison 60 Max 45 Hit Rate HC-Basic Squid HC-SetMem HC-Log Tiger 19
127 Low End Configuration
128 Low End Configuration 300 Performance req/sec HC-Basic Squid HC-SetMem Tiger HC-Log
129 Low End Configuration 300 Open Source and Commercial 50% 20% could index only 18 GB Performance req/sec % RAM for Index HashCache could index 60 GB 75% 5% 0 HC-Basic Squid HC-SetMem Tiger HC-Log
130 High End Configuration 3,000 5x18 GB Disk Performance req/sec 2,250 1, Squid HashCache-Log Tiger 21
131 High End Configuration 3,000 5x18 GB Disk Performance req/sec 2,250 1, % RAM for Index 4% 18% 0 Squid HashCache-Log Tiger 21
132 Index Efficiency
133 Index Efficiency Squid Tiger HC-Log HC-SetMem 0 1,500 3,000 4,500 6,000 Max Disk for 1GB RAM
134 Index Efficiency Squid 40 reqs/sec Tiger 300 HC-Log 300 HC-SetMem ,500 3,000 4,500 6,000 Max Disk for 1GB RAM
135 Large Disk Configuration 1 TB Disk 300 Performance req/sec HC-Basic HC-SetMem HC-Log 23
136 Large Disk Configuration 1 TB Disk MB Performance req/sec MB Index 150 MB 0 HC-Basic HC-SetMem HC-Log 23
137 Conclusions & Status New Storage Engine & Web Cache From no RAM per object to tiny no. of bits/obj 6-10x better than Tiger, 20-50x vs Squid Enables large disk w/ only laptop-class machine More policies, details in paper Suitable for developing-world usage Current deployments: Ghana, Nigeria Working w/ school supplier on new deployments 24
138 Thank You! 25
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