Cloud computing: data-intensive computing and scheduling
"This practical book delves into new cloud computing technologies and indicates the main challenges for their development in the future, especially for resource management problems. By systematizing cloud resource management problems, it helps knowledgeable readers who are not subject matter ex...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, FL [u.a.]
CRC Press
2013
|
| Series: | Chapman & Hall CRC numerical analysis and scientific computing
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | "This practical book delves into new cloud computing technologies and indicates the main challenges for their development in the future, especially for resource management problems. By systematizing cloud resource management problems, it helps knowledgeable readers who are not subject matter experts in a topic but want to have an in-depth analysis. It provides a parallel programming model, MapReduce, to parallelize multidimensional analytical query processing. The text includes how to master the fundamental concepts and programming models and apply them successfully to reach objectives. The authors discuss how to maximize the value of existing scheduling algorithms from a theoretical point of view"-- |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXIII, 205 S. graph. Darst. |
| ISBN: | 9781466507821 |
Staff View
MARC
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| 264 | 1 | |a Boca Raton, FL [u.a.] |b CRC Press |c 2013 | |
| 300 | |a XXIII, 205 S. |b graph. Darst. | ||
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| 490 | 0 | |a Chapman & Hall CRC numerical analysis and scientific computing | |
| 500 | |a Includes bibliographical references and index | ||
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| 650 | 4 | |a Cloud computing | |
| 650 | 4 | |a Parallel programs (Computer programs) | |
| 650 | 4 | |a Computer scheduling | |
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| 650 | 7 | |a MATHEMATICS / Number Systems |2 bisacsh | |
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Record in the Search Index
| _version_ | 1804149503656198144 |
|---|---|
| adam_text | Contents
List of figures
xiü
List of tables xv
Foreword
xvii
Preface six
Warranty xxv
1
Overview of cloud computing
1
1.1
Introduction
.............................. 1
1.1.1
Cloud definitions
....................... 1
1.1.2
System architecture
...................... 2
1.1.3
Deployment models
..................... 3
1.1.4
Cloud characteristics
..................... 3
1.2
Cloud evolution
............................ 6
1.2.1
Getting ready for the cloud
.................. 6
1.2.2
Brief history
......................... 7
1.2.3
Comparison with related technologies
............ 8
1.3
Cloud services
............................ 10
1.4
Cloud projects
............................ 12
1.4.1
Commercial products
..................... 12
1.4.2
Research projects
....................... 13
1.5
Cloud challenges
........................... 15
1.5.1
MapReduce programming model
.............. 15
1.5.2
Data management
....................... 16
1.5.3
Resource scheduling
..................... 16
1.6
Concluding remarks
......................... 17
2
Resource scheduling for cloud computing
19
2.1
Introduction
.............................. 19
2.2
Cloud service scheduling hierarchy
................. 19
2.3
Economic models for resource-allocation scheduling
........ 21
2.3.1
Market strategies
....................... 21
2.3.2
Auction strategies
....................... 23
2.3.3
Economic schedulers
..................... 25
VII
Vlil
2.4
Heuristic models for task-execution scheduling
........... 27
2.4.1
Static strategies
........................ 28
2.4.2
Dynamic strategies
...................... 30
2.4.3
Heuristic schedulers
..................... 32
2.5
Real-time scheduling in cloud computing
.............. 35
2.5.1
Fixed priority strategies
................... 35
2.5.2
Dynamic priority strategies
.................. 37
2.5.3
Real-time schedulers
..................... 38
2.6
Concluding remarks
......................... 39
Game theoretical allocation in a cloud
datacenter
41
3.1
Introduction
.............................. 41
3.2
Game theory
............................. 42
3.2.1
Normal formulation
..................... 42
3.2.2
Payoff choice and utility function
.............. 43
3.2.3
Strategy choice and Nash equilibrium
............ 45
3.3
Cloud resource allocation model
................... 46
3.3.1
Bid-shared auction
...................... 46
3.3.2
Non-cooperative game
.................... 47
3.4
Nash equilibrium allocation algorithms
............... 48
3.4.1
Bid functions
......................... 48
3.4.2
Parameters estimation
.................... 50
3.4.3
Equilibrium price
....................... 53
3.5
Implementation in a cloud
datacenter
................ 55
3.5.1
Cloudsim toolkit
....................... 55
3.5.2
Communication among entities
............... 55
3.5.3
Bidding algorithms
...................... 57
3.5.4
Comparison of forecasting methods
............. 57
3.6
Concluding remarks
......................... 61
Multi-dimensional data analysis in a cloud
datacenter
63
4.1
Introduction
.............................. 63
4.2
Pre-computing
............................ 64
4.2.1
Data cube
........................... 65
4.2.2
Sparse cube
.......................... 65
4.2.3
Reuse of previous query results
............... 66
4.2.4
Data compressing
....................... 66
4.3
Data indexing
............................. 67
4.4
Data partitioning
........................... 68
4.4.1
Data partitioning methods
.................. 68
4.4.2
Horizontal partitioning of a multi-dimensional
dataset
... 70
4.4.3
Vertical partitioning of a multi-dimensional
dataset
..... 73
4.5
Data replication
............................ 75
4.6
Query processing parallelism
.................... 75
4.6.1
Inter- and intra-operators
................... 76
їх
4.6.2
Exchange operator
...................... 77
4.6.3
SQL operator parallelization
................. 78
4.7
Concluding remarks
......................... 84
Data intensive applications with MapReduce
85
5.1
Introduction
.............................. 85
5.2
MapReduce: New parallel computing model in cloud computing
. . 86
5.2.1
Dataflow model
........................ 86
5.2.2
Two frameworks: GridGain versus Hadoop
......... 89
5.2.3
Communication cost analysis
................ 90
5.3
Distributed data storage underlying MapReduce
........... 92
5.3.1
Google file system
...................... 92
5.3.2
Distributed cache memory
.................. 93
5.3.3
Data accessing
........................ 94
5.4
Large-scale data analysis based on MapReduce
........... 95
5.4.1
Data query languages
..................... 96
5.4.2
Data analysis applications
.................. 96
5.4.3
Comparison with shared-nothing parallel databases
..... 97
5.5
SimMapReduce: Simulator for modeling MapReduce framework
. 100
5.5.1
Multi-layer architecture
................... 101
5.5.2
Input and output of simulator
................. 102
5.5.3
Implementation details of simulator
............. 105
5.5.4
Modeling process
....................... 107
5.6
Concluding remarks
......................... 109
Large-scale multi-dimensional data aggregation 111
6.1
Introduction
..............................
Ill
6.2
Data organization
...........................
Ill
6.2.1
Computations in data explorations
.............. 114
6.2.2
Multiple group-by query
................... 117
6.3
Choosing a right MapReduce framework
.............. 118
6.3.1
Advantages of GridGain
................... 118
6.3.2
Combiner support in Hadoop and GridGain
......... 119
6.3.3
Realizing MapReduce applications with GridGain
..... 119
6.3.4
Workflow analysis of GridGain procedure
.......... 120
6.4
Parallelizing single group-by query with MapReduce
........ 122
6.5
Parallelizing multiple group-by query with MapReduce
....... 122
6.5.1
Data partitioning and data placement
............ 123
6.5.2
MapReduce model-based implementation
.......... 123
6.5.3
MapCombineReduce model-based implementation
..... 126
6.6
Cost estimation
............................ 128
6.6.1
MapReduce model-based implementation
.......... 128
6.6.2
MapCombineReduce model-based implementation
..... 131
6.6.3
Comparison of implementations
............... 132
6.7
Concluding remarks
......................... 133
7
Multi-dimensional data analysis optimization
135
7.1
Introduction
.............................. 135
7.2
Data-locating based job-scheduling
................. 135
7.2.1
Job-scheduling implementation
............... 136
7.2.2
Two-level scheduling
..................... 136
7.2.3
Alternative job-scheduling schemes
............. 137
7.3
Improvements by speed-up measurements
.............. 137
7.3.1
Horizontal partitioning
.................... 138
7.3.2
Vertical partitioning
..................... 141
7.4
Improvements by affecting factors
.................. 143
7.4.1
Query selectivity
....................... 144
7.4.2
Side effects
.......................... 144
7.5
Improvement by cost estimation
................... 145
7.5.1
Horizontal partitioning
.................... 146
7.5.2
Vertical partitioning
..................... 149
7.5.3
Comparison of partitioning
.................. 150
7.6
Compressed data structures
..................... 151
7.6.1
Data structure description
.................. 151
7.6.2
Data structures for storing recordlD-list
........... 152
7.6.3
Compressed data structures for different dimensions
.... 152
7.6.4
Bitmap sparcity and compressing
.............. 154
7.7
Concluding remarks
......................... 155
8
Real-time scheduling with MapReduce
157
8.1
Introduction
.............................. 157
8.2
Real-time scheduling problem
.................... 158
8.2.1
Real-time task
........................ 158
8.2.2
Processing resource
...................... 159
8.2.3
Scheduling algorithms
.................... 160
8.3
Schedulability test in the cloud
datacenter
.............. 160
8.3.1
Pseudo-polynomial complexity
............... 161
8.3.2
Polynomial complexity
.................... 162
8.3.3
Constant complexity
..................... 163
8.4
Utilization bounds for schedulability testing
............. 164
8.4.1
Classical bound
........................ 164
8.4.2
Closer periods
........................ 165
8.4.3
Harmonic chains
....................... 165
8.4.4
Hyperbolic bound
....................... 165
8.5
Real-time task scheduling with MapReduce
............. 166
8.5.1
System model
......................... 166
8.5.2
MapReduce segmentation
.................. 167
8.5.3
Worst pattern for a schedulable task set
........... 168
8.6
Reliability indication methods
.................... 174
8.6.1
Reliability indicator
..................... 174
8.6.2
Schedulabilitv test conditions
................ 176
Xl
8.6.3
Comparison of
rate monotonie
conditions
.......... 177
8.6.4
Comparison of deadline
monotonie
conditions
....... 178
8.7
Concluding remarks
......................... 182
9
Future for cloud computing
183
Bibliography
187
Index
203
|
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| id | DE-604.BV040445322 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:24:04Z |
| institution | BVB |
| isbn | 9781466507821 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025293071 |
| oclc_num | 815935153 |
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| physical | XXIII, 205 S. graph. Darst. |
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| series2 | Chapman & Hall CRC numerical analysis and scientific computing |
| spelling | Magoulès, Frédéric Verfasser (DE-588)136913806 aut Cloud computing data-intensive computing and scheduling Frederic Magoules, Jie Pan, and Fei Teng Boca Raton, FL [u.a.] CRC Press 2013 XXIII, 205 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall CRC numerical analysis and scientific computing Includes bibliographical references and index "This practical book delves into new cloud computing technologies and indicates the main challenges for their development in the future, especially for resource management problems. By systematizing cloud resource management problems, it helps knowledgeable readers who are not subject matter experts in a topic but want to have an in-depth analysis. It provides a parallel programming model, MapReduce, to parallelize multidimensional analytical query processing. The text includes how to master the fundamental concepts and programming models and apply them successfully to reach objectives. The authors discuss how to maximize the value of existing scheduling algorithms from a theoretical point of view"-- Cloud computing Parallel programs (Computer programs) Computer scheduling COMPUTERS / Internet / General bisacsh MATHEMATICS / General bisacsh MATHEMATICS / Number Systems bisacsh Cloud Computing (DE-588)7623494-0 gnd rswk-swf Cloud Computing (DE-588)7623494-0 s DE-604 Pan, Jie Verfasser aut Teng, Fei Verfasser (DE-588)133456013 aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025293071&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Magoulès, Frédéric Pan, Jie Teng, Fei Cloud computing data-intensive computing and scheduling Cloud computing Parallel programs (Computer programs) Computer scheduling COMPUTERS / Internet / General bisacsh MATHEMATICS / General bisacsh MATHEMATICS / Number Systems bisacsh Cloud Computing (DE-588)7623494-0 gnd |
| subject_GND | (DE-588)7623494-0 |
| title | Cloud computing data-intensive computing and scheduling |
| title_auth | Cloud computing data-intensive computing and scheduling |
| title_exact_search | Cloud computing data-intensive computing and scheduling |
| title_full | Cloud computing data-intensive computing and scheduling Frederic Magoules, Jie Pan, and Fei Teng |
| title_fullStr | Cloud computing data-intensive computing and scheduling Frederic Magoules, Jie Pan, and Fei Teng |
| title_full_unstemmed | Cloud computing data-intensive computing and scheduling Frederic Magoules, Jie Pan, and Fei Teng |
| title_short | Cloud computing |
| title_sort | cloud computing data intensive computing and scheduling |
| title_sub | data-intensive computing and scheduling |
| topic | Cloud computing Parallel programs (Computer programs) Computer scheduling COMPUTERS / Internet / General bisacsh MATHEMATICS / General bisacsh MATHEMATICS / Number Systems bisacsh Cloud Computing (DE-588)7623494-0 gnd |
| topic_facet | Cloud computing Parallel programs (Computer programs) Computer scheduling COMPUTERS / Internet / General MATHEMATICS / General MATHEMATICS / Number Systems Cloud Computing |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025293071&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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