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E-raamat: Fundamentals of Grid Computing: Theory, Algorithms and Technologies [Taylor & Francis e-raamat]

Edited by (Ecole Centrale Paris, Chatenay Malabry, France Ecole Centrale Paris, Chatenay Malabry, France Ecole Centrale Paris, Chatenay Malabry, France Ecole Centrale Paris, Chatenay Malabry, France)
  • Formaat: 322 pages
  • Ilmumisaeg: 25-Sep-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429166082
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Fundamentals of Grid Computing: Theory, Algorithms and TechnologiesSuurem pilt
  • Formaat: 322 pages
  • Ilmumisaeg: 25-Sep-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429166082
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta

The integration and convergence of state-of-the-art technologies in the grid have enabled more flexible, automatic, and complex grid services to fulfill industrial and commercial needs, from the LHC at CERN to meteorological forecasting systems. Fundamentals of Grid Computing: Theory, Algorithms and Technologies discusses how the novel technologies of semantic web and workflow have been integrated into the grid and grid services.





The book explains how distributed mutual exclusion algorithms offer solutions to transmission and control processes. It also addresses the replication problem in data grids with limited replica storage and the problem of data management in grids. After comparing utility, grid, autonomic, and cloud computing, the book presents efficient solutions for the reliable execution of applications in computational grid platforms. It then describes a fault tolerant distributed scheduling algorithm for large-scale distributed applications, along with broadcasting algorithms for institutional grids. The final chapter shows how load balancing is integrated into a real-world scientific application.





Helping readers develop practical skills in grid technology, the appendices introduce user-friendly open source software written in Java. One of the software packages covers strategies for data replication in the grid. The other deals with the implementation of a simulator for distributed scheduling in grid environments.





The various technology presented in this book demonstrates the wide aspects of interest in grid computing as well as the many possibilities and venues that exist in this research area. This interest will only further evolve as numerous exciting developments still await us.

List of figures
xiii
List of tables
xvii
Foreword xix
Preface xxi
Warranty xxiii
1 Grid computing overview
1(28)
Frederic Magoules
Thi-Mai-Huong Nguyen
Lei Yu
1.1 Introduction
1(1)
1.2 Definitions
2(1)
1.3 Classifying grid systems
3(1)
1.4 Grid applications
4(1)
1.5 Grid architecture
5(1)
1.6 Grid computing projects
6(16)
1.6.1 Grid middleware (core services)
6(5)
1.6.2 Grid resource brokers and schedulers
11(3)
1.6.3 Grid systems
14(2)
1.6.4 Grid programming environments
16(2)
1.6.5 Grid portals
18(4)
1.7 Grid evolution
22(1)
1.8 Concluding remarks
23(1)
1.9 References
24(5)
2 Synchronization protocols for sharing resources in grid environments
29(38)
Julien Sopena
Luciana Arantes
Fabrice Legond-Aubry
Pierre Sens
2.1 Introduction
29(2)
2.2 Token-based mutual exclusion algorithms
31(5)
2.2.1 Martin's algorithm
31(2)
2.2.2 Naimi-Trehel's algorithm
33(1)
2.2.3 Suzuki-Kasami's algorithm
34(2)
2.3 Mutual exclusion algorithms for large configurations
36(3)
2.3.1 Priority-based approach
36(1)
2.3.2 Composition-based approach
37(2)
2.4 Composition approach to mutual exclusion algorithms
39(4)
2.4.1 Coordinator processes
41(2)
2.5 Composition properties and its natural effects
43(4)
2.5.1 Filtering and aggregation
43(2)
2.5.2 Preemption and structural effects
45(1)
2.5.3 Natural effects of composition
46(1)
2.6 Performance evaluation
47(15)
2.6.1 Experiment parameters
47(2)
2.6.2 Performance results: composition study
49(7)
2.6.3 The impact of the grid architecture
56(6)
2.7 Concluding remarks
62(1)
2.8 References
63(4)
3 Data replication in grid environments
67(34)
Thi-Mai-Huong Nguyen
Frederic Magoules
3.1 Introduction
67(1)
3.2 Data replication
68(8)
3.2.1 Replication in databases
69(1)
3.2.2 Replication in peer-to-peer systems
70(1)
3.2.3 Replication in web environments
71(1)
3.2.4 Replication in data grids
72(4)
3.3 System architecture
76(2)
3.4 Selective-rank model for a replication system
78(4)
3.4.1 Model assumptions
79(1)
3.4.2 Estimating the availability of files
80(1)
3.4.3 Problem definition
80(2)
3.5 Selective-rank replication algorithm
82(3)
3.5.1 Popularity of files
82(1)
3.5.2 Correlation of files
82(1)
3.5.3 MaxDAR optimizer algorithm
83(2)
3.6 Evaluation
85(9)
3.6.1 Grid configuration
87(1)
3.6.2 Experimental results
87(7)
3.7 Concluding remarks
94(1)
3.8 References
95(6)
4 Data management in grids
101(24)
Jean-Marc Pierson
4.1 Introduction
101(2)
4.2 From data sources to databases to data sources
103(1)
4.3 Positioning the data management in grids within distributed systems
104(2)
4.4 Links with the other services of the middleware
106(1)
4.5 Problems and some solutions
107(9)
4.5.1 Data identification, indexing, metadata
107(2)
4.5.2 Data access, interoperability, query processing, transactions
109(2)
4.5.3 Transport
111(1)
4.5.4 Placement, replication, caching
112(1)
4.5.5 Security: transport, authentication, access control, encryption
113(2)
4.5.6 Consistency
115(1)
4.6 Toward pervasive, autonomic and on-demand data management
116(1)
4.7 Concluding remarks
117(1)
4.8 References
118(7)
5 Future of grids resources management
125(18)
Fei Teng
Frederic Magoules
5.1 Introduction
125(1)
5.2 Several computing paradigms
126(3)
5.2.1 Utility computing
126(1)
5.2.2 Grid computing
127(1)
5.2.3 Autonomic computing
127(1)
5.2.4 Cloud computing
128(1)
5.3 Definition of cloud computing
129(1)
5.3.1 One definition
129(1)
5.3.2 Architecture
130(1)
5.4 Cloud services
130(4)
5.4.1 Three-level services
130(2)
5.4.2 Service characters
132(2)
5.5 Cloud resource management
134(3)
5.5.1 Comparison with grid systems
134(1)
5.5.2 Resource model
135(1)
5.5.3 Economy-oriented model
136(1)
5.6 Future direction of resource scheduling
137(2)
5.6.1 Scalable and dynamic
138(1)
5.6.2 Secure and trustable
138(1)
5.6.3 Virtual machines-based
138(1)
5.7 Concluding remarks
139(1)
5.8 References
140(3)
6 Fault-tolerance and availability awareness in computational grids
143(34)
Xavier Besseron
Mohamed-Slim Bouguerra
Thierry Gautier
Erik Saule
Denis Trystram
6.1 Introduction
143(3)
6.2 Background and definitions
146(3)
6.2.1 Grid architecture and execution model
147(1)
6.2.2 Faults models
148(1)
6.2.3 Consistent system states
148(1)
6.3 Multi-objective scheduling for safety
149(4)
6.3.1 Generalities
149(1)
6.3.2 No duplication
150(2)
6.3.3 Using duplication
152(1)
6.4 Stable memory-based protocols
153(3)
6.4.1 Log-based rollback recovery
153(2)
6.4.2 Checkpoint-based rollback recovery
155(1)
6.5 Stochastic checkpoint model analysis issues
156(7)
6.5.1 Completion time without fault tolerance
157(2)
6.5.2 Impact of checkpointing on the completion time
159(4)
6.6 Implementations
163(5)
6.6.1 Single process snapshot
164(1)
6.6.2 Fault-tolerance protocol implementations
164(2)
6.6.3 Implementation comparison
166(2)
6.7 Concluding remarks
168(2)
6.8 References
170(7)
7 Fault tolerance for distributed scheduling in grids
177(30)
Lei Yu
Frederic Magoules
7.1 Introduction
177(2)
7.2 Fault tolerance in distributed systems
179(1)
7.3 Distributed scheduling model
180(3)
7.3.1 MMS fault tolerance
180(1)
7.3.2 LMS/SMS fault tolerance
181(1)
7.3.3 CR fault tolerance
182(1)
7.4 Fault detection and repairing in the tree structure
183(6)
7.4.1 Notations
183(1)
7.4.2 Algorithms description
183(5)
7.4.3 Messages treatment analysis
188(1)
7.5 Distributed scheduling algorithm
189(2)
7.5.1 Distributed dynamic scheduling algorithm with fault tolerance (DDFT)
189(1)
7.5.2 Algorithm fault tolerance issues
190(1)
7.6 SimGrid and simulation design
191(1)
7.7 Evaluation
192(7)
7.7.1 Simulation setup
193(1)
7.7.2 Comparison with centralized scheduling
193(4)
7.7.3 Fault tolerance experiments
197(1)
7.7.4 Workload analysis
197(2)
7.8 Related work
199(1)
7.9 Concluding remarks
200(1)
7.10 References
201(6)
8 Broadcasting for grids
207(28)
Christopke Cerin
Luiz-Angelo Steffenel
Hazem Fkaier
8.1 Introduction
207(1)
8.2 Broadcastings
208(3)
8.3 Heuristics for broadcasting
211(9)
8.3.1 Basic approaches for broadcasting in homogeneous environments
212(1)
8.3.2 Advanced approaches for heterogeneous clusters
213(1)
8.3.3 Grid aware heuristics
214(1)
8.3.4 New approach for broadcasting in clusters and hyper clusters
215(5)
8.4 Related work and related methods
220(10)
8.4.1 Broadcasting and dynamic programming
220(3)
8.4.2 Multi-criteria approach
223(5)
8.4.3 Broadcast for clusters
228(2)
8.4.4 Broadcast and heterogeneous systems
230(1)
8.5 Concluding remarks
230(2)
8.6 References
232(3)
9 Load balancing algorithms for dynamic networks
235(38)
Jacques M. Bahi
Raphael Couturier
Abderrahmane Sider
9.1 Introduction
235(2)
9.2 A taxonomy for load balancing
237(3)
9.3 Distributed load balancing algorithms for static networks
240(10)
9.3.1 Network model and performance measures
240(2)
9.3.2 Diffusion
242(4)
9.3.3 Dimension exchange
246(2)
9.3.4 GDE
248(2)
9.3.5 Second order algorithms
250(1)
9.4 Distributed load balancing algorithms for dynamic networks
250(7)
9.4.1 Adaption to dynamic networks
251(1)
9.4.2 Generalized adaptive exchange (GAE)
251(4)
9.4.3 Illustrating the generalized adaptive exchange most to least loaded policy on a dynamic network
255(2)
9.5 Implementation
257(4)
9.5.1 On synchronous and asynchronous approaches
257(2)
9.5.2 How to define the load for some applications
259(1)
9.5.3 Implementation of static algorithms
259(1)
9.5.4 Implementation of dynamic algorithms
260(1)
9.6 A practical example: the advection diffusion application
261(7)
9.6.1 Load balancing and the application
264(2)
9.6.2 Load balancing in a dynamic network
266(2)
9.7 Concluding remarks
268(1)
9.8 References
269(4)
A Implementation of the replication strategies in OptorSim
273(6)
Thi-Mai-Huong Nguyen
Frederic Magoules
A.1 Introduction
273(1)
A.2 Download
274(1)
A.3 Implementation
274(2)
A.3.1 OptorSim implementation
274(1)
A.3.2 MaxDAR implementation
275(1)
A.4 How to execute the simulation
276(3)
B Implementation of the simulator for the distributed scheduling model
279(4)
Lei Yu
Frederic Magoules
B.1 Introduction
279(1)
B.2 Download
279(1)
B.3 Implementation
280(2)
B.3.1 Data structures
280(1)
B.3.2 Functions
280(2)
B.4 How to execute the simulation
282(1)
Glossary 283(14)
Author Index 297
Frédéric Magoulès is a professor in the Applied Mathematics and Systems Laboratory at École Centrale Paris in Châtenay-Malabry, France.
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