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E-raamat: Resource Service Management in Manufacturing Grid System [Wiley Online]

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  • Formaat: 550 pages
  • Ilmumisaeg: 10-Feb-2012
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1118288769
  • ISBN-13: 9781118288764
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  • Wiley Online
  • Hind: 206,17 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Resource Service Management in Manufacturing Grid SystemSuurem pilt
  • Formaat: 550 pages
  • Ilmumisaeg: 10-Feb-2012
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1118288769
  • ISBN-13: 9781118288764
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118288764
This book includes discussion on advance computer technologies such as cloud computing, grid computing, and service computing. In addition, it furthers the theory and technology of grid technologies that is used in manufacturing, and accelerates the development of service-oriented manufacturing.
Acknowledgements xxi
Preface xxiii
Abbreviations xxix
1 Introduction to Manufacturing Grid
1(26)
1.1 Introduction
1(1)
1.2 Proposal of Manufacturing Grid
2(6)
1.2.1 Several Issues of Manufacturing
2(2)
1.2.2 Proposal of MGrid
4(2)
1.2.3 Technological Driving Forces of MGrid
6(2)
1.3 Concept of MGrid
8(5)
1.3.1 A Brief Outline of Grid and its Applications
8(2)
1.3.2 Concept of MGrid
10(3)
1.4 Basic Features of MGrid
13(1)
1.5 The Connotation of MGrid
14(2)
1.6 Comparison between MGrid and Networked Manufacturing
16(3)
1.7 Comparison between MGrid and Computing Grid
19(1)
1.8 Key Research Contents and Technologies of MGrid
20(6)
1.8.1 General Technologies
20(2)
1.8.2 Supporting Technologies
22(1)
1.8.3 Key Enabling Technologies
23(2)
1.8.4 Application Technologies
25(1)
1.9 Summary
26(1)
2 Resource Service Optimal-Allocation System in MGrid
27(16)
1.1 Introduction
27(1)
2.2 The Architecture of MGrid
28(3)
2.2.1 MGrid Resource Layer
28(1)
2.2.2 MGrid Core Middleware Layer
29(1)
2.2.3 MGrid User Service Middleware Layer
30(1)
2.2.4 MGrid User Portal Layer
30(1)
2.2.5 MGrid User Layer
30(1)
2.3 MGrid Collaborative Manufacturing Platform
31(4)
2.3.1 Conceptual Model of MGrid Collaborative Manufacturing Platform
31(2)
2.3.2 Resource Service Publication
33(2)
2.3.3 The Resource Service Exchange between RSP and RSD
35(1)
2.4 MGrid Resource Service Optimal-Allocation System (MGRSOAS)
35(3)
2.5 The Key Issues and Technologies for Realizing RSOAS
38(3)
2.5.1 Modeling and Digital Description of Resource Service (DDoRS)
39(1)
2.5.2 Resource Service Match and Search (RSMS)
39(1)
2.5.3 QoS Modeling and Evaluation of Resource Service
39(1)
2.5.4 MGrid Resource Service Optimal-Selection and Composition (RSOSC)
40(1)
2.5.5 Resource Service Composition and Network Modeling and Dynamic Characteristics
40(1)
2.5.6 Failure Tolerance Management
41(1)
2.6 Summary
41(2)
3 Digital Description of MGrid Resource Service
43(32)
3.1 Introduction
43(1)
3.2 Classification of MGrid Resource Service and its Application
44(9)
3.2.1 Classification of MGrid Resource and Resource Service
44(1)
3.2.2 Application Case: Resource Service Design for Magnetic Bearing System
45(8)
3.3 Requirements of DDoRS in MGrid
53(1)
3.4 MGrid and Ontology
54(1)
3.5 Establishing the Method of MGrid-Ontology
55(4)
3.5.1 Step 1: Define the Scope and Requirements of MGrid-Ontology
56(1)
3.5.2 Step 2: Determine Essential Concepts, Reusing Existing Ontologies if Possible
57(1)
3.5.3 Step 3: Analyses and Design of MGrid-Ontology
57(1)
3.5.4 Step 4: Representation of MGrid-Ontology
58(1)
3.5.5 Step 5: Verification and Validation of MGrid-Ontology
58(1)
3.6 Selection of Describing Language for MGrid-Ontology
59(3)
3.7 MGrid Ontology
62(6)
3.7.1 OWL-S
62(1)
3.7.2 MGrid-Ontology
63(5)
3.8 DDoRS Based on MGrid-Ontology
68(3)
3.8.1 Description of Agent
68(1)
3.8.2 Description of MGSP
68(3)
3.8.3 Description of MGSM
71(1)
3.9 Application Case: MGrid-Ontology Based MGrid Resource Service Discovery
71(3)
3.10 Summary
74(1)
4 MGrid Resource Service Match and Search
75(40)
4.1 Introduction
75(2)
4.2 Related Works
77(2)
4.2.1 Service Discovery in Traditional Distributed System
77(1)
4.2.2 Service Match and Discovery in Distributed Manufacturing System
78(1)
4.3 Framework of Resource Service Match and Search in MGrid
79(3)
4.4 SMAs: Similarity Matching Algorithms (SMAs)
82(12)
4.4.1 Word Matching Algorithms (WMAs)
83(5)
4.4.2 Sentence Matching Algorithms (SeMAs)
88(1)
4.4.3 Number Matching Algorithms (NMAs)
89(4)
4.4.4 Entity Class Matching Algorithms (ECMAs)
93(1)
4.5 RS-Matcher: Resource Service Matcher
94(10)
4.5.1 Basic-matching
97(1)
4.5.2 I/O-matching
98(2)
4.5.3 QoS-matching
100(1)
4.5.4 Integrated-matching
101(3)
4.6 Case Study
104(5)
4.6.1 Step 1: Basic-matching
105(1)
4.6.2 Step 2: I/O-matching
106(2)
4.6.3 Step 3: QoS-matching
108(1)
4.6.4 Step 4: Integrated-matching
108(1)
4.7 Performance Results and Discussion
109(3)
4.7.1 Accuracy
109(2)
4.7.2 Efficiency
111(1)
4.8 Summary
112(3)
5 Resource Service QoS Modeling and Evaluation
115(28)
5.1 Introduction
115(1)
5.2 Related Works
116(2)
5.3 Evaluation Indices System of MGrid Resource Service
118(2)
5.4 Evaluation of SEIs and IEIs
120(10)
5.4.1 Evaluation Framework of SEIs and IEIs
120(4)
5.4.2 Structure Model of Agent
124(3)
5.4.3 Evaluation Process of SEIs and IEIs
127(1)
5.4.3.1 Setting of Manufacturing Resources Evaluation Indexes Set U
127(1)
5.4.3.2 Setting the Evaluation Grade Values Set P
128(1)
5.4.3.3 Establishment of the Comprehensive Information Matrix R
128(1)
5.3.3.4 Establishment of the Weight Coefficients Set Q
129(1)
5.4.3.5 Establishment of the Comprehensive Evaluation Matrix M
129(1)
5.4.3.6 Establishment of the Increased Weight Set Matrix B
129(1)
5.4.3.7 The Result of Comprehensive Evaluation Quantitative Value V Based on Matrix M and Matrix B
129(1)
5.5 Classification and Modeling of MGrid QoS
130(3)
5.5.1 QoS Modeling from the Whole-lifecycle Management of MGrid Resource Service QoS
130(1)
5.5.2 QoS Modeling from MGrid Architecture Views
131(1)
5.5.3 MGrid QoS Attribute Parameters Modeling
132(1)
5.6 Evaluation of MGrid QoS Attribute Parameter
133(5)
5.6.1 Time
133(3)
5.6.2 Cost
136(1)
5.6.3 Reliability
136(1)
5.6.4 Maintainability
136(1)
5.6.5 Trust-QoS
137(1)
5.6.6 Function Similarity
137(1)
5.7 Application Case: QoS-based MGrid Resource Service Management
138(3)
5.8 Summary
141(2)
6 Resource Service Trust-QoS Evaluation
143(42)
6.1 Introduction
143(3)
6.2 Related Works
146(5)
6.2.1 MGrid Resource Scheduling Based on QoS
146(1)
6.2.2 QoS and Trust-QoS
147(4)
6.3 Resource Management and Trust Relationship Management in MGrid
151(2)
6.4 MGrid Resource Service Trust-QoS Relationship Model
153(3)
6.5 MGrid Resource Service Trust-QoS Evaluation Model
156(6)
6.5.1 Related Conceptions and Definitions
156(5)
6.5.2 Intra-domain Trust-QoS Evaluation Model of Resource Service
161(1)
6.5.3 Inter-domain Trust-QoS Evaluation Model of Resources Service
161(1)
6.6 Data Structure Design
162(4)
6.7 Trust-QoS Evaluating and Updating Algorithms
166(4)
6.7.1 Direct Trust-QoS Evaluating Algorithm
166(1)
6.7.2 Recommended Trust-QoS Evaluating Algorithm
166(3)
6.7.3 Comprehensive Combining Recommended Trust-QoS Evaluating Algorithm
169(1)
6.8 Real-time and Dynamical Updating Algorithm of Trust-QoS Degree
170(1)
6.9 Trust-QoS Evaluation Case Study
171(4)
6.10 Application Case: Trust-QoS Based MGrid Resource Service Scheduling
175(9)
6.10.1 Requirements of Trust-QoS Based MGrid Resource Service Scheduling
175(1)
6.10.2 Trust-QoS Based MGrid Resource Service Scheduling Framework
176(4)
6.10.3 Trust-QoS Based MGrid Resource Service Scheduling Algorithms
180(4)
6.11 Summary
184(1)
7 Resource Service Optimal-selection and Composition Framework
185(30)
7.1 Introduction
185(3)
7.2 MGrid-RSOSCF: MGrid Resource Service Optimal-selection and Composition
188(3)
7.2.1 Architecture of MGrid-RSOSCF
188(2)
7.2.2 Life-cycle of MGrid Resource Service Composition
190(1)
7.3 T-Layer: Task Layer
191(5)
7.3.1 Basic Models of MGrid Task
191(4)
7.3.2 Main Functions and Services in T-Layer
195(1)
7.4 S-Layer: Resource Service Match and Search Layer
196(1)
7.5 Q-Layer: Resource Service QoS Synthetically Processing Layer
196(5)
7.5.1 Main Functions and Services in Q-Layer
197(1)
7.5.2 QoS Extraction
197(3)
7.5.3 QoS Comparison
200(1)
7.6 O-Layer: Resource Service Optimal-selection Layer
201(3)
7.6.1 Simple Resource Service Optimal-selection Method
201(3)
7.6.2 The Other Resource Service Optimal-selection Methods
204(1)
7.7 C-Layer: Resource Service Composition Layer
204(8)
7.7.1 RSC-Engine: Resource Service Composition Engine
204(3)
7.7.2 RSCEP-Generator
207(1)
7.7.3 RSCEP-Selector
208(2)
7.7.4 RSCE-Controller: Resource Service Composition Executing Controller
210(1)
7.7.5 RSC-Monitor: Resource Service Composition Monitor
210(1)
7.7.6 RSC-Coordinator: Resource Service Composition Coordinator
211(1)
7.8 Summary
212(3)
8 Resource Service Optimal-selection Based on Intuitionistic Fuzzy Set and Non-functionality QoS
215(38)
8.1 Introduction
215(5)
8.2 Framework of Resource Service Selection
220(2)
8.3 Resource Service Optimal-selection Based on IFS in MGrid
222(11)
8.3.1 Symbols and Notations
222(1)
8.3.2 Preliminaries on Intuitionistic Fuzzy Set (IFS)
223(2)
8.3.3 RSOS Based on IFS
225(1)
8.3.3.1 Evaluating cj of RSm at Time Periods tk
225(1)
8.3.3.2 Calculating the Synthetic Evaluation of Cj of RSm form t0 to tc
226(2)
8.3.3.3 Determining the Weights of QoS Criteria
228(2)
8.3.3.4 Evaluating Fuzzy Synthetic of Each CRS
230(1)
8.3.3.5 Calculating the Closeness Coefficient of Each CRS
231(1)
8.3.3.6 Ranking the Order of Candidate Resource Services (CRSs)
232(1)
8.3.4 Data Structure Design
232(1)
8.4 Case Study
233(13)
8.4.1 Step 1: Extracting the Related Data of RS1, RS2, RS3, RS4, and RS5
233(5)
8.4.2 Step 2: Evaluating the QoS Criteria of Each Candidate Resource Service at Different Time Periods
238(1)
8.4.3 Step 3: Calculating the Synthetic QoS Criteria of Each Candidate Resource Service
238(1)
8.4.4 Step 4: Determining the Weights of Each QoS Criterion
238(1)
8.4.5 Step 5: Calculating the Closeness Coefficient of Each CRS
238(7)
8.4.6 Step 6: Ranking the Order of CRSs
245(1)
8.5 Performance Analysis and Discussion
246(5)
8.5.1 Scalability and Efficiency
246(1)
8.5.2 Effectiveness
246(2)
8.5.3 Comparison with the Method of Bedi (Bedi et al., 2007)
248(3)
8.6 Summary
251(2)
9 Correlation Relationship Management in Resource Services Composition
253(22)
9.1 Introduction
253(1)
9.2 Related Works
254(1)
9.3 Motivation
255(3)
9.4 Correlation Relationship among Resource Services
258(7)
9.4.1 Combinable Correlation
258(1)
9.4.1.1 Definition and Model
258(1)
9.4.1.2 Combinable Correlation Degree
259(4)
9.4.2 Business Entity Correlation
263(1)
9.4.3 Statistical Cooperate Correlation
264(1)
9.5 QoS Computation Model of Correlation-aware Resource Services Composition
265(3)
9.6 Case Study: Correlation-aware Resource Services Composition
268(5)
9.7 Summary
273(2)
10 Resource Service Composition Optimal-selection
275(36)
10.1 Introduction
275(4)
10.2 Problem Formulation and Review
279(14)
10.2.1 Motivating Example
279(2)
10.2.2 Basic Models of Resource Service Composition and their QoS Evaluation
281(1)
10.2.2.1 QoS Properties and QoS Description Mode Supporting Service Correlation
281(3)
10.2.2.2 Basic Composite Models of CRS and their QoS Evaluation
284(2)
10.2.3 Formulation of MO-MRSCOS Problem
286(4)
10.2.4 Pareto Solution of MO-MRSCOS
290(3)
10.3 Review of Standard PSO
293(2)
10.4 Improved PSO for MO-MRSCOS Problem
295(10)
10.4.1 Representation
295(1)
10.4.2 Particle Movement (update)
296(1)
10.4.2.1 Selection of Inertia Weight
297(1)
10.4.2.2 Selection of Acceleration Coefficients
298(1)
10.4.2.3 Biggest Personal Position Value
299(1)
10.4.2.4 Biggest Velocity Value
299(1)
10.4.3 Generation of Personal Best (pbest)
300(1)
10.4.4 Generation of Global Best (gbest)
300(3)
10.4.5 Population Trimming in PSO
303(1)
10.4.6 The Procedures of the Proposed PSO Algorithm for MO-MRSCOS Problem
304(1)
10.5 Performance Analysis and Discussion
305(4)
10.6 Summary
309(2)
11 Resource Services Composition Flexibility
311(46)
11.1 Introduction
311(2)
11.2 Related Works
313(2)
11.3 The Analysis, Definition and Classification of RSC Flexibility
315(7)
11.3.1 The Factors Influencing RSC
315(2)
11.3.2 The Definition and Classification of RSC Flexibility
317(5)
11.4 The Measurement of RSC Flexibility
322(24)
11.4.1 Task Flexibility
323(4)
11.4.2 Resource Service Flexibility
327(3)
11.4.3 QoS Flexibility
330(5)
11.4.4 Correlation Flexibility
335(6)
11.4.5 Flow Flexibility
341(5)
11.5 Case Study and Experiment Results
346(9)
11.5.1 The Change of Tasks
347(1)
11.5.2 The Change of Resource Services
348(2)
11.5.3 The Change of QoS of Resource Services
350(2)
11.5.4 The Change of Correlations among Resource Services
352(1)
11.5.5 The Change of Flows of RSC
353(2)
11.6 Summary
355(2)
12 Resource Services Composition Network
357(46)
12.1 Introduction
357(5)
12.2 Scale-free Network (SFN)
362(2)
12.3 The Theoretical Hypothesis: Composition Service Network is a Scale-free Network
364(1)
12.4 Concepts and Definitions in CoRCS-Net
365(12)
12.4.1 Web Service, Composition Service, and Composition Service Network
365(3)
12.4.2 Combinable Relationship (CoR)
368(5)
12.4.3 Decision Rule of Combinable Relationship
373(1)
12.4.4 Combinable Strength
374(1)
12.4.5 Definition and Establishment of CoRCS-Net
375(2)
12.5 The Evolving Behavior of CoRCS-Net
377(15)
12.5.1 The Elementary Evolving Operators of CoRCS-Net
377(2)
12.5.2 The Influence of the Evolving Operators on the Combinable Strength in CoRCS-Net
379(13)
12.6 Theoretical Proof of the Scale-free Characteristics of CoRCS-Net
392(7)
12.6.1 The Variation Rate of Combinable Strength for Each Service Vertexes in the CoRCS-Net
392(4)
12.6.2 The Proof of Scale-free Characteristic of CoRCS-Net
396(3)
12.6.3 Significance
399(1)
12.7 Summary
399(4)
13 Failure Detection and Recovery in Resource Service Optimal-Allocation
403(32)
13.1 Introduction
403(1)
13.2 Related Works
404(3)
13.2.1 Failure Management in Manufacturing System
404(2)
13.2.2 Failure Management in Distributed System
406(1)
13.3 Define and Classification of MGrid Failure
407(6)
13.3.1 Virtual Link Related Failure
408(1)
13.3.2 Resource Service Related Failure
408(3)
13.3.3 Task Related Failure
411(1)
13.3.4 Application Related Failure
412(1)
13.4 Architecture of MGrid Failure Management System
413(2)
13.5 Detection of MGrid Failure
415(7)
13.5.1 Detection of Virtual Link Related Failures
415(1)
13.5.2 Detection of Resource Service Related Failures
416(3)
13.5.3 Detection of Task Related Failures
419(2)
13.5.4 Detection of Application Related Failures
421(1)
13.6 MGrid Failure Recovery Based on ECA Rules
422(7)
13.7 Implementation and Simulation
429(5)
13.7.1 Comparison of Success Rate
430(1)
13.7.2 Comparison of Execution Time
431(3)
13.8 Conclusion
434(1)
14 Summary of the Application of Grid Technology in Manufacturing
435(34)
14.1 Introduction
435(1)
14.2 Review of MGrid Theories
435(21)
14.2.1 Architecture of MGrid
445(2)
14.2.2 Resource Management System (RMS)
447(1)
14.2.3 Modeling and Encapsulating of MGrid Resource Services
448(1)
14.2.4 Resource Service Discovery and Scheduling
449(1)
14.2.5 QoS Management
450(1)
14.2.6 Service Composition
451(1)
14.2.7 Workflow Management
452(1)
14.2.8 Job Management
453(1)
14.2.9 Reliability Management
454(1)
14.2.10 Security and Trust Management
455(1)
14.2.11 Others
456(1)
14.3 Investigation of Application Research on MGrid
456(7)
14.4 Key Future Research Issues
463(4)
14.4.1 Basic Protocols, Standards, and Criteria
464(1)
14.4.2 Service-oriented MGrid Architecture with Semantic Support
464(1)
14.4.3 MGrid Ontology
464(1)
14.4.4 Quantitative Research on MGrid Resource Service
464(1)
14.4.5 Resource Service Composition and its Optimal-selection
465(1)
14.4.6 Cost and Price Management, and Electronic Payment of MGrid
465(1)
14.4.7 Performance Measurement and Evaluation of an MGrid System
466(1)
14.4.8 Commercial Operation Model of MGrid
466(1)
14.4.9 Embedded Connect Technology for Physical Equipment
467(1)
14.5 Summary
467(2)
15 Cloud Manufacturing: Development and Commerce Realization of MGrid
469(14)
15.1 Introduction
469(1)
15.2 Concept and Architecture of Cloud Manufacturing
470(4)
15.2.1 Concept of Cloud Manufacturing
470(2)
15.2.2 Architecture of Cloud Manufacturing
472(2)
15.3 Core Enabling Technologies for Cloud Manufacturing
474(1)
15.4 Typical Characteristics of Cloud Manufacturing
475(1)
15.5 Difference and Relationship between Cloud Computing and Cloud Manufacturing
476(1)
15.6 Classification of Cloud Manufacturing Service Platform
477(2)
15.7 Key Technologies and Main Research Contents of Cloud Manufacturing
479(1)
15.8 Key Advantages and Challenges of Cloud Manufacturing
479(2)
15.9 Summary
481(2)
Bibliography 483(26)
Index 509
Fei Tao is currently an associate professor at the Beihang University of Aeronautics and Astronautics. He obtained his PhD in mechanical engineering from Wuhan University of Technology, China, in 2008. He was elected a research affiliate of CIRP, The International Academy for Production Engineering, in 2009. Dr. Tao is the founder and editor-in-chief of International Journal of Service and Computing Oriented Manufacturing.

Lin Zhang received his PhD in 1992 from the Department of Automation at Tsinghua University, China. He is a full professor at Beihang University, the Vice President of Chinese Association for System Simulation, and a member of the council of Chinese Association for Artificial Intelligence.

Yefa Hu received his PhD in mechanical design and theory from Wuhan University of Technology (WHUT) in 2001. He is a full professor at the School of Mechanical and Electronic Engineering at WHUT. He has published two books on "Magnetic Suspension Technology" and "Manufacturing Grid," as well as more than sixty refereed international and domestic papers.
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