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Guide to Modeling and Simulation of Systems of Systems 2nd ed. 2017 [Kõva köide]

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  • Formaat: Hardback, 400 pages, kõrgus x laius: 235x155 mm, kaal: 8204 g, 141 Illustrations, color; 75 Illustrations, black and white; XIV, 400 p. 216 illus., 141 illus. in color., 1 Hardback
  • Sari: Simulation Foundations, Methods and Applications
  • Ilmumisaeg: 06-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319641336
  • ISBN-13: 9783319641331
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Guide to Modeling and Simulation of Systems of Systems 2nd ed. 2017Suurem pilt
  • Formaat: Hardback, 400 pages, kõrgus x laius: 235x155 mm, kaal: 8204 g, 141 Illustrations, color; 75 Illustrations, black and white; XIV, 400 p. 216 illus., 141 illus. in color., 1 Hardback
  • Sari: Simulation Foundations, Methods and Applications
  • Ilmumisaeg: 06-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319641336
  • ISBN-13: 9783319641331
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319641331.html

This easy-to-follow textbook provides an exercise-driven guide to the use of the Discrete Event Systems Specification (DEVS) simulation modeling formalism and the System Entity Structure (SES) simulation model ontology supported with the latest advances in software architecture and design principles, methods, and tools for building and testing virtual Systems of Systems (SoS). The book examines a wide variety of SoS problems, ranging from cloud computing systems to biological systems in agricultural food crops. This enhanced and expanded second edition also features a new chapter on DEVS support for Markov modeling and simulation.

Topics and features: provides an extensive set of exercises throughout the text to reinforce the concepts and encourage use of the tools, supported by introduction and summary sections; discusses how the SoS concept and supporting virtual build and test environments can overcome the limitations of current approaches; offers a step-by-step introduction to the DEVS concepts and modeling environment features required to build sophisticated SoS models; describes the capabilities and use of the tools CoSMoS/DEVS-Suite, Virtual Laboratory Environment, and MS4 Me™; reviews a range of diverse applications, from the development of new satellite design and launch technologies, to surveillance and control in animal epidemiology; examines software/hardware co-design for SoS, and activity concepts that bridge information-level requirements and energy consumption in the implementation; demonstrates how the DEVS formalism supports Markov modeling within an advanced modeling and simulation environment (NEW).

This accessible and hands-on textbook/reference provides invaluable practical guidance for graduate students interested in simulation software development and cyber-systems engineering design, as well as for practitioners in these, and related areas.



Systems of systems are at the root of this century’s global economic, climate, and energy challenges. This volume provides an approach that integrates both energy and information processing requirements into system design.

Part I: Basic Concepts
1 Modeling and Simulation of Systems of Systems
3(10)
1.1 Virtual Build and Test
5(1)
1.2 Modeling and Simulation Intrinsic to Virtual Build and Test
6(1)
1.3 Multi-disciplinary Collaboration Using Multi-formalism Modeling
7(2)
1.4 Background in the Literature
9(1)
1.5 Guide to Modeling and Simulation of Systems of Systems
10(3)
2 DEVS Integrated Development Environments
13(18)
2.1 The MS4 Me Is a Modeling and Simulation (M&S) Environment
13(7)
2.1.1 Introduction for the M&S User
14(2)
2.1.2 Introduction for the M&S Developer
16(4)
2.2 Introduction for the M&S Professional
20(3)
2.2.1 System Structure and Behavior
20(1)
2.2.2 Finite Deterministic DEVS (FDDEVS)
21(1)
2.2.3 System Entity Structure (SES)
21(2)
2.3 Jazz Band Continued
23(3)
2.4 Summary
26(3)
References
29(2)
3 System Entity Structure Basics
31(12)
3.1 Modeling and Simulation as a Simple Workflow
32(1)
3.2 Decomposition and Coupling
33(3)
3.3 Hierarchical Construction
36(6)
3.4 Summary
42(1)
4 DEVS Natural Language Models and Elaborations
43(28)
4.1 FDDEVS Model for Generating Jobs in a Time Sequence
43(3)
4.2 FDDEVS Model for Processing Jobs
46(2)
4.3 A Simple Workflow Coupled Model
48(1)
4.4 Elaborating FDDEVS into Fully Capable Models in Java
49(6)
4.5 Elaborating ProcessorOfJobs into a Java Model
55(1)
4.6 Transducer: Model to Measure Job Completion Time and Throughput
56(4)
4.7 Using Elaboration to Handle Non-deterministic State Transitions
60(2)
4.8 Using Elaboration to Handle Multiple Simultaneous Inputs
62(1)
4.9 Using Elaboration to Generate Multiple Simultaneous Outputs
63(1)
4.10 Model Development Accelerated by the Sequence Diagram
64(4)
4.11 Summary 67 Appendix: Transducer FDDEVS File (Transducer.dnl)
68(3)
5 Specialization and Pruning
71(10)
5.1 Specializations
71(1)
5.2 Pruning of Specializations
72(1)
5.3 Multiple Occurrences of Specializations
73(3)
5.4 Rules for Adding Specializations: There Are None
76(2)
5.4.1 Specialization Under Root Entity
76(1)
5.4.2 Specialization Under Entity Under Aspect
76(1)
5.4.3 Specialization Under Entity Under Specialization
76(1)
5.4.4 Specialization Under Entity Besides Another Specialization
77(1)
5.5 Variables and Specializations
78(1)
5.6 Summary
79(2)
6 Aspects and Multi-aspects
81(2)
6.1 Multiple Aspects (Decompositions)
81(2)
6.1.1 Expressing Different Aspects for Same Entity
81(1)
6.1.2 Pruning of Aspects
82(1)
6.1.3 Aspects: Perspectives and Abstractions
83(16)
6.2 Multi-aspects-Multiple Related Decompositions of an Entity
84(13)
6.2.1 Limitations of Aspects
84(1)
6.2.2 Multi-aspect Restructuring
85(1)
6.2.3 Pruning Multi-aspects
86(1)
6.2.4 Multi-aspect Uniform Coupling
87(2)
6.2.5 One-to-All and All-to-One Coupling
89(1)
6.2.6 Hierarchical Construction with Multi-aspects
90(3)
6.2.7 Uniform Pairwise Coupling
93(2)
6.2.8 Predefined Coupling Specifications
95(2)
6.3 Summary
97(1)
References
97(2)
7 Managing Inheritance in Pruning
99(8)
7.1 Creating Instances with Underscore
99(1)
7.2 Specifying the Base Class for Inheritance
100(1)
7.3 Configuring the Base Class
100(2)
7.4 Inheritance in Pruning
102(1)
7.5 Specifying Inheritance from a Child
103(2)
7.6 Summary
105(2)
8 Automated and Rule-Based Pruning and Experimental Execution
107(18)
8.1 Automated Pruning
107(2)
8.1.1 Enumerative Pruning
108(1)
8.1.2 Random Pruning
109(1)
8.2 Context-Free and Context-Sensitive Pruning
109(7)
8.2.1 Pruning Algorithm for Context-Sensitive Selection
111(2)
8.2.2 Conditional Rule-Based Pruning
113(1)
8.2.3 The Unless or if-not Conditional Rule
114(1)
8.2.4 Example: Time-Critical Modeling and Simulation
115(1)
8.2.5 Random Selection from Choices Remaining
116(1)
8.3 Executive Control of Experimentation
116(5)
8.3.1 First Level Control of Simulation
117(2)
8.3.2 Second Level of Control with the SES
119(1)
8.3.3 Third Level of Control
120(1)
8.4 Summary
121(1)
References
121(4)
Part II: Advanced Concepts
9 DEVS Simulation Protocol
125(22)
9.1 DEVS Simulation Protocol
126(2)
9.2 MS4 Me Exposition of the DEVS Simulation Protocol
128(5)
9.2.1 Interface Objects
130(1)
9.2.2 Input and Output Ports
130(1)
9.2.3 FDDEVS Specifications
131(2)
9.3 Distributed Simulation Implementations of the DEVS Protocol
133(6)
9.3.1 Standard DEVS Protocol
134(1)
9.3.2 Peer Message Exchanging Implementation
135(2)
9.3.3 Real-Time Message Exchanging Implementation
137(2)
9.4 DEVS Protocol as a Standard for Simulation Interoperability
139(2)
9.4.1 DEVS Protocol with Event-Scheduling Simulator
139(2)
9.4.2 Lessons for Simulation Interoperability
141(1)
9.5 Summary
141(5)
References
146(1)
10 Dynamic Structure: Agent Modeling and Publish/Subscribe
147(22)
10.1 Dynamic Structure and Agent Modeling
147(3)
10.2 Publish/Subscribe Data Distribution
150(5)
10.2.1 Publisher
151(1)
10.2.2 Subscriber
152(1)
10.2.3 PublishSubscribeRouter
153(1)
10.2.4 Publish Subscribe Operation
154(1)
10.3 Data Distribution Service
155(12)
10.3.1 DEVS Simulation Protocols in DDS
157(1)
10.3.2 DEVS Messages
158(1)
10.3.3 Relating Ports and Topics
159(3)
10.3.4 Summary
162(5)
References
167(2)
11 Interest-Based Information Exchange: Mappings and Models
169(24)
11.1 Background
169(4)
11.1.1 Example: The Information Framework Applied to Car Purchases
171(2)
11.2 Application to Network Data Collection
173(4)
11.2.1 Network Traffic Data Representations
174(3)
11.3 Mapping Approach
177(4)
11.3.1 Mapping Multi-aspects
179(2)
11.4 DEVS Models that Exchange XML
181(9)
11.4.1 Model for Generating XML Documents
181(3)
11.4.2 DEVS Model for SES-Based XML Mapping
184(1)
11.4.3 Models to Distribute Mappings of Master SES to Interest-Based SESs
185(2)
11.4.4 Models that Exchange the Same XML
187(3)
11.5 Summary 190 Appendix: System Entity Structures for Examples
190(1)
References
191(2)
12 Languages for Constructing DEVS Models
193(16)
12.1 Constrained Natural Language Specification of Atomic Models
194(5)
12.1.1 Limitations of FDDEVS Models
196(1)
12.1.2 FDDEVS Enhancement Facility
196(2)
12.1.3 Development Advantages of the Enhancement Facility
198(1)
12.2 Constrained Natural Language Specifications of Hierarchical CoupledModels
199(2)
12.3 DEVS, UML, and EMF
201(1)
12.4 Summary 202 Appendix: Formal Definition of FDDEVS
202(3)
References
205(4)
Part III: Applications
13 Flexible Modeling Support Environments
209(28)
13.1 Supporting Multiple Paths Through Development Process
209(5)
13.2 M&S Tools as Services in a Service-Oriented Architecture
214(1)
13.3 Case Study: Fractionated Satellite Systems
215(10)
13.3.1 How the MSE Adapts to Types of Stakeholders
216(3)
13.3.2 System Entity Structure (SES): Key Support for MSE Flexibility
219(1)
13.3.3 MSE Implementation: Service-Oriented Architecture
219(2)
13.3.4 MSE Simulation Service
221(2)
13.3.5 Simulation Using Web Services
223(2)
13.4 MSE in Operation: An Example Thread
225(9)
13.5 Summary 229
Appendix 1: GeneralClusterArchSeS.txt 231
Appendix 2: Outline of GeneralClusterSeS 233
Appendix 3: GeneralClusterArchBasicPrune.pes 233
Appendix 4: GeneralClusterArchMonolithicPrune.pes
234(1)
References
235(2)
14 Service-Based Software Systems
237(28)
14.1 Introduction
237(1)
14.2 Service-Based Software Systems
238(2)
14.3 Service-Oriented Architecture
240(1)
14.4 SOA-DEVS Simulation Modeling
241(3)
14.4.1 Primitive Models
242(1)
14.4.2 Composite Models
243(1)
14.5 SOA-DEVS Model Components
244(6)
14.5.1 Generic Messages
245(1)
14.5.2 Primitive Service Models
246(3)
14.5.3 Composite Service Model
249(1)
14.6 Exemplar Simulation Model
250(5)
14.7 Dynamic Structure SOAD
255(7)
14.7.1 Broker-Executive Model Design
257(2)
14.7.2 Flat and Hierarchical Model Compositions
259(3)
14.8 Summary
262(1)
14.9 Exercises
263(1)
References
264(1)
15 Cloud System Simulation Modeling
265(30)
15.1 Introduction
265(1)
15.2 Software/Hardware Co-Design
266(2)
15.3 SOC-DEVS SW/HVV Modeling
268(17)
15.3.1 Software Service System Model
268(10)
15.3.2 Hardware System Model
278(4)
15.3.3 Service System Mapping
282(3)
15.4 Service-Oriented Voice Communication System
285(6)
15.4.1 Basic Measurements
287(1)
15.4.2 Simulation Parameter Estimation
288(1)
15.4.3 Experimentation Setup and Execution
289(1)
15.4.4 Example Simulation Model Results
289(2)
15.5 Summary
291(1)
References
292(3)
16 Model Development and Execution Process with Repositories, Validation, and Verification
295(30)
16.1 Introduction
295(2)
16.2 Unified Logical, Visual, and Persistent Modeling
297(11)
16.2.1 Simple Network Virus Model
298(1)
16.2.2 Template, Instance Template, and Instance Model Types
298(3)
16.2.3 Simulatable and Non-simulatable Model Types
301(1)
16.2.4 Logical Models
302(1)
16.2.5 Visual Models
303(2)
16.2.6 Complexity Metrics
305(1)
16.2.7 Persistence Models
305(1)
16.2.8 Model Namespaces
306(2)
16.3 CoSMoS Process Life Cycle
308(3)
16.4 Hybrid Software and Hardware Modeling in CoSMoS
311(6)
16.4.1 Hardware Models
313(1)
16.4.2 Software Models
314(1)
16.4.3 Composite Software/Hardware Mapping Models
314(1)
16.4.4 Model Constraints
315(2)
16.5 Guided Model Validation and Constrained Model Verification
317(6)
16.5.1 Model Validation
317(1)
16.5.2 Model Verification
318(1)
16.5.3 Constrained DEVS Model
319(1)
16.5.4 Verification Algorithm
320(1)
16.5.5 Dynamic Model Property Modeling
321(1)
16.5.6 Model Development and Verification in CoSMoS
322(1)
16.5.7 Summary
322(1)
References
323(2)
17 Modeling and Simulation of Living Systems as Systems of Systems
325(26)
17.1 Challenges for Living System Modeling and Simulation
326(1)
17.2 Why DEVS and VLE for Living System Modeling and Simulation?
327(4)
17.2.1 A Systemic Approach: Emergence and Scale Transfer
327(1)
17.2.2 Heterogeneous Formalisms and Living Systems Complexity
328(1)
17.2.3 VLE and the Experimental Plans
329(2)
17.3 Surveillance and Control in Animal Epidemiology
331(6)
17.3.1 Motivations and Objectives
331(1)
17.3.2 Model Description
332(1)
17.3.3 Simulation Results
333(4)
17.4 Plant Growth Modeling
337(9)
17.4.1 Motivations and Objectives
337(1)
17.4.2 The Ecomeristem Model
338(2)
17.4.3 Overall Functioning
340(1)
17.4.4 Topology
341(1)
17.4.5 Implementation in DEVS
342(2)
17.4.6 Validation
344(2)
17.4.7 Conclusion
346(1)
17.5 Model Continuity for Living Systems
346(1)
17.6 Summary
347(2)
References
349(2)
18 Activity-Based Implementations of Systems of Systems
351(20)
18.1 Energy and Activity
351(2)
18.2 Prototype System of Systems
353(2)
18.3 Experimental Frame and Timing Requirements
355(2)
18.4 Including Energy and Activity in SoS Models
357(1)
18.5 Review of Activity Concepts
358(2)
18.6 Timing Requirements, Energy, and Activity
360(2)
18.7 SoS Example: Forest Fire Fighting
362(3)
18.8 Relating Activity to Hardware-Implemented SoS
365(2)
18.9 Experimental Test
367(2)
18.10 Summary
369(1)
References
370(1)
19 DEVS Support for Markov Modeling and Simulation
371(24)
19.1 Introduction
371(1)
19.2 Markov Matrix Model
372(3)
19.2.1 Steady-State Probabilities
373(2)
19.3 CTM and DTM Models
375(2)
19.3.1 Implementation of CTM and DTM in MS4 Me
376(1)
19.4 Transient Behavior in Markov Models
377(3)
19.5 DEVS Features for Markov Modeling
380(2)
19.5.1 Individualizing Markov Models
380(1)
19.5.2 Dynamic Structure Markov Models
380(1)
19.5.3 Multi-aspects and Statistical Considerations
381(1)
19.6 Case Studies
382(8)
19.6.1 Markov Modeling of Populations with Multiple Distinguishing Characteristics
382(1)
19.6.2 Speedup in Multiprocessor Computation
383(7)
19.7 Summary
390(1)
Appendix 1: Exponential Distribution and Markov Model Basics
390(4)
References
394(1)
Index 395
Dr. Bernard P. Zeigler is Emeritus Professor of Electrical & Computer Engineering and Co-Director of the Arizona Center for Integrative Modeling and Simulation (ACIMS), at the University of Arizona, Tucson, USA. He is also Chief Scientist at RTSync, Rockville, MD, USA.

Dr. Hessam S. Sarjoughian is Associate Professor of Computer Science and Co-Director of ACIMS at Arizona State University, Tempe, USA.

The chapter Modeling and Simulation of Living Systems as Systems of Systems is contributed by Raphaël Duboz and Jean-Christophe Soulié of CIRAD (the Centre for International Cooperation in Agricultural Research for Development), France.