This book explores the world of reconfigurable stochastic Petri nets (RSPNs), a powerful method for modeling and verifying complex, dynamic and reconfigurable systems. As modern discrete-event systems become increasingly flexible, requiring structural adaptability at runtime, classical Petri nets are proving insufficient. This book presents innovative extensions to Petri nets, offering enhanced modeling capabilities for reconfigurable systems, while ensuring efficient verification. Through a structured approach, this book introduces reconfigurable generalized stochastic Petri nets (RecGSPNs),…mehr
This book explores the world of reconfigurable stochastic Petri nets (RSPNs), a powerful method for modeling and verifying complex, dynamic and reconfigurable systems. As modern discrete-event systems become increasingly flexible, requiring structural adaptability at runtime, classical Petri nets are proving insufficient. This book presents innovative extensions to Petri nets, offering enhanced modeling capabilities for reconfigurable systems, while ensuring efficient verification. Through a structured approach, this book introduces reconfigurable generalized stochastic Petri nets (RecGSPNs), an advanced framework that integrates reconfigurability while preserving crucial system properties such as liveness, boundedness and deadlock-freedom. This book systematically explores modeling techniques, including stochastic reward nets and dynamic topology transformations, demonstrating their effectiveness through quantitative and qualitative analyses. By addressing challenges in state-space explosion and computational complexity, this book provides essential methodologies for researchers and practitioners working on reconfigurable systems, and serves as a valuable resource for those working in network security, manufacturing systems and distributed computing, where dynamic reconfigurations are essential.
Samir Tigane is Associate Professor at the University of Biskra and researcher at the Laboratoire de l'Informatique Intelligente (LINFI), Algeria. His research includes software engineering, formal methods and artificial intelligence. Laid Kahloul is Professor at the University of Biskra and researcher at the Laboratoire de l'Informatique Intelligente (LINFI), Algeria. His research includes software engineering, formal methods, security and artificial intelligence. Abdelhamid Mellouk is Full-time University Professor, Director of the IT4H High School Engineering Department and Head of the TincNET Research Team, UPEC, France. He is also the founder of Network Control Research and Curricula activities at UPEC, President of the Policies and Programs commission at the National Council for Scientific Research and Technologies, a HCERES Expert, a CNU member and Co-President of the DS-AI Systematic Deep Tech Hub.
Inhaltsangabe
Preface ix General Introduction xi Part 1. State of the Art 1 Chapter 1. From Petri Nets to Stochastic Petri Nets 3 1.1. Introduction 3 1.2. Modeling with Petri nets 4 1.3. Petri net structure 6 1.4. Dynamic behavior of Petri nets 7 1.5. Petri net analysis 11 1.5.1. Petri net properties 11 1.5.2. Temporal logic 14 1.5.3. Analysis methods 18 1.6. Stochastic Petri nets 19 1.6.1. Stochastic process 21 1.6.2. Markov process 21 1.6.3. Stochastic Petri nets having exponential law 22 1.6.4. Quantitative properties 26 1.7. Generalized stochastic Petri nets 27 1.7.1. Embedded Markov Chain 29 1.8. Conclusion 32 Chapter 2. Reconfiguration Aspects in Petri Nets 33 2.1. Introduction 33 2.2. Graph Transformation Systems 34 2.3. Double-pushout approach for Petri nets 35 2.4. Net rewriting systems 38 2.5. Self-modifying nets 41 2.6. Reconfigurable Petri nets 43 2.7. Improved net rewriting systems 47 2.8. Other extensions 48 2.9. Trade-off between expressiveness and calculability in PN-based reconfigurable formalisms 49 2.10.Conclusion 51 Part 2. Orientation 1 53 Chapter 3. Rewritable Topology in Generalized Stochastic Petri Nets 55 3.1. Introduction 55 3.2. GSPNs with rewritable topology 56 3.2.1. Formal definition 58 3.3. Proofs 60 3.4. Illustrative example 61 3.5. Stochastic reward nets 67 3.6. Configuration-dependent stochastic reward nets 68 3.7. Transformation of CD-SRNs into basic SRNs 69 3.8. Proofs 72 3.9. Illustrative example 73 3.10.Conclusion 79 Chapter 4. Generalized Stochastic Petri Nets with Dynamic Structure 81 4.1. Introduction 81 4.2. Dynamic GSPNs 83 4.2.1. Formal definition 83 4.3. D-GSPN transformation towards GSPNs 85 4.4. Qualitative/quantitative analysis of D-GSPNs 89 4.5. Proofs 90 4.6. Illustrative example 92 4.7. Generalized stochastic Petri nets with inhibitor and reset arcs 98 4.8. Improved D-GSPNs under infinite-server semantics 99 4.9. Unfolding ID-GSPNs into GSPNs 105 4.10. Running examples 110 4.11. Conclusion 119 Part 3. Orientation 2 121 Chapter 5. Reconfigurable Generalized Stochastic Petri Nets 123 5.1. Introduction 123 5.2. Reconfigurable generalized stochastic Petri nets 125 5.2.1. Definition of RecGSPNs 125 5.2.2. Properties preserving nets 130 5.3. Preservation of properties in RecGSPNs 131 5.3.1. Preservation of LBR, home state and deadlock-free 131 5.3.2. Preservation of linear temporal properties 136 5.4. Quantitative analysis 141 5.5. Using RecGSPNs in practice 144 5.6. Conclusion 149 Part 4. Evaluation, Discussion and Conclusion 151 Chapter 6. Evaluation and Discussion 153 6.1. Introduction 153 6.2. Qualitative aspects 153 6.3. Quantitative aspects 156 6.3.1. Factor 1: model size 157 6.3.2. Factor 2: Markov chain spatial complexity 159 6.3.3. Factor 3: Markov chain time complexity 161 6.4. Conclusion 163 Conclusion 165 References 169 Index 177
