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Discrete Event System Specification (DEVS) is a sound formal modeling and simulation (M&S) structure based on generic dynamic system concepts. PDEVS (Parallel Discrete Event System Specification) is a well-known formalism for the specification of complex concurrent systems organized as an interconnection of atomic and coupled interacting components. The abstract simulator of a PDEVS model is normally founded on the assumption of maximal parallelism: multiple components are allowed to undertake at the same time an independent state transition. Our work is to study PDEVS formalism, its operational semantics through various implementation strategies, the cleaning of the thread-less and the threaded implementations proposed in the PDEVS simulation engine, benchmarking of the two implementations and formal analysis of the simulation protocol.

Keywords: Discrete Event System Specification, Modelling and Simulation, Formal Analysis


Table of Contents
List of Figures

Chapter 1: Introduction
1.1       Introduction to Modeling and Simulation
1.1.1    Modeling and Simulation Concepts
1.1.2    Modeling and Simulation Benefits
1.1.3    Modeling and Simulation Importance
1.1.4    Modeling and Simulation Challenges
1.2       Introduction to DEVS formalism and its variants
1.3       The need for formal analysis of DEVS simulation protocol
1.4       Structure of thesis report

Chapter 2: Discrete Event System Specification (DEVS)
2.1       Discrete Event System Specification (DEVS)
2.2       Classic DEVS (CDEVS)
2.3       Example of CDEVS model
2.4       Parallel DEVS (PDEVS)
2.5       The PDEVS Simulation Algorithm
2.6       Example of PDEVS model and simulation by hand

Chapter 3: Literature Review on PDEVS Implementations
3.1       Survey of PDEVS Implementations
3.2       SimStudio implementation (meta-models)
3.3       Other Implementation
3.4       Comparison of approaches
3.5       Problems with existing implementations

Chapter 4: Formal Methods
4.1       Introduction to Formal Methods Concepts, Approaches and Formalisms
4.2       Benefits of formal methods
4.3       Survey of tools and methods

Chapter 5: SimStudio and Formal Methods
5.1       SimStudio
5.2       Improvements on SimStudio (meta-models and discussions)
5.3       Towards integration of formal analysis with SimStudio
5.4       Use of formal tools with SimStudio
5.5       Results and Discussions

Chapter 6: Conclusions
6.1       Summary of work
6.2       Challenges
6.3       Future work


1.1        Introduction to Modeling and Simulation

A computer simulation is a computer program, or network of computers, that attempts to generate the behaviour of an abstract model of a particular system. Computer simulations have become a useful part of mathematical modeling of many natural systems in computational physics, astrophysics, chemistry and biology, human systems

in economics, psychology, social science, and engineering. Simulations can be used to explore and gain new insights into new technology, and to estimate the performance of systems too complex for analytical solutions.

Computer simulations vary from computer programs that run a few minutes, to network-based groups of computers running for hours, to ongoing simulations that run for days. The scale of events being simulated by computer simulations has far exceeded anything possible using the traditional paper-and-pencil mathematical modeling.

Modeling and simulation (M&S) is the use of models, including emulators, prototypes, simulators, and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The use of modeling and simulation (M&S) within engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included into the body of knowledge of engineering management. M&S has already helped to reduce costs and increase the quality of products and systems.

Modeling and Simulation is a discipline for developing a level of understanding of the interaction of the parts of a system, and of the system as a whole. The level of understanding which may be developed via this discipline is seldom achievable via any other discipline.

A system is understood to be an entity which maintains its existence through the interaction of its parts. A model is a simplified representation of the actual system intended to promote understanding. Whether a model is a good model or not depends on the extent to which it promotes understanding. Since all models are simplifications of reality there is always a trade-off as to what level of detail is included in the model. If too little detail is included in the model one runs the risk of missing relevant interactions and the resultant model does not promote understanding. If too much detail is included in the model, the model may become overly complicated and actually preclude the development of understanding. One simply cannot develop all models in the context of the entire universe.....

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