Formal and Operational Study of C-DEVS

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ABSTRACT


C-DEVS is a formalism for modeling and analysis of discrete event systems. It refers to the original formalism defined by Zeigler in 1976. While the simulation algorithms are well defined, their implementation is a challenge due to both correctness and efficiency issues. This work aims at studying the formalism and its operational semantics. We review and validate the meta-model for SimStudio - a Java implementation of the DEVS simulation protocol, and we debug its existing Java codes. We finally use formal methods to perform model checking and theorem proving on the C-DEVS simulation system to assess the properties of correctness.


TABLE OF CONTENTS

Abstract

Chapter 1: Introduction
1.1       DEVS formalism and its variants
1.2       Formal analysis of DEVS simulation protocol
1.3       Structure

Chapter2: Discrete Even System Specification (DEVS)
2.1. DEVS (CDEVS and PDEVS with differences)
2.2 CDEVS
2.2.1 Classic DEVS (CDEVS) Atomic Model
2.2.2    Classic DEVS Coupled Model
2.3 Examples of CDEVS model and Simulation
2.4.The CDEVS Simulation Algorithm
2.5. Example of CDEVS model and simulation (by hand)
2.5.1 Simulation by hand
2.5.2 Simulation result Snapshot

Chapter 3: Literature Review on PDEVS Implementations
3.1 DEVS WITH SIMULTANEOUS EVENTS (PARALLEL DEVS)
3.1.1 PDEVS Atomic Model
3.1.2. PDEVS Coupled Model
3.2 Survey of DEVS Implementations (with examples)
3.2.1 Simstudio implementation (meta – models)
3.2.2 Comparison of approaches
3.2.3 Problems with existing implementations

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

Chapter 5: Simstudio and Formal Methods
5.1 Improvements on Simstudio
5.2. Towards integration of formal analysis with Simstudio
5.2.1 Formal Method: The B Method
5.2.2 UML to B
5.2.3 Formal specification of Simstudio
5.3 Use of formal tools with Simstudio: The Atelier B
5.4 Results and Discussions

Chapter6: Conclusions
Challenges
Future work
REFERENCES


Chapter 1.             Introduction

Many natural systems in physics, astrophysics, chemistry and biology, and human systems in economics, psychology, social science and engineering have long relied on the unsuitable methods for their study during the twentieth century. Traditional mathematical methods such as differential equations have been used for centuries as the main tools for analysis, comprehension, design, and prediction for complex systems in varied areas.

The emergence of computers simulation provided alternative methods of analysis for both natural and artificial systems. Since the early days of computing, users translated their analytical methods into computer-based simulation to solve with a level of complexity unknown in earlier stages of scientific development.

Computational methods based on differential equations could not be easily applied in studying human-made dynamic systems such as traffic controllers, robotic arms, automated factories, production plants, and computer networks and so on. These systems are usually referred to as discrete-event systems because their states do not change continuously but, rather, because of the occurrence of events. This makes them asynchronous, inherently concurrent, and highly nonlinear, rendering their modeling and simulation different from that used in traditional approaches [1].

A number of techniques were introduced in order to improve the model definition for this class of systems, Petri Nets, Finite state Machines, min-max algebra, Timed Automata, and others [1].

Let us define the following key words.

Model: A model is a simplified representation of a system at some particular point in time or space intended to promote understand of the real system.

System: A system exits and operates in time and space.

Simulation: A simulation is the manipulation of a model in such a way that it operates on time or space to compress it, thus enabling one to perceive the interactions that would not otherwise be apparent because of their separation on time or space.


Modeling and Simulation (M&S) is the use of models, including emulators, prototypes, simulators, and either statically or over time, to develop data as a basis for making managerial or technical decisions. The primary motivation for modeling and simulation is risk reduction, that is, to ensure that the simulation can support its user/developer objectives acceptably. This is the primary benefit i n cost-benefit concerns about Verification and Validation (V & V), which is the core issue in the question of how much V & V is needed.

Modeling and simulation play increasingly important roles in modern life. It contributes to our understanding of how things function and are essential to the effective and efficient design, evaluation, and operation of new products and systems. Modeling and simulation results provide vital information for decisions and actions in many areas of business and government. Verification and validation are processe s that help to ensure that models and simulations are correct and reliable. Although significant advances in V&V have occurred in the past 15 years, significant challenge s remain that impede the full potential of modeling and simulation made possible by advances in computers and software.

1.1     DEVS formalism and its variants

DEVS (Discrete Event System Specification) defined by Zeigler in the 1970’s is one of the existing theories of Modeling and Simulation allowing the modular description of discrete event models. It finds a way to specify systems whose states change either upon the reception of an input event or due to the expiration of a time delay. In order to attack the complexity of the system under study, the model is organized hierarchically (i.e., it is organized in a way such that every element is higher than its precedent), and higher-level components of the system are decomposed into simpler elements.....


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