Netcentric System of Systems Engineering with DEVS Unified Process

Saurabh Mittal, José L. Risco Martín

• Publisher: CRC Press
• Year: 2018
• eBook ISBN: 9781351833745
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

In areas such as military, security, aerospace, and disaster management, the need for performance optimization and interoperability among heterogeneous systems is increasingly important. Model-driven engineering, a paradigm in which the model becomes the actual software, offers a promising approach toward systems of systems (SoS) engineering. However, model-driven engineering has largely been unachieved in complex dynamical systems and netcentric SoS, partly because modeling and simulation (M&S) frameworks are stove-piped and not designed for SoS composability. Addressing this gap, Netcentric System of Systems Engineering with DEVS Unified Process presents a methodology for realizing the model-driven engineering vision and netcentric SoS using DEVS Unified Process (DUNIP).

The authors draw on their experience with Discrete Event Systems Specification (DEVS) formalism, System Entity Structure (SES) theory, and applying model-driven engineering in the context of a netcentric SoS. They describe formal model-driven engineering methods for netcentric M&S using standards-based approaches to develop and test complex dynamic models with DUNIP. The book is organized into five sections:

• Section I introduces undergraduate students and novices to the world of DEVS. It covers systems and SoS M&S as well as DEVS formalism, software, modeling language, and DUNIP. It also assesses DUNIP with the requirements of the Department of Defense's (DoD) Open Unified Technical Framework (OpenUTF) for netcentric Test and Evaluation (T&E).
• Section II delves into M&S-based systems engineering for graduate students, advanced practitioners, and industry professionals. It provides methodologies to apply M&S principles to SoS design and reviews the development of executable architectures based on a framework such as the Department of Defense Architecture Framework (DoDAF). It also describes an approach for building netcentric knowledge-based contingency-driven systems.
• Section III guides graduate students, advanced DEVS users, and industry professionals who are interested in building DEVS virtual machines and netcentric SoS. It discusses modeling standardization, the deployment of models and simulators in a netcentric environment, event-driven architectures, and more.
• Section IV explores real-world case studies that realize many of the concepts defined in the previous chapters.
• Section V outlines the next steps and looks at how the modeling of netcentric complex adaptive systems can be attempted using DEVS concepts. It touches on the boundaries of DEVS formalism and the future work needed to utilize advanced concepts like weak and strong emergence, self-organization, scale-free systems, run-time modularity, and event interoperability.

This groundbreaking work details how DUNIP offers a well-structured, platform-independent methodology for the modeling and simulation of netcentric system of systems.

Section I

The Basics

1 Introduction to Systems Modeling and Simulation

1.1 THE NATURE OF SIMULATION

A simulation is an imitation of some real thing, state of affairs, or process in action. The act of simulating something generally entails representing certain key characteristics or dynamic behaviors of a selected physical or abstract system. Simulation is used in many contexts, including the modeling of natural systems or human systems to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training, and education. Simulation can also be used as a prediction tool to show the eventual real effects of alternative conditions and courses of action.

Key issues in developing a simulation technology include the acquisition of valid source information about the referent, selection of key characteristics and behaviors, use of simplifying approximations and assumptions within the simulation model, and fidelity and validity of the simulation outcomes.

The definitions taken from WordNet (2012) are as follows:

1.Simulation: The act of imitating the behavior of some situation or some process by means of something suitably analogous (especially for the purpose of study or personnel training).

2.Simulation, computer simulation(computer science): The technique of representing the real world by a computer program. “A simulation should imitate the internal processes and not merely the results of the thing being simulated.”

3.Model, simulation: Representation of something (sometimes on a smaller scale).

4.Pretense, pretence, pretending, simulation, feigning: The act of giving a false appearance. “His conformity was only pretending.”

This book is focused on the computer simulation concept, as per the highlighted definition above. A computer simulation attempts to simulate an abstract model (that is computationally represented) of a particular system. Computer simulations have become useful parts of mathematical modeling of many natural systems in physics, chemistry, biology, human systems, economics, psychology, and social science, and in the process of engineering new technology, to gain insights into the operation of those systems.

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 (or perhaps even imaginable) using the traditional paper-and-pencil mathematical modeling over 30 years ago. For example, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks, and other vehicles on simulated terrain around Kuwait using multiple supercomputers in the U.S. Department of Defense (DoD High Performance Computing Modernization Program, 1992). Another simulation ran a 1-billion-atom model, where previously a 2.64-million-atom model of a ribosome, in 2005, had been considered a massive computer simulation (Schwede & Peitsch, 2008), and the Blue Brain project at EPFL (Switzerland) began in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level (Blue Brain Project, 2005).

1.2 SYSTEMS, MODELS, AND MODELING

1.2.1 SYSTEMS

A system is a part of the real world under study that can be identified from the rest of its environment for a specific purpose. Such a system is called a real system because it is physically part of the real world. The state of a system is defined as the collection of variables necessary to describe a system at a particular time, relative to the objectives of a study.

1.2.1.1 Components of a System

A system is composed of a set of entities (or components) that interact among themselves and with the environment to accomplish the system’s goal. This interaction influences the behavior of the system. An entity has a structure and a behavior. The behavior is the definition of states and state transitions generated by actions the system takes during the time interval in which it is studied. We may also define the behavior as a set of actions or events that change the system’s state. The structure, to a large part, captures the aspects of the system that stay invariant during the time interval in which it is studied, if the system does not modify its structure internally, as in many organic and natural systems.

1.2.1.2 Discrete and Continuous Systems

Systems may be categorized into two types, discrete and continuous. “Few systems in practice are wholly discrete or wholly continuous, but since one type of change predominates for most systems, it will be usually possible to classify a system as being either discrete or continuous” (Law & Kelton, 2000). A discrete system is one for which the state variables may change only at discrete values of time. It is also known as a discrete-time system.

A bank is an example of a discrete system because the state variable, the number of customers in the bank, changes only when a customer arrives or when service provided for a customer is completed. Figure 1.1 shows how the number of customers changes only at discrete points of time.

FIGURE 1.1 Discrete system state variable.

A continuous system is one whose state is capable of changing at any instant of time. It is also known as con...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Preface
• Acknowledgments
• Authors
• SECTION I The Basics
• SECTION II Modeling and Simulation-Based Systems Engineering
• SECTION III Netcentric System of Systems
• SECTION IV Case Studies
• SECTION V Next Steps
• Acronyms
• Index