The Role of Modeling and Asynchronous Distributed Simulation in Analyzing Complex Systems of the Future

Abstract

The word simulate implies to imitate or to mimic while the word modeling refers to a small object, usually built to scale, that represents some existing object. Although the art of mimicking and modeling may be traced back to the beginning of civilization, with the emergence of computers, a few decades ago, the art of modeling and simulation experienced a remarkable transformation. The computational intelligence of the computer imparted the ability to encapsulate and simulate specific characteristics of not only living and inanimate objects but abstract concepts. While the human brain is equally capable of simulating abstract concepts, the precision and speed of the computers are unparalleled and they impart computer modeling and simulation a qualitative jump in its capability and fidelity. Also, while intimately connected to each other, modeling refers to the notion of representing the desired behavior of the target object or idea in the host computer and simulation refers to the execution of the model on a host computer. Today, towards the end of the twentieth century, the nature of modeling and simulation is undergoing another radical transformation. The emergence of economical and powerful computers coupled with the ability to network a large number of computers, promises the ability to model and simulate complex, real-world systems, that are rapidly becoming commonplace in the society, successfully and with a high degree of fidelity. Already, today's real-world systems including complex banking, credit-card transaction, transportation, ground-based communication, and space-based tele-communication systems defy the analytical modeling that had characterized the efforts over the past three decades. Virtually all analytical studies are severely restricted in the number of variables and the number of interacting units that may be modeled. Tomorrow's systems are expected to be far more complex, implying that modeling and large-scale simulation may be the most logical and, often, the only vehicle to study them objectively. This paper presents a fundamental analysis of the nature of complex physical and natural processes that will increasingly constitute the challenging problems of the future. It then develops a set of principles for modeling complex systems. Finally, it examines the role of asynchronous, distributed simulation in the study of a number of real-world systems. In general, modeling and simulation enables one to detect design errors, prior to developing a prototype, in a cost effective manner, identify potential problems during system operation, detect rare and elusive errors, and investigate hypothetical concepts that do not exist in nature. Upon execution, the simulation provides greater quantitative and qualitative insight into the behavior of the actual system. In addition, an asynchronous, distributed simulation, executing on a loosely-coupled parallel processor, closely resembles the actual, operational system, yielding results that potentially reflect reality, as close as possible. Furthermore, elements of the simulation code that emulate the system behavior may be transferred directly onto the operational system with minimal modifications. The knowledge encapsulated in this paper, has been derived from a number of actual case studies involving the modeling and simulation of a number of real-world problems.