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Computer Systems Organization -- general.
Modeling and Simulation of Computer Networks and Systems: Methodologies and Applications introduces you to a broad array of modeling and simulation issues related to computer networks and systems. It focuses on the theories, tools, applications and uses of modeling and simulation in order to effectively optimize networks. It describes methodologies for modeling and simulation of new generations of wireless and mobiles networks and cloud and grid computing systems. Drawing upon years of practical experience and using numerous examples and illustrative applications recognized experts in both academia and industry, discuss: - Important and emerging topics in computer networks and systems including but not limited to; modeling, simulation, analysis and security of wireless and mobiles networks especially as they relate to next generation wireless networks - Methodologies, strategies and tools, and strategies needed to build computer networks and systems modeling and simulation from the bottom up - Different network performance metrics including, mobility, congestion, quality of service, security and more... Modeling and Simulation of Computer Networks and Systems is a must have resource for network architects, engineers and researchers who want to gain insight into optimizing network performance through the use of modeling and simulation. - Discusses important and emerging topics in computer networks and Systems including but not limited to; modeling, simulation, analysis and security of wireless and mobiles networks especially as they relate to next generation wireless networks - Provides the necessary methodologies, strategies and tools needed to build computer networks and systems modeling and simulation from the bottom up - Includes comprehensive review and evaluation of simulation tools and methodologies and different network performance metrics including mobility, congestion, quality of service, security and more
The design of communication systems has grown too complicated for the traditional design tools--mathematical analysis and laboratory breadboards. Enter the computer simulation, a powerful and versatile tool that is becoming essential for anyone who designs signal transmission or storage systems. This volume explains in detail how to use simulation programs as a software breadboard to analyze and evaluate the performance of data communications links. It describes the engineering principles of signal transmission and its simulation, explores programming issues, and provides a comprehensive reference for models of signal processes. The book clearly demonstrates how simulation techniques can be used to: * Create valid models of signal processes * Provide exibility through the use of modules * Simulate various elements of communications systems, from filters and modulators to test instruments * Explore alternative models for a given system * Circumvent the mathematical intractability of modern transmission links * Plan and construct a computer model in a matter of hours or days, versus the weeks or months needed for laboratory breadboards * Make parameter changes in minutes once a link has been modeled * Provide engineers and students with complete training on the elements of simulation A must have for designers, practicing engineers, and graduate students, this volume presents real-world techniques that can be used with the authors' ST?DT program (a companion work also published by Wiley), or independently with other commercially available simulators.
Based on the author's experience in industry, this book focuses on simple techniques for solving everyday problems in systems design and analysis. All techniques are covered in a non-mathematical way, so that no statistics expertise is necessary.
Computer-Assisted Simulation of Dynamic Systems with Block Diagram Languages explores the diverse applications of these indispensable simulation tools. The first book of its kind, it bridges the gap between block diagram languages and traditional simulation practice by linking the art of analog/hybrid computation with modern pc-based technology. Direct analogies are explored as a means of promoting interdisciplinary problem solving. The reader progresses step-by-step through the creative modeling and simulation of dynamic systems from disciplines as diverse from each other as biology, electronics, physics, and mathematics. The book guides the reader to the dynamic simulation of chaos, conformal mapping, VTOL aircraft, and other highly specialized topics. Alternate methods of simulating a single device to emphasize the dynamic rather than schematic features of a system are provided. Nearly-forgotten computational techniques like that of integrating with respect to a variable other than time are revived and applied to simulation and signal processing. Actual working models are found throughout this eminently readable book, along with a complete international bibliography for individuals researching subjects in dynamic systems. This is an excellent primary text for undergraduate and graduate courses in computer simulation or an adjunct text for a dynamic systems course. It is also recommended as a professional reference book.
Social systems are among the most complex known. This poses particular problems for those who wish to understand them. The complexity often makes analytic approaches infeasible and natural language approaches inadequate for relating intricate cause and effect. However, individual- and agent-based computational approaches hold out the possibility of new and deeper understanding of such systems. Simulating Social Complexity examines all aspects of using agent- or individual-based simulation. This approach represents systems as individual elements having each their own set of differing states and internal processes. The interactions between elements in the simulation represent interactions in the target systems. What makes these elements "social" is that they are usefully interpretable as interacting elements of an observed society. In this, the focus is on human society, but can be extended to include social animals or artificial agents where such work enhances our understanding of human society. The phenomena of interest then result (emerge) from the dynamics of the interaction of social actors in an essential way and are usually not easily simplifiable by, for example, considering only representative actors. The introduction of accessible agent-based modelling allows the representation of social complexity in a more natural and direct manner than previous techniques. In particular, it is no longer necessary to distort a model with the introduction of overly strong assumptions simply in order to obtain analytic tractability. This makes agent-based modelling relatively accessible to a range of scientists. The outcomes of such models can be displayed and animated in ways that also make them more interpretable by experts and stakeholders. This handbook is intended to help in the process of maturation of this new field. It brings together, through the collaborative effort of many leading researchers, summaries of the best thinking and practice in this area and constitutes a reference point for standards against which future methodological advances are judged. This book will help those entering into the field to avoid "reinventing the wheel" each time, but it will also help those already in the field by providing accessible overviews of current thought. The material is divided into four sections: Introductory, Methodology, Mechanisms, and Applications. Each chapter starts with a very brief section called ‘Why read this chapter?’ followed by an abstract, which summarizes the content of the chapter. Each chapter also ends with a section of ‘Further Reading’ briefly describing three to eight items that a newcomer might read next.
Simulation is the art of using tools - physical or conceptual models, or computer hardware and software, to attempt to create the illusion of reality. The discipline has in recent years expanded to include the modelling of systems that rely on human factors and therefore possess a large proportion of uncertainty, such as social, economic or commercial systems. These new applications make the discipline of modelling and simulation a field of dynamic growth and new research. Stanislaw Raczynski outlines the considerable and promising research that is being conducted to counter the problems of uncertainty surrounding the methods used to approach these new applications. It aims to stimulate the reader into seeking out new tools for modelling and simulation.Examines the state-of-the-art in recent research into methods of approaching new applications in the field of modelling and simulationProvides an introduction to new modelling tools such as differential inclusions, metric structures in the space of models, semi-discrete events, and use of simulation in parallel optimization techniquesDiscusses recently developed practical applications: for example the PASION simulation system, stock market simulation, a new fluid dynamics tool, manufacturing simulation and the simulation of social structuresIllustrated throughout with a series of case studies "Modelling and Simulation: The Computer Science of Illusion" will appeal to academics, postgraduate students, researchers and practitioners in the modelling and simulation of industrial computer systems. It will also be of interest to those using simulation as an auxiliary tool.