Discrete event simulation

About: Discrete event simulation is a research topic. Over the lifetime, 8671 publications have been published within this topic receiving 146287 citations.

Discrete-Event System Simulation

Abstract: Beleska o autorima: str. XV-XVI. - Bibliografija uz svako poglavlje. - Registar. - Summaries.

Theory of modeling and simulation

Abstract: From the Publisher: Although twenty-five years have passed since the first edition of this classical text, the world has seen many advances in modeling and simulation, the need for a widely accepted framework and theoretical foundation is even more necessary today. Methods of modeling and simulation are fragmented across disciplines making it difficult to re-use ideas from other disciplines and work collaboratively in multidisciplinary teams. Model building and simulation has been made easier and faster by riding piggyback on advances in software and hardware. However, difficult and fundamental issues such as model credibility and interoperation have received less attention. These issues are now front and center under the impetus of the High Level Architecture (HLA) standard mandated by the U.S. DoD for all contractors and agencies. This book concentrates on integrating the continuous and discrete paradigms for modeling and simulation. A second major theme is that of distributed simulation and its potential to support the co-existence of multiple formalisms in multiple model components. Prominent throughout are the fundamental concepts of modular and hierarchical model composition. This edition presents a rigorous mathematical foundation for modeling and simulation. Also, it now provides a comprehensive framework for integrating the various simulation approaches employed in practice. Including such popular modeling methods as cellular automata, chaotic systems, hierarchical block diagrams, and Petri nets. A unifying concept, called the DEVS Bus, enables models, as expressed in their native formalisms, to be transparently mapped into the Discrete Event System Specification (DEVS). The book shows how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. If you are doing integrative simulations, whether or not they are HLA compliant, this is the only book available to provide the foundation to understand, simplify and successfully accomplish your task. Herbert Praehofer is an Assistant Professor at the Johannes Kepler University in Linz, Austria. He has over 50 publications in international journals and conference proceedings on Modeling and Computer Simulation, Systems Theory, and Software Engineering. Tag Gon Kim is a Professor of Electrical Engineering at the Korea Advanced Institutes of Science and Technology (KAIST), Taejon, Korea. His research interests include discrete event systems modeling/simulation, computer/communication systems analysis, and object-oriented simulation engineering. He is a senior member of IEEE and SCS, and a member of ACM. * Provides a comprehensive framework for continuous and discrete event modeling and simulation * Explores the mathematical foundation of simulation modeling * Discusses system morphisms for model abstraction and simplification * Presents a new approach to discrete event simulation of continuous processes * Includes parallel and distributed simulation of discrete event models * Presentation of a concept to achieve simulator interoperability in the form of the DEVS-Bus * Complete coverage necessary for compliance with High Level Architecture (HLA) standards Bernard P Zeigler, is a Professor of Electrical & Computer Engineering at the University of Arizona and heads the Artificial Intelligence Simulation Research Group. He is the author of numerous books and publications, and he is the Editor-in-Chief of the Transactions of the Society for Computer Simulation International.

The omnet++ discrete event simulation system

Abstract: The paper introduces OMNeT++, a C++-based discrete event simulation package primarily targeted at simulating computer networks and other distributed systems. OMNeT++ is fully programmable and modular, and it was designed from the ground up to support modeling very large networks built from reusable model components. Large emphasis was placed also on easy traceability and debuggability of simulation models: one can execute the simulation under a powerful graphical user interface, which makes the internals of a simulation model fully visible to the person running the simulation: it displays the network graphics, animates the message flow and lets the user peek into objects and variables within the model. These features make OMNeT++ a good candidate for both research and educational purposes. The OMNeT++ simulation engine can be easily embedded into larger applications. OMNeT++ is opensource, free for non-profit use, and it has a fairly large user

Virtual time

Abstract: Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concurrency control. Virtual time provides a flexible abstraction of real time in much the same way that virtual memory provides an abstraction of real memory. It is implemented using the Time Warp mechanism, a synchronization protocol distinguished by its reliance on lookahead-rollback, and by its implementation of rollback via antimessages.

Parallel discrete event simulation

Abstract: Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14].A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc.We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem.This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems.Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.