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Simulation, especially discrete event simulation (DES), is used in a variety of disciplines where numerical methods are difficult or impossible to apply. One problem with this method is that a sufficiently detailed simulation may take hours or days to execute, and multiple runs may be needed in order to generate the desired results. Parallel discrete event simulation (PDES) has been explored for many years as a method to decrease the time taken to execute a simulation. Many protocols have been developed which work well for particular types of simulations, but perform poorly when used for other types of simulations. Often it is difficult to know a priori whether a particular protocol is appropriate for a given problem. In this work, an adaptive synchronization method (ASM) is developed which works well on an entire spectrum of problems. The ASM determines, using an artificial neural network (ANN), the likelihood that a particular event is safe to process.
Active networking is an exciting new paradigm in digital networking that has the potential to revolutionize the manner in which communication takes place. It is an emerging technology, one in which new ideas are constantly being formulated and new topics of research are springing up even as this book is being written. This technology is very likely to appeal to a broad spectrum of users from academia and industry. Therefore, this book was written in a way that enables all these groups to understand the impact of active networking in their sphere of interest. Information services managers, network administrators, and e-commerce developers would like to know the potential benefits of the new technology to their businesses, networks, and applications. The book introduces the basic active networking paradigm and its potential impacts on the future of information handling in general and on communications in particular. This is useful for forward-looking businesses that wish to actively participate in the development of active networks and ensure a head start in the integration of the technology in their future products, be they applications or networks. Areas in which active networking is likely to make significant impact are identified, and the reader is pointed to any related ongoing research efforts in the area. The book also provides a deeper insight into the active networking model for students and researchers, who seek challenging topics that define or extend frontiers of the technology. It describes basic components of the model, explains some of the terms used by the active networking community, and provides the reader with taxonomy of the research being conducted at the time this book was written. Current efforts are classified based on typical research areas such as mobility, security, and management. The intent is to introduce the serious reader to the background regarding some of the models adopted by the community, to outline outstanding issues concerning active networking, and to provide a snapshot of the fast-changing landscape in active networking research. Management is a very important issue in active networks because of its open nature. The latter half of the book explains the architectural concepts of a model for managing active networks and the motivation for a reference model that addresses limitations of the current network management framework by leveraging the powerful features of active networking to develop an integrated framework. It also describes a novel application enabled by active network technology called the Active Virtual Network Management Prediction (AVNMP) algorithm. AVNMP is a pro-active management system; in other words, it provides the ability to solve a potential problem before it impacts the system by modeling network devices within the network itself and running that model ahead of real time.
Discrete-event simulation has long been an integral part of the design process of complex engineering systems and the modelling of natural phenomena. Many of the systems that we seek to understand or control can be modelled as digital systems. In a digital model, we view the system at discrete instants of time, in effect taking snapshots of the system at these instants. For example, in a computer network simulation an event can be the sending of a message from one node to another node while in a VLSI logic simulation, the arrival of a signal at a gate may be viewed as an event. Digital systems such as computer systems are naturally susceptible to this approach. However, a variety of other systems may also be modelled this way. These include transportation systems such as air-traffic control systems, epidemiological models such as the spreading of a virus, and military war-gaming models. This book is representative of the advances in this field.
This text on high-performance computing includes coverage of the topics: applications; I/O and compilers; scientific computing; data and file management; interconnection networks; compilers; image and signal processing; distributed systems; algorithms; architecture; and parallel programming.