Herbert D. Schwetman

CSIM18—the simulation engine

A simulation engine is the collection of components, features and support functions which are crucial to the implementation of an efficient discrete event simulation model. Furthermore, this model can be embedded in a larger application. A good simulation engine combines efficiency, functionality and completeness to enable a model builder to construct a comprehensive, customized model of either a specific system or a class of systems. This paper presents CSIM18, a simulation engine which supports development of applications with efficient, em bedded simulation models on a variety of system plat forms.

Introduction to process-oriented simulation and CSIM

CSIM is a simulation package which lets C and C++ programmers write process-oriented simulation models. The author first presents the basic concepts of the process-oriented paradigm. He then illustrates this simulation paradigm as implemented in CSIM. Issues involving the size and costs of simulation models written using this paradigm are discussed. A number of examples drawn from diverse application areas are considered which illustrate the descriptive power of process-oriented simulation models.<<ETX>>

Object-oriented simulation modeling with C++/CSIM17

C++/CSIM17 is toolkit for constructing process-oriented, discrete-simulation models by writing C++ programs. The use of C++ as the implementation language for the model means that the majority of the object-oriented techniques and methodologies that have been developed for C++ programmers can be applied to the design and implementation of simulation models.

CSIM17: a simulation model-building toolkit

CSIM is a simulation model-building toolkit that is used by C/C++ programmers to implement process-oriented, discrete-event simulation models. These models mimic the operation of complex systems, to give modelers insight into the dynamic behavior of these systems. Because CSIM models are C/C++ programs, there are virtually no limits on the level of detail, degree of complexity and size of the simulation models. Furthermore, CSIM uses special implementation techniques so that these models execute efficiently on a wide variety of systems platforms, including many UNIX workstations, PCs with Windows and Macintoshes. This tutorial introduces CSIM and then presents some of the features that make CSIM a useful tool for building efficient simulation models of complex systems. Two examples help illustrate these features. Integration of CSIM into other software products are also discussed.

Model-based systems analysis using CSIM18

Achieving the best possible cost performance for a system is often the goal of the manager of that system. This is true for large systems (such as an entire railroad system) as well as for small systems (such as a computerized departmental transaction processing system). One technique for achieving this management goal is to develop and use an operational model of the system. This paper describes model-based systems analysis using simulation models of systems. This technique for finding and addressing performance related problems is widely used in a variety of industries. In many cases, the resulting improvements in cost-performance characteristics for the system can be significant.

Data analysis and automatic run-length control in CSIM18

The data collection and the automatic run-length control features provided in the CSIM18 library allow model builders to easily collect valid data from a simulation model and to be assured that statistically valid results have been achieved at a reasonable computational cost. This paper gives an overview of the CSIM 18 library and then presents these features by using an example of a system model.

Simulation of a signal quality survey

This paper describes a simulator for a large outdoor operation called a signal quality survey. Design and implementation of the simulator follows an object oriented approach with a primary focus of modeling operations cycles. The simulator is implemented in Visual C++/CSIM17. Excel acts as the primary user interface. A Microsoft Foundations Classes Single Document Interface application acts as a secondary user interface for simulation animation. We discuss the benefits of using discrete event simulation for this application. We also report on some of the challenges encountered during the implementation stages of the project.

Simulation of computer systems using automatically generated load descriptions

A complete description of a computer system must include three groups of components:n 1. The hardware componentsn 2. The software components (the operating system), andn 3. The load components (the stream of tasks to be processed).n When computer systems are simulated all of these components are found as features of the simulations. The hardware normally appears as a collection of tables and associated operational data. The operating system appears both as tables and as scheduling or assignment algorithms. The load is represented either as a sequence of requests for service (use of system resources) or as a set of functions which can generate such a sequence of requests. In many applications, the description of the load turns out to be the most difficult task.n This paper discusses some techniques and procedures which have been used to automatically generate the required load descriptions. The emphasis is on a request sequence generator which uses data found in a system event trace. One such technique, called trace-driven modeling, has been implemented at the Purdue University Computing Center. This implementation is discussed and its usefulness and accuracy are illustrated with actual data.

“Model, then build”: a modern approach to systems development using CSIM18

Developing a behavioral model of a system prior to implementing that system provides significant benefits. In some cases, errors in the design can be detected and corrected prior to implementation. In other cases, having a means for predicting the performance of the system prior to its deployment may aid in the design, implementation and configuration of the system. This paper presents a case for developing simulation models of systems early in the design and implementation process. A number of examples illustrate the benefits which can accrue.

Using Java method traces to automatically characterize and model J2EE server applications

This paper describes a novel framework used to characterize a J2EE (Java Enterprise Edition) application and develop models of the application by using Java method tracing in a Java-technology based application server. Application servers are critical to large-scale, online servers and serve as middleware to provide secure access to transactional, legacy and Web services. The tracing tool in this framework gives a detailed and comprehensive view of the sequences of methods invoked as the application server processes requests. The output of this tool is processed and automatically summarized into a set of transaction profiles which form the input for a simulation model of the application server and its related components. These profiles have proven to be a useful abstraction of the behavior of the transactions processed by the system. After describing the tool and the model, the paper provides results of validation runs and discusses the usefulness of quantitative measurement, analysis and modeling in some areas of system design and system deployment. The models help architects, designers, developers and deployers explore the different facets of performance during all stages of an applications life-cycle, especially during concept development and prototyping