Enhancing Understanding of Discrete Event Simulation Models Through Analysis

Simulation is used increasingly throughout research, development, and planning for many purposes. While model output is often the primary interest, insights gained through the simulation process can also be valuable. Insights can come from building and validating the model as well as analyzing its behaviors and output; however, much that could be informative may not be easily discernible through these existing traditional approaches, particularly as models continue to increase in complexity. This research extends current work in model analysis and program understanding to assist modelers in obtaining more insight into their models and the systems they represent. A primary technique for model understanding is analysis of model output; this research has developed new, complementary techniques. A significant point of this research is that the created tools do not necessitate that a modeler or model user be able to encode the model or have any coding expertise. Some of the information presented here could be produced by existing software development tools; however, most modelers today do not have the technical background to use such tools or to make use of the reports they can produce. Additionally, one of the significant details of this research is the focus on model aspects rather than simulation aspects: the tools developed here detail the model embedded in implementation code, not the code necessary for implementation. Source code tends to involve many issues unrelated to the model itself, such as data collection, animation, and tricks for efficient run-time behavior. Even when the modeler is an expert programmer, this other code often can obscure features of the model as implemented. Results indicate these tools and techniques, when applied to even modest simulation models, can reveal aspects of those models not readily apparent to the builders or users of the models. This work provides both model builders and model users with additional techniques that can give them improved understanding of their models

The Design of XML-Based Model and Experiment Description Languages for Network Simulation

The Simulation Automation Framework for Experiments (SAFE) is a project created to raise the level of abstraction in network simulation tools and thereby address issues that undermine credibility. SAFE incorporates best practices in network simulationto automate the experimental process and to guide users in the development of sound scientific studies using the popular ns-3 network simulator. My contributions to the SAFE project: the design of two XML-based languages called NEDL (ns-3 Experiment Description Language) and NSTL (ns-3 Script Templating Language), which facilitate the description of experiments and network simulationmodels, respectively. The languages provide a foundation for the construction of better interfaces between the user and the ns-3 simulator. They also provide input to a mechanism which automates the execution of network simulation experiments. Additionally,this thesis demonstrates that one can develop tools to generate ns-3 scripts in Python or C++ automatically from NSTL model descriptions