The Joint Battlespace Infosphere (JBI) program is performing a technology investigation into global communications, data mining and warehousing, and data fusion technologies by focusing on techniques and methodologies that support twenty first century military distributed collaboration. Advancement of these technologies is vitally important if military decision makers are to have the right data, in the right format, at the right time and place to support making the right decisions within available timelines. A quantitative understanding of individual and combinational effects arising from the application of technologies within a framework is presently far too complex to evaluate at more than a cursory depth. In order to facilitate quantitative analysis under these circumstances, the Distributed Information Enterprise Modeling and Simulation (DIEMS) team was formed to apply modeling and simulation (M&S) techniques to help in addressing JBI analysis challenges. The DIEMS team has been tasked utilizing collaborative distributed M&S architectures to quantitatively evaluate JBI technologies and tradeoffs. This paper first presents a high level view of the DIEMS project. Once this approach has been established, a more concentrated view of the detailed communications simulation techniques used in generating the underlying support data sets is presented.
State-of-the-art simulation computing requirements are continually approaching and then exceeding the performance capabilities of existing computers. This trend remains true even with huge yearly gains in processing power and general computing capabilities; simulation scope and fidelity often increases as well. Accordingly, simulation studies often expend days or weeks executing a single test case. Compounding the problem, stochastic models often require execution of each test case with multiple random number seeds to provide valid results. Many techniques have been developed to improve the performance of simulations without sacrificing model fidelity: optimistic simulation, distributed simulation, parallel multi-processing, and the use of supercomputers such as Beowulf clusters. An approach and prototype toolset has been developed that augments existing optimization techniques to improve multiple-execution timelines. This approach, similar in concept to the SETI @ home experiment, makes maximum use of unused licenses and computers, which can be geographically distributed. Using a publish/subscribe architecture, simulation executions are dispatched to distributed machines for execution. Simulation results are then processed, collated, and transferred to a single site for analysis.