Many modern systems involve complex interactions between a large number of diverse entities that constitute these systems. Unfortunately, these large, complex systems frequently defy analysis by conventional analytical methods and their study is generally performed using simulation models. Further aggravating the situation, detailed simulations of large systems will frequently require days, weeks, or even months of computer time and lead to scaled down studies. These scaled down studies may be achieved by the creation of smaller, representative, models and/or by analysis with short duration simulation exercises. Unfortunately, scaled down simulation studies will frequently fail to exhibit behaviors of the full-scale system under study. Consequently, better simulation infrastructure is needed to support the analysis of ultra-large (models containing over 1 million components)-scale models.
Simulation support for ultra-large-scale simulation models must be achieved using low-cost commodity computer systems. The expense of custom or high-end parallel systems prevent their widespread use. Consequently, we have developed an Ultra-large-Scale Simulation Framework (USSF). This paper presents the issues involved in the design and development of USSF. Parallel simulation techniques are used to enable optimal time versus resource tradeoffs in USSF. The techniques employed in the framework to reduce and regulate the memory requirements of the simulations are described. The API needed for model development is illustrated. The results obtained from the experiments conducted using various system models with two parallel simulation kernels (comparing a conventional approach with USSF) are also presented.