Predictive Analytics Using Statistical, Learning, and Ensemble Methods to Support Real-Time Exploration of Discrete Event Simulations

Discrete event simulations (DES) provide a powerful means for modeling com-plex systems and analyzing their behavior. DES capture all possible interactions between the entities they manage, which makes them highly expressive but also compute-intensive. These computational requirements often impose limitations on the breadth and/or depth of research that can be conducted with a discrete event simulation. This work describes our approach for leveraging the vast quantity of com-puting and storage resources available in both private organizations and public clouds to enable real-time exploration of discrete event simulations. Rather than directly targeting simulation execution speeds, we autonomously generate and execute novel scenario variants to explore a representative subset of the simulation parameter space. The corresponding outputs from this process are analyzed and used by our framework to produce models that accurately forecast simulation outcomes in real time, providing interactive feedback and facilitating exploratory research. Our framework distributes the workloads associated with generating and exe-cuting scenario variants across a range of commodity hardware, including public and private cloud resources. Once the models have been created, we evaluat