Using Distributed Analytics to Enable Real-Time Exploration of Discrete Event Simulations

Discrete event simulations (DES) provide a powerful means for modeling complex 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 computing and storage resources available in both private organizations and public clouds to enable real-time exploration of a discrete event simulation. Rather than considering the execution speed of a single simulation run, we autonomously generate novel scenario variants to explore an entire subset of the simulation parameter space. These workloads are orchestrated in a distributed fashion across a wide range of commodity hardware. The resulting outputs are analyzed to produce models that accurately forecast simulation outcomes in real time, providing interactive feedback and bolstering research possibilities.