SAMAS: Scalable Architecture for Multiscale Agent-based Simulation

Large-scale agent-based simulation has been shown to be an effective tool for understanding a variety of complex systems, such as market economies, war games, and epidemic propagation models. As systems of interest grow in complexity, it is often desirable to support different categories of artificial agents that execute tasks on widely varying time scales. The scalability of a simulation environment becomes a crucial measure of its ability to handle the complexity of the underlying system. In this paper, we present a design for a highly scalable architecture for multi-resolution agentbased simulation. SAMAS is a dynamic data-driven application system (DDDAS) that allows large numbers of heterogeneous agents to operate across a wide range of time scales without unnecessarily compromising simulation runtime. This is accomplished through the use of gossiping, an efficient broadcasting communication model for maintaining the overall consistency of the simulation environment. We demonstrate the effectiveness of this communication model using experimental results obtained from simulation.