SAMAS: Scalable Architecture for Multi-resolution Agent-Based Simulation
Large scale agent-based simulation has become the focus of great interest from both academy and industry in resent years. It has been shown an effective tool for understanding a variety of complex systems, such as market economics, war games, and epidemic propagation models. As the systems of interest grow in complexity, it is often desirable to have different categories of artificial agents that execute tasks on different time scales. With the added complexity, the scalability of a simulation environment becomes a crucial measure of its ability in coping with the complexity of the underlying system. In this paper, we present the design of SAMAS, a highly scalable architecture for multi-resolution agent-based simulation. SAMAS is a dynamic data driven application system (DDDAS) that adopts an efficient architecture that allows large number of agents to operate on a wide range of time scales. It uses 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.
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