A Scalable Architecture for Crowd Simulation: Implementing a Parallel Action Server

Crowd simulation can be considered as a special case of virtual environments where avatars are intelligent agents instead of user-driven entities. These applications require both rendering visually plausible images of the virtual world and managing the behavior of autonomous agents. Although several proposals have focused on the software architectures for these systems, the scalability of crowd simulation is still an open issue. In this paper, we propose a scalable architecture that can manage large crowds of autonomous agents at interactive rates. This proposal consists of enhancing a previously proposed architecture through the efficient parallelization of the action server and the distribution of the semantic database. In this way, the system bottleneck is removed, and new action servers (hosted each one on a new computer) can be added as necessary. The evaluation results show that the proposed architecture is able to fully exploit the underlying hardware platform, regardless of both the number and the kind of computers that form the system. Therefore, this system architecture provides the scalability required for large-scale crowd simulation.