Measuring and Comparing Scalability of Agent-Based Simulation Frameworks

While computer simulation gained importance as a technique for generating knowledge in various research disciplines, the size of simulation models representing real world scenarios is growing, too. In Social Simulation, e.g., there is a need to simulate a large number of humans using individual software agents for generating and analyzing human-like behavior in artificial societies. Nowadays, a variety of toolkits and frameworks exists providing functionalities for supporting implementation and execution of simulation experiments. Yet, the choice of a suitable framework is difficult as unforeseen scalability issues may arise when extending agent models. Therefore, this paper aims at providing a method for analyzing and comparing agent-based simulation frameworks regarding their ability to scale simulation models and experiments. Based on performance metrics, standardized experiments are conducted while altering internal and external scaling parameters. As part of the study, four Java-based agent frameworks are analyzed and compared: Aimpulse Spectrum, JADE, MASON, and Repast.