Enhancing the Performances of D-MASON - A Motivating Example

Agent-based simulation models are an increasingly popular tool for research and management in many, different and diverse fields. In executing such simulations the “speed” is one of the most general and important issues and the traditional answer to this issue is to invest resources in deploying a dedicated installation of dedicated computers, with highly specialized parallel applications, devoted to the purpose of achieving extreme computational performances. In this paper we present our experience with a distributed framework, D-M ASON, that is a distributed version of MASON, a well-known and popular library for writing and running Agent-based simulations. D-M ASON introduces the parallelization at framework level so that scientists that use the framework (domain expert but with limited knowledge of distributed programming) can be only minimally aware of such distribution. The framework allowed only a static decomposition of the work among workers, and was not able to cope with load unbalance among them, therefore incurring in serious performance degradation where, for example, many of the agents were concentrate on one specific part of the space. We elaborated two strategies for ameliorate the balancing and enhance the synchronization among workers. We present their design principles and the experimental tests that validate our approach.

[1]  Gennaro Cordasco,et al.  A Framework for Distributing Agent-Based Simulations , 2011, Euro-Par Workshops.

[2]  S. Luke,et al.  Ant Foraging Revisited , 2004 .

[3]  P. Davidsson,et al.  Scalability in Distributed Multi-Agent Based Simulations: The JADE Case , 2008, 2008 Second International Conference on Future Generation Communication and Networking Symposia.

[4]  Sean Luke,et al.  A pheromone-based utility model for collaborative foraging , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[5]  Matthew J. Berryman,et al.  Review of Software Platforms for Agent Based Models , 2008 .

[6]  Steven L. Lytinen,et al.  Agent-based Simulation Platforms: Review and Development Recommendations , 2006, Simul..

[7]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[8]  Sean Luke,et al.  Learning Ant Foraging Behaviors , 2004 .

[9]  Bruce Edmonds,et al.  Special Issue: Agent Based Simulation of Complex Social Systems , 2012, Simul..

[10]  Steffen Straßburger,et al.  Scalability in distributed simulations of agent-based models , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).