Relaxing Synchronization Constraints in Distributed Agent-based Simulations

Authors

  • Omar Rihawi LIFL (CNRS UMR 8022), Université Lille1, 59650 Villeneuve d’Ascq, France
  • Yann Secq LIFL (CNRS UMR 8022), Université Lille1, 59650 Villeneuve d’Ascq, France
  • Philippe Mathieu LIFL (CNRS UMR 8022), Université Lille1, 59650 Villeneuve d’Ascq, France

DOI:

https://doi.org/10.11113/jt.v63.1957

Keywords:

Distributed multi-agent systems, distribute situated agent-based simulations, distributed architectures, synchronization policies, time management

Abstract

In the context of situated agents simulations, when the number of agents increases, the number of their interactions will be increased too. These growths leads to higher requirements in memory and computation power. When simulations involve millions of agents, it becomes necessary to distribute the simulator on a computer network. In this paper we study the impact of synchronization policies in such context. Our claim is that when millions of agents are used in a simulation, because observations of these complex systems is made at the population level, emergent properties at the macroscopic level should not be highly impacted if some failure appears at the microscopic level. This paper is focused on the study of the impact of synchronization relaxation in the context of large scale situated agents simulations. We evaluate the cost in performance of several synchronization policies and their impact on the macroscopic properties of simulations. To that aims, we study three different time management mechanisms and evaluate them on two multi-agent applications.

References

Russell, S. J., Norvig, P., Candy, J. F., Malik, J. M., Edwards, D. D. 1996. Artificial Intelligence: A Modern Approach. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.

Gold, T. 2003. Why Time Flows: The Physics of Past & Future. Daedalus. 132(2): 37–40.

Lamport, L. 1978. Ti Clocks, and the Ordering of Events in a Distributed System. Commun. ACM 21. 558–565.

Jefferson, D. R. 1985. Virtual Time. ACM Trans. 7: 404–425.

Scerri, D., Drogoul, A., Hickmott, S., Padgham, L. 2010. An Architecture for Modular Distributed Simulation with Agent-based Models. In: AAMAS ’10: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems. 541–548.

Siebert, J., Ciarletta, L., Chevrier, V. 2010. Agents and Artefacts for Multiple Models Co-evolution: Building Complex System Simulation as a Set Of Interacting Models. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 -Volume 1. AAMAS ’10, International Foundation for Autonomous Agents and Multiagent Systems. 509–516.

Logan, B., Theodoropoulos, G. 2001. The Distributed Simulation of Multiagent Systems. Proceedings of the IEEE. 89(2): 174–185.

Fujimoto, R. 2000. Parallel and Distributed Simulation Systems. Wiley Series on Parallel ad Distributed Computing. Wiley.

Gupta, B., Rahimi, S., Yang, Y. 2007. A Novel Roll-back Mechanism for Performance Enhancement of Asynchronous Checkpointing and Recovery. Informatica, Slovenia. 31(1): 1–13.

Minson, R., Theodoropoulos, G. K. 2004 Distributing Repast Agent-Based Simulations with Hla. In: In European Simulation Interoperability Workshop. 04–046.

Kiran, M., Richmond, P., Holcombe, M., Chin, L.S., Worth, D., Greenough, C. 2010. Flame: Simulating Large Populations of Agents on Parallel Hardware Architectures. In: AAMAS ’10: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems. 1633–1636.

Karmakharm, T., Richmond, P., Romano, D. 2010. Agent-based Large Scale Simulation of PedestriansWith Adaptive Realistic Navigation Vector Fields. In: Theory and Practice of Computer Graphics. 67–74

Å iÅ¡lák, D., Volf, P., Jakob, M., PÄ›chouÄek, M. 2009. Distributed Platform For Large-scale Agent-based Simulations. In: Agents for Games and Simulations, Springer-Verlag, Berlin. 16–32

Cordasco, G., Rosario, D.C., Ada, M., Dario, M., Vittorio, S., Carmine, S. 2011. A Framework for Distributing Agent-based Simulations. In: In Proc. of The International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms. HeteroPar'11, Bordeaux, France.

Cosenza, B., Cordasco, G., De Chiara, R., Scarano, V. 2011. Distributed Load Balancing for Parallel Agent-based Simulations. In: Parallel, Distributed and Network-Based Processing (PDP), 19th Euromicro International Conference on.

Reynolds, C. 1999. Steering Behaviors for Autonomous Characters.

Wilensky, U. 1997. Netlogo Wolf-Sheep Predation Model, Center For Connected Learning And Computer-Based Modeling, Northwestern University, Evanston, Il.

P. Mathieu and O. Brandouy. 2010. A Generic Architecture for Realistic Simulations of Complex Financial Dynamics. In Advances in Practical Applications of Agents and Multiagent Systems, 8th International conference on Practical Applications of Agents and Multi-Agents Systems (PAAMS’2010). Springer. 185–197.

Y. Kubera, P. Mathieu, and S. Picault. 2008. Interaction-oriented Agent Simulations: From Theory to Implementation. In Proceedings of the 18th European Conference on Artificial Intelligence (ECAI’08). IOS Press. 383–387.

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Published

2013-07-15

Issue

Section

Science and Engineering

How to Cite

Relaxing Synchronization Constraints in Distributed Agent-based Simulations. (2013). Jurnal Teknologi, 63(3). https://doi.org/10.11113/jt.v63.1957