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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Adaptive and optimized agent placement scheme for parallel agent-based simulation

  • Jin, Ki-Sung (Data Centric Computing System Research Section, Electronics and Telecommunications Research Institute) ;
  • Lee, Sang-Min (Data Centric Computing System Research Section, Electronics and Telecommunications Research Institute) ;
  • Kim, Young-Chul (Data Centric Computing System Research Section, Electronics and Telecommunications Research Institute)
  • Received : 2020.10.19
  • Accepted : 2021.07.07
  • Published : 2022.04.10
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Abstract

This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost-benefit analysis, we propose a novel approach to overcome the performance constraints of agent-based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.

Keywords

Acknowledgement

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00225, Development of City Interior Digital Twin Technology to establish Scientific Policy).

References

  1. C. M. Macal and M. J. North, Introductory tutorial: Agent-based modeling and simulation, in Proc. Winter Simul. Conf. (Savannah, GA, USA), Dec. 2014, pp. 6-20.
  2. L. Padgham et al., Integrating BDI reasoning into agent based modeling and simulation, in Proc. Winter Simul. Conf. (Phoenix, AZ, USA), Dec. 2011, pp. 345-356.
  3. C. M. Macal and M. J. North, Toward teaching agent-based simulation, in Proc. Winter Simul. Conf. (Baltimore, MD, USA), Dec. 2010, pp. 268-277.
  4. S. Coakley et al., Exploitation of high performance computing in the FLAME agent-based simulation framework, in Proc. IEEE Int. Conf. High Perform. Comput. Commun. & IEEE Int. Conf. Embed. Softw. Syst. (Liverpool, UK), June 2012, pp. 538-545.
  5. G. Cordasco et al., Bringing together efficiency and effectiveness in distributed simulations: The experience with D-MASON, J. Simul. 89 (2013), no. 10, 1236-1253. https://doi.org/10.1177/0037549713489594
  6. E. S. Angelotti, E. E. Scalabrin, and B. C. Avila, Pandora: A multi-agent system using paraconsistent logic, in Proc. Int. Conf. Comput. Intell. Multimed. Appl. (Yokusika City, Japan), Oct. 2001, pp. 352-356.
  7. N. Collier, J. Ozik, and C. M. Macal, Large-scale agent-based modeling with repast HPC: A case study in parallelizing an agent-based model, in European Conference on Parallel Processing, Springer, Cham, Switzerland, 2015, pp. 454-465.
  8. G. Cordasco et al., A framework for distributing agent-based simulations, in European Conference on Parallel Processing, vol. 7155, Springer, Berlin, Heidelberg, Germany, 2012, pp. 460-470.
  9. X. Li, W. Cai, and S. J. Turner, Cloning agent-based simulation, ACM Trans. Model. Comput. Simul. 27 (2017), no. 2, 1-24.
  10. C. Marquez, E. Cesar, and J. Sorribes, Agent migration in HPC Systems using FLAME, in European Conference on Parallel Processing, vol. 8374, Springer, Berlin, Heidelberg, Germany, 2013, pp. 523-532.
  11. B. K. Gorur et al., Repast HPC with optimistic time management, in Proc. Symp. High Perform. Comput. (Pasadena, CA, USA), Apr. 2016, pp. 1-9.
  12. P. Wittek and X. Rubio-Campillo, Scalable agent-based modelling with cloud HPC resources for social simulations, in Proc. IEEE Int. Conf. Cloud Comput. Technol. Sci. (Taipei, Taiwan), Dec. 2012. pp. 355-362.
  13. S. Bandini, S. Manzoni, and G. Vizzari, Agent based modeling and simulation an informatics perspective, J. Artif. Soc. Soc. Simul. 12 (2009), no. 4, 4.
  14. C. Cioffi-Revilla, A methodology for complex social simulations, J. Artif. Soc. Soc. Simul. 13 (2010), no. 1, 7. https://doi.org/10.18564/jasss.1528
  15. M. J. North et al., Complex adaptive systems modeling with repast simphony, Complex Adapt. Syst. Model. 1 (2013), no. 3, 1-26. https://doi.org/10.1186/2194-3206-1-1
  16. S. Luke et al., MASON: A new multi-agent simulation toolkit, Proc. Swarmfest Workshop 8 (2004), no. 2, 316-327.
  17. S. Tisue and U. Wilensky, NetLogo: Design and implementation of a multi-agent modeling environment, in Proc. Agent (Chicago, IL, USA), Oct. 2004, pp. 7-9.
  18. E. Amouroux et al., Gama: An environment for implementing and running spatially explicit multi-agent simulations, in Agent Computing and Multi-Agent Systems, vol. 5044, Springer, Berlin, Heidelberg, Germany, 2009, pp. 359-371.
  19. M. Scheutz et al., Dingler, Swages-an extendable distributed experimentation system for large-scale agent-based alife simulations, in Artificial Life X, MIT Press, Cambridge, MA, USA, 2006, pp. 412-419.
  20. P. Davidsson, Agent based social simulation: A computer science view, J. Artif. Soc. Soc. Simul. 5 (2002), no. 1, 1-7.
  21. S. Abar et al., Agent based modelling and simulation tools: A review of the state-of-art software, Comput. Sci. Rev. 24 (2017), 13-33. https://doi.org/10.1016/j.cosrev.2017.03.001
  22. P. Leit~ao, U. Inden, and C. P. Ruckemann, Parallelising multiagent systems for high performance computing, in Proc. Int. Conf. Adv. Commun. Comput. (Lisbon, Portugal), Nov. 2013, pp. 1-6.
  23. A. Rousset et al., A survey on parallel and distributed multiagent systems, in European Conference on Parallel Processing, vol. 8805, Springer, Cham, Switzerland, 2013, pp. 371-382.
  24. J. Oh et al., Exploiting thread-level parallelism in lockstep execution by partially duplicating a single pipeline, ETRI J. 30 (2008), no. 4, 576-586. https://doi.org/10.4218/etrij.08.0107.0343