Logo PTI Logo FedCSIS

Communication Papers of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 45

Flexible and Scalable Results Collecting in Distributed Spatial Simulations

, ,

DOI: http://dx.doi.org/10.15439/2025F8243

Citation: Communication Papers of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 45, pages 9–17 ()

Full text

Abstract. Among many distributed spatial simulation systems each has its own approach to the problem of results collecting and analysis. The volume of results can be huge, while not all results are finally needed. The presented solution is to provide a unified form of defining the range of data to be collected and the methods for efficiently collecting them during the simulation runtime. Simulation results can be represented as a stream of records, where every record has the same structure. This observation means, that simulation can specify one or more data schema, equivalent of the $CREATE TABLE$ command in an SQL database. Then data selection and analysis comes down to writing proper $SELECT$ statements. The paper describes three main parts of the proposed, SQL-inspired results collecting method: parsing and query analysis, distributed computing and integrating all parts together. The method has been integrated with the $HiPUTS$, a distributed urban traffic simulator.

References

  1. R. De Nicola, L. Di Stefano, O. Inverso, and S. Valiani, “Modelling flocks of birds from the bottom up,” in Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, T. Margaria and B. Steffen, Eds. Cham: Springer Nature Switzerland, 2022. ISBN 978-3-031-19759-8 pp. 82–96.
  2. M. De Iuliis, E. Battegazzorre, M. Domaneschi, G. P. Cimellaro, and A. G. Bottino, “Large scale simulation of pedestrian seismic evacuation including panic behavior,” Sustainable Cities and Society, vol. 94, p. 104527, 2023. https://doi.org/10.1016/j.scs.2023.104527. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2210670723001385
  3. P. Topa, Łukasz Faber, J. Tyszka, and M. Komosinski, “Modelling ecology and evolution of foraminifera in the agent-oriented distributed platform,” Journal of Computational Science, vol. 18, pp. 69–84, 2017. https://doi.org/10.1016/j.jocs.2016.07.009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877750316301168
  4. S. S. S. M. Qadri, M. A. Gökçe, and E. Öner, “State-of-art review of traffic signal control methods: challenges and opportunities,” European transport research review, vol. 12, pp. 1–23, 2020.
  5. M. B. K. Kubiak and R. Długosz, “Solutions for planning smart hybrid public transportation system–poznan agglomeration as a case study of satellite towns’ connections,” in Communication Papers of the 2019 Federated Conference on Computer Science and Information Systems, 2019, p. 67.
  6. P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in 2018 21st international conference on intelligent transportation systems (ITSC). IEEE, 2018, pp. 2575–2582.
  7. G. Cordasco, V. Scarano, and C. Spagnuolo, “Distributed mason: A scalable distributed multi-agent simulation environment,” Simulation Modelling Practice and Theory, vol. 89, pp. 15–34, 2018. https://doi.org/10.1016/j.simpat.2018.09.002. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1569190X18301230
  8. M. Holcombe, S. Coakley, and R. Smallwood, “A general framework for agent-based modelling of complex systems,” in Proceedings of the 2006 European conference on complex systems, vol. 1. European Complex Systems Society Paris, France, 2006.
  9. 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 Euro-Par 2015: Parallel Processing Workshops: Euro-Par 2015 International Workshops, Vienna, Austria, August 24-25, 2015, Revised Selected Papers 21. Springer, 2015, pp. 454–465.
  10. X. Rubio-Campillo, “Pandora: a versatile agent-based modelling platform for social simulation,” Proceedings of SIMUL 2014, The Sixth International Conference on Advances in System Simulation, pp. 29–34, 2014.
  11. P. A. Wilcox, A. G. Burger, and P. Hoare, “Advanced distributed simulation: a review of developments and their implication for data collection and analysis,” Simulation Practice and Theory, vol. 8, no. 3, pp. 201–231, 2000. https://doi.org/10.1016/S0928-4869(00)00023-9. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0928486900000239
  12. S. S. Y. Xu and F. Ciarallo, “An agent-based data collection architecture for distributed simulations,” International Journal of Modelling and Simulation, vol. 24, no. 2, pp. 55–64, 2004.
  13. E. Kaya and F. E. Sevilgen, “A fully distributed data collection method for hla based distributed simulations,” in Proceedings of the 2009 Summer Computer Simulation Conference, 2009, pp. 337–347.
  14. K.-T. Yao, R. F. Lucas, C. E. Ward, G. Wagenbreth, and T. D. Gottschalk, “Data analysis for massively distributed simulations,” in Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), 2009, pp. 2–32.
  15. Y. Wu and G. Gong, “A fully distributed collection technology for mass simulation data,” 06 2013. https://dx.doi.org/10.1109/ICCIS.2013.438 pp. 1679–1683.
  16. H. Chen, K. Yang, S. G. Rizzo, G. Vantini, P. Taylor, X. Ma, and S. Chawla, “Qarsumo: a parallel, congestion-optimized traffic simulator,” in Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 2020, pp. 578–588.
  17. A. Horni, K. Nagel, and K. W. Axhausen, “Introducing matsim,” in The multi-agent transport simulation MATSim. Ubiquity Press, 2016, pp. 3–7.
  18. K. Ramamohanarao, H. Xie, L. Kulik, S. Karunasekera, E. Tanin, R. Zhang, and E. B. Khunayn, “SMARTS: Scalable microscopic adaptive road traffic simulator,” ACM Trans. on Intelligent Systems and Technology (TIST), vol. 8, no. 2, pp. 1–22, 2016.
  19. W. T. Mateusz Najdek, Natalia Brzozowska, “Hiputs: Super-scalable simulation of microscopic continuous urban traffic model,” in Proceedings of the 39th ECMS International Conference on Modelling and Simulation, 2025, pp. 476–485.
  20. Y. Shafranovich, “Common Format and MIME Type for Comma-Separated Values (CSV) Files,” RFC 4180, Oct. 2005. [Online]. Available: https://www.rfc-editor.org/info/rfc4180
  21. S. Melnik, A. Gubarev, J. J. Long, G. Romer, S. Shivakumar, M. Tolton, and T. Vassilakis, “Dremel: interactive analysis of web-scale datasets,” Proceedings of the VLDB Endowment, vol. 3, no. 1-2, pp. 330–339, 2010.
  22. D. Vohra and D. Vohra, “Apache parquet,” Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools, pp. 325–335, 2016.