D-Mason on the Cloud: An Experience with Amazon Web Services

  • Michele Carillo
  • Gennaro Cordasco
  • Flavio Serrapica
  • Carmine SpagnuoloEmail author
  • Przemysaw Szufel
  • Luca Vicidomini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10104)


D-Mason framework is a parallel version of the Mason library for writing and running Agent-based simulations – a class of models that, by simulating the behavior of multiple agents, aims to emulate and/or predict complex phenomena. D-Mason has been conceived to harness the amount of unused computing power available in common installations like educational laboratory. Then the focus moved to dedicated installation, such as massively parallel machines or supercomputing centers. In this paper, D-Mason takes another step forward and now it can be used on a cloud environment.

The goal of the paper is twofold. Firstly, we are going to present D-Mason on the cloud – a D-Mason extension that, starting from an IaaS (Infrastructure as a Service) abstraction, and exploiting Amazon Web Services and StarCluster, provides a SIMulation-as-a-Service (SIMaaS) abstraction that simplifies the process of setting up and running distributed simulations in the cloud. Secondly, an additional goal of the paper is to assess computational and economic efficiency of running distributed multi-agent simulations on the Amazon Web Services EC2 instances. The computational speed and costs of an EC2 cluster will be compared against an on-site HPC cluster.


Agent-Based simulation Models Cloud computing D-Mason Parallel computing Distributed systems High performance computing 


  1. 1.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gen. Comput. Syst. 25(6), 599–616 (2009). CrossRefGoogle Scholar
  2. 2.
    Cordasco, G., Chiara, R., Fulgido, F., Vitale, M.F.: Supporting the exploratory nature of simulations in D-Mason. In: Mey, D., et al. (eds.) Euro-Par 2013. LNCS, vol. 8374, pp. 555–564. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-54420-0_54 CrossRefGoogle Scholar
  3. 3.
    Cordasco, G., Chiara, R., Mancuso, A., Mazzeo, D., Scarano, V., Spagnuolo, C.: A framework for distributing agent-based simulations. In: Alexander, M., et al. (eds.) Euro-Par 2011. LNCS, vol. 7155, pp. 460–470. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29737-3_51 CrossRefGoogle Scholar
  4. 4.
    Cordasco, G., De Chiara, R., Mancuso, A., Mazzeo, D., Scarano, V., Spagnuolo, C.: Bringing together efficiency and effectiveness in distributed simulations: The experience with D-MASON. SIMULATION: Trans. Soc. Model. Simul. Int. 89(10), 1236–1253 (2013)CrossRefGoogle Scholar
  5. 5.
    Cordasco, G., Milone, F., Spagnuolo, C., Vicidomini, L.: Exploiting D-Mason on parallel platforms: a novel communication strategy. In: Lopes, L., et al. (eds.) Euro-Par 2014. LNCS, vol. 8805, pp. 407–417. Springer, Cham (2014). doi: 10.1007/978-3-319-14325-5_35 Google Scholar
  6. 6.
    Cosenza, B., Cordasco, G., De Chiara, R., Scarano, V.: Distributed load balancing for parallel agent-based simulations. In: Proceedings of the 19th International Euromicro Conference on Parallel, Distributed, and Network-Based Processing, (PDP 2011), pp. 62–69 (2011)Google Scholar
  7. 7.
    D’Angelo, G., Marzolla, M.: New trends in parallel and distributed simulation: from many-cores to cloud computing. Simul. Model. Pract. Theory 49, 320–335 (2014). CrossRefGoogle Scholar
  8. 8.
    Fujimoto, R., Malik, A., Park, A.: Parallel and distributed simulation in the cloud. Int. Simul. Mag. Soc. Model. Simul. 3(1) (2010)Google Scholar
  9. 9.
    López-Paredes, A., Edmonds, B., Klugl, F.: Editorial of the special issue: agent based simulation of complex social systems. SIMULATION: Trans. Soc. Model. Simul. Int. 88(1), 4–6 (2012)CrossRefGoogle Scholar
  10. 10.
    Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K.: MASON: a new multi-agent simulation toolkit. In: Proceedings of the 2004 SwarmFest Workshop (2004)Google Scholar
  11. 11.
    Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: a multiagent simulation environment. Simulation 81(7), 517–527 (2005). CrossRefGoogle Scholar
  12. 12.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. SIGGRAPH Comput. Graph. 21(4), 25–34 (1987). CrossRefGoogle Scholar
  13. 13.
  14. 14.
    D-MASON Official GitHub Repository. Accessed May 2016
  15. 15.
  16. 16.
  17. 17.
  18. 18.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michele Carillo
    • 1
  • Gennaro Cordasco
    • 2
  • Flavio Serrapica
    • 1
  • Carmine Spagnuolo
    • 1
    Email author
  • Przemysaw Szufel
    • 3
  • Luca Vicidomini
    • 1
  1. 1.ISISLab–Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly
  2. 2.Dipartimento di PsicologiaSeconda Università degli Studi di NapoliCasertaItaly
  3. 3.Warsaw School of Economics (WSE - SGH)WarsawPoland

Personalised recommendations