Integration of an Event-Based Simulation Framework into a Scientific Workflow Execution Environment for Grids and Clouds

  • Simon Ostermann
  • Kassian Plankensteiner
  • Daniel Bodner
  • Georg Kraler
  • Radu Prodan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6994)


The utilisation of Grid and Cloud-based computing environments for solving scientific problems has become an increasingly used practice in the last decade. To ease the use of these global distributed resources, sophisticated middleware systems have been developed, enabling the transparent execution of applications by hiding low-level technology details from the user. The ASKALON environment is such a system, which supports the development and execution of distributed applications such as scientific workflows or parameter studies in Grid and Cloud computing environments. On the other hand, simulation is a widely accepted approach to analyse and further optimise the behaviour of software systems. Beside the advantage of enabling repeatable deterministic evaluations, simulations are able to circumvent the difficulties in setting up and operating multi-institutional Grid systems, thus providing a lightweight simulated distributed environment on a single machine. In this paper, we present the integration of the GroudSim Grid and Cloud event-based simulator into the ASKALON environment. This enables system, application developers, and users to perform simulations using their accustomed environment, thereby benefiting from the combination of an established real-world platform and the advantages of a simulation.


Execution Time Cloud Computing Cloud Resource Simulation Framework Cloud Infrastructure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Bell, W.H., Cameron, D.G., Millar, A.P., Capozza, L., Stockinger, K., Zini, F.: Optorsim: A Grid Simulator for Studying Dynamic Data Replication Strategies. Journal of High Performance Computing Applications 17(4), 403–416 (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Calheiros, R.N., Ranjan, R., Rose, C.A.F.D., Buyya, R.: CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services. Computing Research Repository abs/0903.2525 (2009)Google Scholar
  3. 3.
    Cao, J., Jarvis, S.A., Saini, S., Nudd, G.R.: GridFlow: Workflow Management for Grid Computing. In: IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 198–205. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  4. 4.
    Casanova, H.: Simgrid: A Toolkit for the Simulation of Application Scheduling. In: First IEEE International Symposium on Cluster Computing and the Grid, Brisbane, Australia, May 15-18, pp. 430–441. IEEE Computer Society, Los Alamitos (2001)CrossRefGoogle Scholar
  5. 5.
    Dumitrescu, C., Foster, I.T.: GangSim: A simulator for Grid scheduling studies. In: 5th International Symposium on Cluster Computing and the Grid (CCGrid 2005), Cardiff, UK, May 9-12, pp. 1151–1158. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  6. 6.
    Fahringer, T., Prodan, R., Duan, R., Nerieri, F., Podlipnig, S., Qin, J., Siddiqui, M., Truong, H.L., Villazón, A., Wieczorek, M.: ASKALON: A Grid application development and computing environment. In: Proceedings of 6th IEEE/ACM International Conference on Grid Computing (GRID 2005), Seattle, Washington, USA, November 13-14, pp. 122–131. IEEE, Los Alamitos (2005)Google Scholar
  7. 7.
    Hirales-Carbajal, A., Tchernykh, A., Röblitz, T., Yahyapour, R.: A Grid simulation framework to study advance scheduling strategies for complex workflow applications. In: Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1–8. IEEE, Los Alamitos (2010)Google Scholar
  8. 8.
    Iosup, A.: The Grid Workloads Archive. Future Generation Computer Systems 24(7), 672–686 (2008)CrossRefGoogle Scholar
  9. 9.
    McNab, R., Howell, F.: Using Java for Discrete Event Simulation. In: Twelfth UK Computer and Telecommunications Performance Engineering Workshop (UKPEW), pp. 219–228. Univ. of Edinburgh, Edinburgh (1996)Google Scholar
  10. 10.
    Ostermann, S., Plankensteiner, K., Prodan, R., Fahringer, T.: GroudSim: An Event-based Simulation Framework for Computational Grids and Clouds. In: CoreGRID/ERCIM Workshop on Grids and Clouds. Springer Computer Science Editorial, Ischia (2010)Google Scholar
  11. 11.
    Ostermann, S., Prodan, R., Fahringer, T.: Extended Grids with Cloud Resource Management for Scientific Computing. In: Grid 2009: IEEE/ACM International Conference on Grid Computing, pp. 42–59 (October 2009)Google Scholar
  12. 12.
    Ostermann, S., Prodan, R., Fahringer, T.: Dynamic Cloud Provisioning for Scientific Grid Workflows. In: The 11th ACM/IEEE International Conference on Grid Computing (Grid 2010), pp. 97–104 (October 2010)Google Scholar
  13. 13.
    Sotomayor, B., Childers, L.: Globus Toolkit 4 Programming Java Services. Morgan Kaufman, San Francisco (2006)Google Scholar
  14. 14.
    Sulistio, A., Cibej, U., Venugopal, S., Robic, B., Buyya, R.: A toolkit for modelling and simulating data Grids: an extension to GridSim. Concurrency and Computation: Practice and Experience 20(13), 1591–1609 (2008)CrossRefGoogle Scholar
  15. 15.
    Xia, E., Jurisica, I., Waterhouse, J.: CasSim: A top-level-simulator for Grid scheduling and applications. In: Erdogmus, H., Stroulia, E., Stewart, D.A. (eds.) CASCON, pp. 353–356. IBM (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Simon Ostermann
    • 1
  • Kassian Plankensteiner
    • 1
  • Daniel Bodner
    • 1
  • Georg Kraler
    • 1
  • Radu Prodan
    • 1
  1. 1.Institute of Computer ScienceUniversity of InnsbruckAustria

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