Advertisement

SimScience 2017: Simulation Science pp 176-192 | Cite as

Transparent Model-Driven Provisioning of Computing Resources for Numerically Intensive Simulations

  • Fabian Korte
  • Alexander Bufe
  • Christian Köhler
  • Gunther Brenner
  • Jens Grabowski
  • Philipp Wieder
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 889)

Abstract

Many simulations require large amounts of computing power to be executed. Traditionally, the computing power is provided by large high performance computing clusters that are solely built for this purpose. However, modern data centers do not only provide access to these high performance computing systems, but also offer other types of computing resources e.g., cloud systems, grid systems, or access to specialized computing resources, such as clusters equipped with accelerator hardware. Hence, the researcher is confronted with the choice of picking a suitable computing resource type for his simulation and acquiring the knowledge on how to access and manage his simulation on the resource type of choice. This is a time consuming and cumbersome process and could greatly benefit from supportive tooling. In this paper, we introduce a framework that allows to describe the simulation application in a resource-independent manner. It furthermore helps to select a suitable resource type according to the requirements of the simulation application and to automatically provision the required computing resources. We demonstrate the feasibility of the approach by providing a case study from the area of fluid mechanics.

Notes

Acknowledgements

We thank the Simulationswissenschaftliches Zentrum Clausthal-Göttingen (SWZ) for financial support.

References

  1. 1.
    Ardagna, D., et al.: MODAClouds: a model-driven approach for the design and execution of applications on multiple clouds. In: 2012 4th International Workshop on Modeling in Software Engineering (MISE), pp. 50–56. IEEE, June 2012.  https://doi.org/10.1109/MISE.2012.6226014
  2. 2.
    Arkın, E., Tekinerdogan, B., İmre, K.M.: Model-driven approach for supporting the mapping of parallel algorithms to parallel computing platforms. In: Moreira, A., Schätz, B., Gray, J., Vallecillo, A., Clarke, P. (eds.) MODELS 2013. LNCS, vol. 8107, pp. 757–773. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41533-3_46CrossRefGoogle Scholar
  3. 3.
    Bunch, C., Chohan, N., Krintz, C., Shams, K.: Neptune: a domain specific language for deploying HPC software on cloud platforms. In: Proceedings of the 2nd International Workshop on Scientific Cloud Computing, pp. 59–68. ScienceCloud 2011. ACM (2011).  https://doi.org/10.1145/1996109.1996120
  4. 4.
    Deiterding, R.: AMROC - Adaptive Mesh Refinement in Object-Oriented C++ (2017). http://www.vtf.website/asc/wiki/bin/view/Amroc/WebHome. Accessed 8 Nov 2017
  5. 5.
    Di Martino, B., Petcu, D., Cossu, R., Goncalves, P., Máhr, T., Loichate, M.: Building a mosaic of clouds. In: Guarracino, M.R., et al. (eds.) Euro-Par 2010. LNCS, vol. 6586, pp. 571–578. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21878-1_70CrossRefGoogle Scholar
  6. 6.
    Flissi, A., Dubus, J., Dolet, N., Merle, P.: Deploying on the grid with deployware. In: 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID), pp. 177–184. IEEE (2008).  https://doi.org/10.1109/CCGRID.2008.59
  7. 7.
    Guillén, J., Miranda, J., Murillo, J.M., Canal, C.: A service-oriented framework for developing cross cloud migratable software. J. Syst. Softw. 86(9), 2294–2308 (2013).  https://doi.org/10.1016/j.jss.2012.12.033CrossRefGoogle Scholar
  8. 8.
    Hofmann, S., Bufe, A., Brenner, G., Turek, T.: Pressure drop study on packings of differently shaped particles in milli-structured channels. Chem. Eng. Sci. 155, 376–385 (2016).  https://doi.org/10.1016/j.ces.2016.08.011CrossRefGoogle Scholar
  9. 9.
    IBM Corporation: Introduction to IBM Platform LSF. https://www.ibm.com/support/knowledgecenter/SSETD4_9.1.2/lsf_foundations/lsf_introduction_to.html. Accessed 8 Nov 2017
  10. 10.
    Limmer, S., Srba, M., Fey, D.: Performance investigation and tuning in the interoperable Cloud4E platform. In: Lopes, L., et al. (eds.) Euro-Par 2014. LNCS, vol. 8806, pp. 85–96. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-14313-2_8CrossRefGoogle Scholar
  11. 11.
    Nyrén, R., Edmonds, A., Papaspyrou, A., Metsch, T., Parák, B.: Open Cloud Computing Interface - Core, September 2016. http://ogf.org/documents/GFD.221.pdf
  12. 12.
    OASIS: Topology and Orchestration Specification for Cloud Applications (TOSCA) 1.0, November 2013. http://docs.oasis-open.org/tosca/TOSCA/v1.0/os/TOSCA-v1.0-os.html. Accessed 8 Nov 2017
  13. 13.
    OASIS: TOSCA Simple Profile in YAML Version 1.1, August 2016. http://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.1/csprd01/TOSCA-Simple-Profile-YAML-v1.1-csprd01.html. Accessed 8 Nov 2017
  14. 14.
    Ober, I., Palyart, M., Bruel, J.M., Lugato, D.: On the use of models for high-performance scientific computing applications: an experience report. Softw. Syst. Model. 17(1), 319–342 (2018).  https://doi.org/10.1007/s10270-016-0518-0
  15. 15.
    Object Management Group: Model Driven Architecture. http://www.omg.org/cgi-bin/doc?ormsc/14-06-01.pdf. Accessed 8 Nov 2017
  16. 16.
    Qasha, R., Cala, J., Watson, P.: Towards automated workflow deployment in the cloud using TOSCA. In: 2015 IEEE 8th International Conference on Cloud Computing, pp. 1037–1040. IEEE (2015).  https://doi.org/10.1109/CLOUD.2015.146
  17. 17.
    Quinton, C., Romero, D., Duchien, L.: SALOON: a platform for selecting and configuring cloud environments. Softw. Pract. Experience 46(1), 55–78 (2016).  https://doi.org/10.1002/spe.2311
  18. 18.
    Soldani, J., Binz, T., Breitenbücher, U., Leymann, F., Brogi, A.: ToscaMart: a method for adapting and reusing cloud applications. J. Syst. Softw. 113, 395–406 (2016).  https://doi.org/10.1016/j.jss.2015.12.025CrossRefGoogle Scholar
  19. 19.
    Steinberg, D., Budinsky, F., Paternostro, M., Merks, E.: EMF: Eclipse Modeling Framework 2.0, 2nd edn. Addison-Wesley Professional, Amsterdam (2009)Google Scholar
  20. 20.
    The Eclipse Foundation: Epsilon. https://eclipse.org/epsilon/. Accessed 8 Nov 2017
  21. 21.
    Vukojevic-Haupt, K., Haupt, F., Leymann, F.: On-demand provisioning of workflow middleware and services into the cloud: an overview. Computing 99(2), 147–162 (2017).  https://doi.org/10.1007/s00607-016-0521-x
  22. 22.
    Vukojevic-Haupt, K., Haupt, F., Leymann, F., Reinfurt, L.: Bootstrapping complex workflow middleware systems into the cloud. In: 2015 IEEE 11th International Conference on e-Science, pp. 126–135. IEEE (2015).  https://doi.org/10.1109/eScience.2015.69
  23. 23.
    Zalila, F., Challita, S., Merle, P.: A model-driven tool chain for OCCI. In: Panetto, H., et al. (eds.) On the Move to Meaningful Internet Systems. OTM 2017 Conferences, pp. 389–409. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69462-7_26

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fabian Korte
    • 1
  • Alexander Bufe
    • 2
  • Christian Köhler
    • 3
  • Gunther Brenner
    • 2
  • Jens Grabowski
    • 1
  • Philipp Wieder
    • 3
  1. 1.Institute of Computer ScienceUniversity of GoettingenGoettingenGermany
  2. 2.Institute of Applied MechanicsClausthal University of TechnologyClausthal-ZellerfeldGermany
  3. 3.Gesellschaft fuer wissenschaftliche Datenverarbeitung mbH Goettingen (GWDG)GoettingenGermany

Personalised recommendations