Journal of Grid Computing

, Volume 14, Issue 4, pp 545–557 | Cite as

Using Science Gateways for Bridging the Differences between Research Infrastructures

  • Sandra Gesing
  • Jens Krüger
  • Richard Grunzke
  • Sonja Herres-Pawlis
  • Alexander Hoffmann


Researchers can perform large-scale analyses on diverse computing and data infrastructures such as NGIs (National Grid Infrastructures), XSEDE (Extreme Science and Engineering Discovery Environment) and PRACE (Partnership for Advanced Computing in Europe). Some are national like NGIs and XSEDE, some are international like PRACE and all of them require a more or less restrictive application process to get access to resources. Science gateways integrating diverse infrastructures provide the possibility to re-use methods independent of such underlying infrastructures and thus potentially deliver the technical prerequisite for creating reproducible science. To achieve this goal, science gateways have to be integrated seamlessly with security mechanisms and job, data as well as workflow management of the targeted resources. This paper gives an overview on general findings for porting science gateways as well as the challenges faced for porting the German MoSGrid science gateway (Molecular Simulation Grid) to exploit XSEDE and PRACE infrastructures.


—Science gateways Research infrastructures Security Workflows Reproducibility 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Krüger, J., Grunzke, R., Herres-Pawlis, S., Hoffmann, A., de la Garza, L., Kohlbacher, O., Nagel, W.E., Gesing, S.: Performance Studies on Distributed Virtual Screening. Biomed. Res. Int. 2014(624024), 7. doi: 10.1155/2014/624024
  2. 2.
    Rajasekar, A., Moore, R., Hou, C.-Y., Lee, C.A., Marciano, R., de Torcy, A., Wan, M., Schroeder, W., Chen, S.-Y., Gilbert, L., et al: iRODS primer: Integrated Rule-Oriented Data System, Synthesis Lectures on Information Concepts, Retrieval, and Services, Morgan & Claypool Publishers 2, 1–143 (2010)Google Scholar
  3. 3.
    Fuhrmann, P.: dCache, the commodity cache, IEEE Mass Storage Systems and Technologies (2004)Google Scholar
  4. 4.
    Plankensteiner, K., Prodan, R., Janetschek, M., Fahringer, T., Montagnat, J., Rogers, D., Harvey, I., Taylor, I., Balaskó, A., Kacsuk, P.: Fine-Grain Interoperability of Scientific Workflows in Distributed Computing Infrastructures. J. Grid Comput. 11, 429 (2013).CrossRefGoogle Scholar
  5. 5.
    SHIWA (SHaring Interoperable Workflows for Large-scale Scientic Simulations on Available DCIs). (2016)
  6. 6.
    Gesing, S., Herres-Pawlis, S., Birkenheuer, G., Brinkmann, A., Grunzke, R., Kacsuk, P., Kohlbacher, O., Kozlovszky, M., Krüger, J., Müller-Pfefferkorn, R., Schäfer, P., Steinke, T.: A Science Gateway Getting Ready for Serving the International Molecular Simulation Community. In: Proceedings of Science, PoS(EGICF12-EMITC2)050 (2012)Google Scholar
  7. 7.
    Zhao, J., Gomez-Perez, J.M., Belhajjame, K., Klyne, G., Garcia-Cuesta, E., Garrido, A., Hettne, K., Roos, M., De Roure, D., Goble, C.: Why workflows break understanding and combating decay in Taverna workflows. In: E-Science (e-Science), 2012 IEEE 8th International Conference on, pages 1–9. IEEE (2012)Google Scholar
  8. 8.
    Wolstencroft, K., Haines, R., Fellows, D., Williams, A., Withers, D., Owen, S., Soiland-Reyes, S., Dunlop, I., Nenadic, A., Fisher, P., Bhagat, J., Belhajjame, K., Bacall, F., Hardisty, A., Nieva de la Hidalga, A., Balcazar Vargas, M.P., Sufi, S., Goble, C.: The taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucleic Acids Res. 41(W1), W557—W561 (2013)CrossRefGoogle Scholar
  9. 9.
    Krüger, R., Grunzke, S., Gesing, S., Breuers, A., Brinkmann, L., de la Garza, O., Kohlbacher, M., Kruse, W.E., Nagel, L., Packschies, R., Müller-Pfefferkorn, P., Schäfer, C., Schärfe, T., Steinke, T., Schlemmer, K.D., Warzecha, A.Z., Herres-Pawlis, S.: The MoSGrid Science Gateway – A Complete Solution for Molecular Simulations. J. Chem. Theory Comput. 10(6), 2232–2245 (2014)CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.
    Kacsuk, P., Farkas, Z., Kozlovszky, M., Hermann, G., Balasko, A., Karoczkai, K., Marton, I.: WS-PGRADE/gUSE Generic DCI Gateway Framework for a Large Variety of User Communities. J. Grid Comput. 10, 601–630 (2012). Springer NetherlandsCrossRefGoogle Scholar
  13. 13.
    Liferay: Enterprise open source portal and collaboration software, (2016)
  14. 14.
    Benedyczak, K., Schuller, B., Petrova, M., Rybicki, J., Grunzke, R.: UNICORE 7 - Middleware Services for Distributed and Federated Computing. In: International Conference on High Performance Computing Simulation (HPCS), 2016, acceptedGoogle Scholar
  15. 15.
    Hupfeld, F., Cortes, T.i., Kolbeck, B., Stender, J., Focht, E., Hess, M., Malo, J., Marti, J., Cesario, E.: The XtreemFS Architecture - A Case for Object-based File Systems in Grids. Concurrency and Computation: Practice and Experience 20, 2049–2060 (2008)CrossRefGoogle Scholar
  16. 16.
    Grunzke, R., Gesing, S., Jäkel, R., Nagel, W.E.: Towards Generic Metadata Management in Distributed Science Gateway Infrastructures. In: IEEE/ACM CCGrid 2014(14th International Symposium on Cluster, Cloud and Grid Computing), pp 566–570 (2014)Google Scholar
  17. 17.
    Grunzke, R., Breuers, S., Gesing, S., Herres-Pawlis, S., Kruse, M., Blunk, D., de la Garza, L., Packschies, L., Schäfer, P., Schärfe, C., Schlemmer, T., Steinke, T., Schuller, B., Müller-Pfefferkorn, R., Jäkel, R., Nagel, W.E., Atkinson, M., Krüger, J.: Standards-based Metadata Management for Molecular Simulations, Concurrency and Computation: Practice and Experience, vol. 26 (2014)Google Scholar
  18. 18.
    Noor, W., Schuller, B.: MMF: A flexible framework for metadata management in UNICORE. In: UNICORE Summit 2010 Proceedings, 2010, 5, 51–60Google Scholar
  19. 19.
    Apache Lucene: Java-based indexing and search technology, (2015)
  20. 20.
    Mattmann, C., Zitting, J.: Tika in action, Manning Publications Co. (2011)Google Scholar
  21. 21.
    Gesing, S., Grunzke, R., Krüger, J., Birkenheuer, G., Wewior, M., Schäfer, P., Schuller, B., Schuster, J., Herres-Pawlis, S., Breuers, S., Balaskó, Á., Kozlovszky, M., Fabri, A. S., Packschies, L., Kacsuk, P., Blunk, D., Steinke, T., Brinkmann, A., Fels, G., Müller-Pfefferkorn, R., Jäkel, R., Kohlbacher, O.: A Single Sign-On Infrastructure for Science Gateways on a Use Case for Structural Bioinformatics. J. Grid Comput. 10, 769 (2012)CrossRefGoogle Scholar
  22. 22.
    Samual, T.K., Wan, S., Conveney, P.V., Riedel, M., Memon, S., Gesing, S., Wilkins-Diehr, N.: Overview of XSEDE-PRACE Collaborative Projects in 2014. In: Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure (XSEDE ’15). ACM, New York, NY, USA,, Article 24, 8 pages. doi: 10.1145/2792745.2792769 (2015)
  23. 23.
    Goecks, J., Nekrutenko, A, Taylor, J., The Galaxy Team: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11(8), R86 (2010)CrossRefGoogle Scholar
  24. 24.
    Suresh, M., Gunathilake, L., Herath, C., Tangchaisin, P., Pierce, M., Mattmann, C., Singh, R., et al.: Apache airavata: a framework for distributed applications and computational workflows. In: Proceedings of the 2011 ACM workshop on Gateway computing environments, pp. 21-28. ACM (2011)Google Scholar
  25. 25.
    Kacsuk, P. (ed.): Science Gateways for Distributed Computing Infrastructures: Development Framework and Exploitation by Scientific User Communities, Springer, 2014. pp. 301 (ISBN:978-3-319-11267-1)Google Scholar
  26. 26.
    ENVRIplus, (2016)
  27. 27.
    Kissling, W.D., Hardisty, A., García, E.A., Santamaria, M., De Leo, F., Pesole, G., Freyhof, J., Manset, D., Wissel, S., Konijn, J., Los, W.: Towards global interoperability for supporting biodiversity research on Essential Biodiversity Variables (EBVs). Biodiversity 16, 99–107 (2015)CrossRefGoogle Scholar
  28. 28.
  29. 29.
    Morgan, M., Grimshaw, A.: Genesis II - Standards Based Grid Computing. In Seventh IEEE International Symposium on Cluster Computing and the Grid. IEEE Computer Society, Rio de Janario, Brazil (2007)Google Scholar
  30. 30.
  31. 31.
    XSEDE Science Gateways, (2016)
  32. 32.
    GridChem, (2016)
  33. 33.
    Demeler, B.: UltraScan A Comprehensive Data Analysis Software Package for Analytical Ultracentrifugation Experiments. Modern Analytical Ultracentrifugation: Techniques and Methods. In: Scott, D.J., Harding, S.E., Rowe, A.J. (eds.) . Royal Society of Chemistry (UK) (2005) 210–229Google Scholar
  34. 34.
    ROSIE, (2016)Google Scholar
  35. 35.
    Li, L., Bum-Erdene, K., Baenziger, P.H., Rosen, J.J., Hemmert, J.R., Nellis, J.A., Pierce, M.E., Meroueh, S.O.: BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome. Nucleic Acids Res. 38(Database issue), D765–73 (2010)CrossRefGoogle Scholar
  36. 36.
    Jo, S., Cheng, X., Islam, S.M., Huang, L., Rui, H., Zhu, A., Lee, H.S., Qi, Y., Han, W., Vanommeslaeghe, K., MacKerell Jr, A.D., Benoît Roux, W.I.: CHARMM-GUI PDB Manipulator for Advanced Modeling and Simulations of Proteins Containing Nonstandard Residues. Adv. Protein Chem. Struct. Biol. 96, 235–265 (2014)CrossRefGoogle Scholar
  37. 37.
  38. 38.
    InCommon. (2016)
  39. 39.
  40. 40.
    Jesser, A, Rohrmüller, M, Schmidt, W G, Herres-Pawlis, S: Geometrical and optical benchmarking of copper guanidine–quinoline complexes: Insights from TD-DFT and many-body perturbation theory. J. Comput. Chem. 35, 1–17 (2014). doi: 10.1002/jcc.23449 CrossRefGoogle Scholar
  41. 41.
    Hoffmann, A., Grunzke, R., Herres-Pawlis, S.: Insights into the influence of dispersion correction in the theoretical treatment of guanidine-quinoline copper(I) complexes. J. Comput. Chem. 35, 1943–1950 (2014). doi: 10.1002/jcc.23706 CrossRefGoogle Scholar
  42. 42.
    Hoffmann, A., Rohrmüller, M., Jesser, A., dos Santos Vieira, I., Schmidt, W.G., Herres-Pawlis, S.: Geometrical and optical benchmarking of copper(II) guanidine–quinoline complexes: Insights from TD-DFT and many-body perturbation theory (part II). J. Comput. Chem. 35, 2146–2161 (2014). doi: 10.1002/jcc.23740 CrossRefGoogle Scholar
  43. 43.
    Valiev, M., Bylaska, E.J., Govind, N., Kowalski, K., Straatsma, T.P., Van Dam, H.J.J., Wang, D., Nieplocha, J., Apra, E., Windus, T.L., de Jong, W.A., de Jong, A.: NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations. Comput. Phys. Commun. 181(9), 1477–1489 (2010)CrossRefzbMATHGoogle Scholar
  44. 44.
    Gaussian Inc. Gaussian 03, Revision C.02 (2004)Google Scholar
  45. 45.
    Hoffmann, A., Gesing, S., de la Garza, L., Krüger, J., Grunzke, R., Weingarten, N., Terstyansky, G., Herres-Pawlis, S.: Meta-metaworkflows for Combining Quantum Chemistry and Molecular Dynamics in the MoSGrid Science Gateway. In: IEEE Xplore - Proceedings 6th International Workshop on Science Gateways (IWSG), pp 73–78 (2014)Google Scholar
  46. 46.
    Herres-Pawlis, S., Hoffmann, A., Balasko, A., Kacsuk, P., Birkenheuer, G., Brinkmann, A., de la Garza, L., Krüger, J., Gesing, S., Grunzke, R., Terstyansky, G., Weingarten, N.: Quantum chemical metaworkflows in MoSGrid. Concurrency Computat.: Pract. Exper. 27, 344–57 (2015)CrossRefGoogle Scholar
  47. 47.
    Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D., Hess, B., Lindahl, E.: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7), 845–54 (May 2013)CrossRefGoogle Scholar
  48. 48.
    Kohlbacher, O.: CADDSuite - a workflow-enabled suite of open-source tools for drug discovery 4 (2012). no. Suppl 1, p. O2 +Google Scholar
  49. 49.
    Hildebrandt, A., Dehof, A.K., Rurainski, A., Bertsch, A., Schumann, M., Toussaint, N.C., Moll, A., Stöckel, D., Nickels, S., Mueller, S.C., Lenhof, H.-P., Kohlbacher, O.: BALL–biochemical algorithms library 1.3. BMC Bioinformatics 11(1), 531 (2010)CrossRefGoogle Scholar
  50. 50.
    Hildebrandt, K., Stöckel, D., Fischer, N.M., de la Garza, L., Krüger, J., Nickels, S., Röttig, M., Schärfe, C., Schumann, M., Thiel, P., Lenhof, H.-P., Kohlbacher, O., Hildebrandt, A.: ballaxy: web services for structural bioinformatics. Bioinformatics (2014)Google Scholar
  51. 51.
    O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., Hutchison, G.R.: Open Babel: An open chemical toolbox. J. Cheminform. 3(1), 33 (2011)CrossRefGoogle Scholar
  52. 52.
    Trott, O., Olson, A.J.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem. 31, 455–461 (2010)Google Scholar
  53. 53.
    Rarey, M., Kramer, B., Lengauer, T., Klebe, G.: A Fast Flexible Docking Method Using an Incremental Construction Algorithm. J. Mol. Biol. 261, 470–489 (1996)CrossRefGoogle Scholar
  54. 54.
    Landesman, B.: Seeing Standards: A Visualization of the Metadata Universe, Technical Services Quarterly,, 2011, 28, 459–460
  55. 55.
    Grunzke, R., Gesing, S., Jäkel, R., Nagel, W.E.: Towards Generic Metadata Management in Distributed Science Gateway Infrastructures. In: IEEE/ACM CCGrid 2014(14th International Symposium on Cluster, Cloud and Grid Computing), pp 566–570 (2014)Google Scholar
  56. 56.
    Grunzke, R., Breuers, S., Gesing, S., Herres-Pawlis, S., Kruse, M., Blunk, D., de la Garza, L., Packschies, L., Schäfer, P., Schärfe, C., Schlemmer, T., Steinke, T., Schuller, B., Müller-Pfefferkorn, R., Jäkel, R., Nagel, W.E., Atkinson, M., Krüger, J.: Standards-based Metadata Management for Molecular Simulations, Concurrency and Computation: Practice and Experience, vol. 26 (2014)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Sandra Gesing
    • 1
  • Jens Krüger
    • 2
  • Richard Grunzke
    • 3
  • Sonja Herres-Pawlis
    • 4
  • Alexander Hoffmann
    • 4
  1. 1.University of Notre Dame, 123 Information Technology CenterNotre DameUSA
  2. 2.Applied Bioinformatics TübingenUniversity of TübingenTübingenGermany
  3. 3.Center for Information Services and High Performance ComputingTechnische Universität DresdenDresdenGermany
  4. 4.Institut für Anorganische ChemieRheinisch-Westfälische Technische Hochschule AachenAachenGermany

Personalised recommendations