Implementing a Grid/Cloud eScience Infrastructure for Hydrological Sciences

  • Gen-Tao Chiang
  • Martin T. Dove
  • C. Isabella Bovolo
  • John Ewen
Part of the Computer Communications and Networks book series (CCN)


The objective of this chapter is to describe building an eScience infrastructure suitable for use with environmental sciences and especially with hydrological science applications. The infrastructure allows a wide range of hydrological problems to be investigated and is particularly suitable for either computationally intensive or multiple scenario applications. To accomplish this objective, this research discovered the shortcomings of current grid infrastructures for hydrological science and developed missing components to fill this gap. In particular, there were three primary areas which needed work: first, integrating data and computing grids; second, visualization of geographic information from grid outputs; and third, implementing hydrological simulations based on this infrastructure. This chapter focuses on the first area, which is focusing on grid infrastructure system integration and development. A grid infrastructure, which consists of a computing and a data grid, has been built. In addition, the computing grid has been extended to utilize the Amazon EC2 cloud computing resources. Users can conduct a complete simulation job life cycle from job submission, and data management to metadata management based on the tools available in the infrastructure.


Cloud Computing Grid Resource Wellcome Trust Sanger Institute Storage Resource Broker Grid Security 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.


  1. 1.
    Hey T, Trefethen A (2003) e-Science and its implications. Phil. Trans. Roy Soc (A) 361:1809–1825. CrossRefGoogle Scholar
  2. 2.
    zalay A, Gray J (2006) 2020 Computing: Science in an exponential world. Nature 440:413–414.CrossRefGoogle Scholar
  3. 3.
    Clery D (2006) Can Grid Computing Help Us Work Together. Science 303:433–434.CrossRefGoogle Scholar
  4. 4.
    Foster I, Kesselman C, Tuecke S (2001) The Anatomy of the Grid: Enabling Scalable Virtual Organizations. The International Journal of Supercomputer Applications 15(3):200–222.CrossRefGoogle Scholar
  5. 5.
    Dove, M., Walker, A., White, T., Bruin, R., Austen, K., Frame, I., Chiang, GT., Murray-Rust, P., Tyer, R., Couch, P., Kleese van Dam, K., Parker, SC., Marmier, C., Arrouvel, C. Usable grid infrastructures: practical experiences from the eMinerals project. in UK e-Science All Hands Meeting. 2007. Nottingham, UK. pp. 48–55.Google Scholar
  6. 6.
    Google Trend (2010) Available via Google. {­puting. Cited 10 Feb 2010}.
  7. 7.
    Renard P, Badoux V (2009) Grid Computing for Earth Science. Eos Trans. AGU 90(14):117–119.CrossRefGoogle Scholar
  8. 8.
    Anderson D (2004) BOINC: A System for Public-Resource Computing and Storage, in 5th IEEE/ACM Inernational Workshop on Grid Computing. 2004: Pittsburgh, USA.Google Scholar
  9. 9.
    Allen M (1999) Do-it-yourself climate prediction. Nature 401:642. CrossRefGoogle Scholar
  10. 10.
    Frame DJ, Aina T, Christensen CM, Faull NE, Knight, SHE, Piani, C, Rosier, SM, Yamazaki, K, Yamazaki, Y, Allen, M (2009) The BBC climate change experimnet: dedsign of the coupled model ensemble. Phil. Trans. R.Soc. A 367(1890):855–870. MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Lawrence, B.N., Cramer, R., Gutierrez, M., Kleese van Dam, K., Kondapalli, S., Latham, S., Lowry, R., O’Neill, K., and Woolf, A. The NERC DataGrid Prototype. in UK e-Science All Hands Meeting 2003. Notingham, UK.
  12. 12.
    Gahegana, M., Luob, J., Weaver, S.D., Pike, W., Banchuenb, T., (2009) Connecting GEON: Making sense of the myriad resources, researchers and concepts that comprise a geoscience cyberinfrastructure. Computers & Geosciences 35: pp. 836–854.CrossRefGoogle Scholar
  13. 13.
    Dove M, De Leeuw NH (2005) Grid computing and molecular simulations: the vision of the eMinerals project. Molecular Simulation 31(5):297–301.CrossRefGoogle Scholar
  14. 14.
    Mechoso C, Ma C-C, Farrara J, Spahr J, Moore R (1993) Parallization and distribution of a coupled atmosphere-ocean general circulation model. Monthly Weather Review 121:2062 CrossRefGoogle Scholar
  15. 15.
    Saltelli A, Ratoo M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global Sensitivity Analysis. In: Saltelli A (ed) Global Sensitivity Analysis, John Willey & Sons, New York.Google Scholar
  16. 16.
    Papakhian, M., (1998) Comparing Job-Management Systems: The User’s Perspective. IEEE Computational Science & Engineering 5(2): pp. 4–9. CrossRefGoogle Scholar
  17. 17.
    Thain D, Tannenbaum T, Livny M (2005) Distributed Computing in Practice: The Condor Experience. Concurrency - Practice and Experience 17(2–4):323–356.CrossRefGoogle Scholar
  18. 18.
    Foster I, Kesselman C (1997) Globus:A Metacomputing Infrastructure Tookit. International Journal of Supercomputer Applications 11(2):115–128.CrossRefGoogle Scholar
  19. 19.
    Butler R, Engert D, Foster I, Kesselman I, Tuecke S (2000) A National-Scale Authentication Infrastructure. IEEE Computer 33(12):60–66.Google Scholar
  20. 20.
    Montero, R.S., Huedo, E., Llorente, I.M., (2006) Grid Scheduling Infrastucture based on the GridWay Meta-scheduler. IEEE Technical Committee on Scalable Computing (TCSC).Google Scholar
  21. 21.
    Bruin R, White TOH, Walker AM, Austen KF, Dove MT, Tyer RP, Couch PA, Todorov IT, Blanchard MO (2006) Job submission to grid computing environment. in UK e-Science All Hands Meeting 2006. Nottingham, UK. pp. 754–761.Google Scholar
  22. 22.
    Bruin R (2006) Development of a grid computing infrastructure to support combinatorial simulation studies of pollutant organic molecules on mineral surfaces, PhD Thesis, Department of Earth Sciences, University of Cambridge.Google Scholar
  23. 23.
    Couch, P., Sherwood, P., Sufi, S., Todorov, I., Allan, R., Knowles, P., Bruin, R., Dove, M., and Murray-rust, P. Towards data integration for computational chemistry. in UK e-Science All Hands Meeting 2005. Nottingham, UK pp. 19–22.Google Scholar
  24. 24.
    Chervenak A, Foster I, Kesselman C, Salisbury C, Tuecke S (2001) The Data Grid: Towards and Architecture for the Distributed Management and Analysis of Large Scientific Datasets. Journal of Network and Computer Applications 23:187–200.CrossRefGoogle Scholar
  25. 25.
    Baru, C., Moore, R., Rajasekar, A., Wan, M. The SDSC Storage Resource Broker. in IBM Toronto Centre for Advanced Studies Conference (CASCON’98) 1998. Toronto, Canada pp. 1–12.
  26. 26.
    Ewen J, Parkin G, O’Connell PE (2000) SHETRAN: distributed river basin flow and transport modeling system. American Society of Civil Engineers Journal of Hydrologic Engineering 5(3):250–258.Google Scholar
  27. 27.
    Chiang G-T, White TO, Dove MT, Bovolo CI, Ewen J (2011) Geo-visualization Fortran Library. Computers & Geosciences, doi:10.1016/j.cageo.2010.04.012 Google Scholar
  28. 28.
    Chiang G-T, White TO, Dove MT (2009) Geospatial visualization tool kit for scientists using Fortran. Eos Trans. AGU 90(29):249–250.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Gen-Tao Chiang
    • 1
  • Martin T. Dove
    • 2
  • C. Isabella Bovolo
    • 3
  • John Ewen
    • 3
  1. 1.Wellcome Trust Sanger InstituteCambridgeUK
  2. 2.Department of Earth SciencesUniversity of CambridgeCambridgeUK
  3. 3.School of Civil Engineering and GeosciencesUniversity of Newcastle upon TyneNewcastle upon TyneUK

Personalised recommendations