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Stroll: A Universal Filesystem-Based Interface for Seamless Task Deployment in Grid Computing

  • Abdulrahman Azab
  • Hein Meling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7272)

Abstract

Developing applications for solving compute intensive problems is not trivial. Despite availability of a range of Grid computing platforms, domain specialists and scientists only rarely take advantage of these computing facilities. One reason for this is the complexity of Grid computing, and the need to learn a new programming environment to interact with the Grid. Typically, only a few programming languages are supported, and often scientists use special-purpose languages that are not supported by most Grid platforms. Moreover, users cannot easily deploy their compute tasks to multiple Grid platforms without rewriting their program to use different task submission interfaces.

In this paper we present Stroll, a universal filesystem-based interface for seamless task submission to one or more Grid facilities. Users interact with the Grid through simple read and write filesystem commands. Stroll allows all categories of users to submit and manage compute tasks both manually, and from within their programs, which may be written in any language. Stroll has been implemented on Windows and Linux, and we demonstrate that we can submit the same compute tasks to both Condor and Unicore clusters. Our evaluation shows the overhead of Stroll to negligible. Comparing the code complexity of a Stroll compute task with command-line clients and Grid APIs show that Stroll can eliminated up to 95% of the complexity.

Keywords

Code Complexity Cyclomatic Complexity Virtual Storage Grid Task Grid Access 
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.

References

  1. 1.
    Domingues, P., Marques, P., Silva, L.: Resource usage of windows computer laboratories. In: ICPP Workshops 2005, pp. 469–476 (2005)Google Scholar
  2. 2.
    Anderson, D.P.: Public Computing: Reconnecting People to Science. In: Shared Knowledge and the Web, Madrid, Spain (November 2003)Google Scholar
  3. 3.
    McConnell, B.: Beyond Contact: A Guide to SETI and Communicating with Alien Civilizations. O’Reilly (2001)Google Scholar
  4. 4.
    Bijsterbosch, M., et al.: DRIVER, Digital Repository Infrastructure Vision for European Research II. Technology Watch Report (December 2008)Google Scholar
  5. 5.
    Filesystem in userspace, http://fuse.sourceforge.net/
  6. 6.
    Callback File System (2011), http://www.eldos.com/cbfs/
  7. 7.
    Litzkow, M., Livny, M., Mutka, M.: Condor - a hunter of idle workstations. In: ICDCS (June 1988)Google Scholar
  8. 8.
    Erwin, D.W., Snelling, D.F.: Unicore: A grid computing environment. In: ECPP, pp. 825–834 (2001)Google Scholar
  9. 9.
    McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. (1976)Google Scholar
  10. 10.
    Halstead, M.H.: Elements of Software Science (Operating and programming systems series). Elsevier Science Inc., NY (1977)Google Scholar
  11. 11.
    Card, D.N., Agresti, W.W.: Measuring software design complexity. The Journal of Systems And Software 3(8) (June 1988)Google Scholar
  12. 12.
    Pike, R., et al.: The use of name spaces in Plan 9. SIGOPS Oper. Syst. Rev. 27(2), 72–76 (1993)CrossRefGoogle Scholar
  13. 13.
    Wang, X.D., Yang, X., Allan, R.: Top ten questions to design a successful grid portal. In: SKG, pp. 18–24 (2006)Google Scholar
  14. 14.
    Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2003)zbMATHCrossRefGoogle Scholar
  15. 15.
    Abramson, D., Giddy, J., Kotler, L.: High performance parametric modeling with nimrod/g: Killer application for the global grid? (2000)Google Scholar
  16. 16.
    Goodale, T., et al.: Saga: A simple api for grid applications. high-level application programming on the grid. In: Comput. Methods in Science and Tech. (2006)Google Scholar
  17. 17.
    Herrera, J., Huedo, E., Montero, R.S., Llorente, I.M.: Developing grid-aware applications with drmaa on globus-based grids (2004)Google Scholar
  18. 18.
    Hagemeier, B., Menday, R., Schuller, B., Streit, A.: A universal api for grids. In: Cracow Grid Workshop (July 2007)Google Scholar
  19. 19.
    Chapman, C., et al.: Condor birdbath: Web service interfaces to condor. In: UK e-Science All Hands Meeting, Nottingham, UK (2005)Google Scholar
  20. 20.
    Grid ASCII Helper Protocol, http://www.cs.wisc.edu/condor/gahp/
  21. 21.
    Wegener, D., et al.: GridR: An R-based tool for scientific data analysis in grid environments. Future Gener. Comput. Syst. 25, 481–488 (2009)CrossRefGoogle Scholar
  22. 22.
    R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2011)Google Scholar
  23. 23.
    Urbanek, S.: rJava: Low-Level R to Java Interface (2009), http://cran.r-project.org/package=rJava
  24. 24.
    Wilde, M., et al.: Swift: A language for distributed parallel scripting. Parallel Computing 37(9), 633–652 (2011)CrossRefGoogle Scholar
  25. 25.
    Murray, D.G., Hand, S.: Scripting the cloud with skywriting (2010)Google Scholar
  26. 26.
    Morgan, M.M., Grimshaw, A.S.: Genesis ii - standards based grid computing. In: CCGRID, pp. 611–618 (2007)Google Scholar
  27. 27.
    Anjomshoaa, A., et al.: Job Submission Description Language Specification (2005)Google Scholar
  28. 28.
    van Hensbergen, E., Evans, N.P., Stanley-Marbell, P.: A unified execution model for cloud computing. In: LADIS (October 2009)Google Scholar
  29. 29.
    Thain, D., Livny, M.: Parrot: Transparent user-level middleware for data-intensive computing. Scalable Computing: Practice and Experience 6(3), 9–18 (2005)Google Scholar
  30. 30.
    Azab, A., Meling, H.: A Virtual File System Interface for Computational Grids. In: Aagesen, F.A., Knapskog, S.J. (eds.) EUNICE 2010. LNCS, vol. 6164, pp. 87–96. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  31. 31.
  32. 32.
  33. 33.
    Microsoft.Net (2011), http://www.microsoft.com/net/
  34. 34.
    Tierney, L., Rossini, A.J., Li, N., Sevcikova, H.: Snow: Simple Network of Workstations (November 2011)Google Scholar
  35. 35.
    Boyer, C.B.: A History of Mathematics, 2nd edn., p. 431. Wiley (1968)Google Scholar
  36. 36.
  37. 37.
    Bowbrick, S., Borg, A.: ECG complete. Churchill Livingstone (2006)Google Scholar
  38. 38.
    Safar, P.: History of cardiopulmonary-cerebral resuscitation. In: Cardiopulmonary Resuscitation, New York, pp. 1–53 (1989)Google Scholar
  39. 39.
    Klim, S., et al.: Population stochastic modelling (psm)-an r package for mixed-effects models based on stochastic differential equations. Comput. Methods Prog. Biomed. 94, 279–289 (2009)CrossRefGoogle Scholar
  40. 40.
    Schuller, B., Demuth, B., Mix, H., Rasch, K., Romberg, M., Sild, S., Maran, U., Bała, P., del Grosso, E., Casalegno, M., Piclin, N., Pintore, M., Sudholt, W., Baldridge, K.K.: Chemomentum - UNICORE 6 Based Infrastructure for Complex Applications in Science and Technology. In: Bougé, L., Forsell, M., Träff, J.L., Streit, A., Ziegler, W., Alexander, M., Childs, S. (eds.) Euro-Par Workshops 2007. LNCS, vol. 4854, pp. 82–93. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Abdulrahman Azab
    • 1
  • Hein Meling
    • 1
  1. 1.Dept. of Electrical Engineering and Computer ScienceUniversity of StavangerNorway

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