Skip to main content
Download PDF

Revisiting the Anatomy and Physiology of the Grid

Journal of Grid Computing volume 13, pages 19–34 (2015)Cite this article

Abstract

A domain-specific software architecture (DSSA) represents an effective, generalized, reusable solution to constructing software systems within a given application domain. In this paper, we revisit the widely cited DSSA for the domain of grid computing. We have studied systems in this domain over the last ten years. During this time, we have repeatedly observed that, while individual grid systems are widely used and deemed successful, the grid DSSA is actually underspecified to the point where providing a precise answer regarding what makes a software system a grid system is nearly impossible. Moreover, every one of the existing purported grid technologies actually violates the published grid DSSA. In response to this, based on an analysis of the source code, documentation, and usage of eighteen of the most pervasive grid technologies, we have significantly refined the original grid DSSA. We demonstrate that this DSSA much more closely matches the grid technologies studied. Our refinements allow us to more definitively identify a software system as a grid technology, and distinguish it from software libraries, middleware, and frameworks.

References

  1. Foster, I., et al.: The physiology of the grid: An open grid services architecture for distributed systems integration, Globus research, work-in-progress (2002)

  2. Kesselman, C., et al.: The anatomy of the grid: Enabling scalable virtual organizations. J. Supercomputing Applications, 1–25 (2001)

  3. Crichton, D.J., et al.: A distributed information services architecture to support biomarker discovery in early detection of cancer. In: Proceedings of e-Science, p 44 (2006)

  4. Hughes, J.S., et al.: An ontology-based archive information model for the planetary science community. In: Proceedings Spaceops, Heidelberg, Germany (2008)

  5. Bernholdt, D., et al.: The earth system grid: supporting the next generation of climate modeling research. Proc. IEEE 93, 485–495 (2005)

    Article  Google Scholar 

  6. Deelman, E., et al.: Grid-based galaxy morphology analysis for the national virtual observatory. In: Proceedings IEEE Conference on Supercomputing, Phoenix, AZ (2003)

  7. Mattmann, C., et al.: A software architecture-based framework for highly distributed and data intensive scientific applications. In: Proceedings ICSE, Shanghai, China (2006)

  8. Mattmann, C., et al.: GLIDE: a grid-based, lightweight, infrastructure for data-intensive environments. In: Proceedings EGC, Amsterdam, the Netherlands (2005)

  9. Chervenak, A., et al.: The data grid: towards an architecture for the distributed management and analysis of large scientific data sets. J. Netw. Comput. Appl. 23, 187–200 (2000)

    Article  Google Scholar 

  10. Atkinson, M.P., et al.: Grid database access and integration: Requirements and functionalities, global grid forum GFD-I.13 (2003)

  11. Badia, R., et al.: Use-cases and requirements for grid checkpoint and recovery, global grid forum GFD-I.92 (2007)

  12. Bhatia, K.: Peer-to-peer requirements on the open grid services architecture framework, global grid forum GFD-I. 049 (2005)

  13. Gamiel, K., et al.: Grid information retrieval requirements, global grid forum GFD-I.027 (2004)

  14. Jha, S., Merzky, A.: A requirements analysis for a simple api for grid applications, global grid forum GFD-I.071 (2006)

  15. Welch, V., et al.: OGSI authorization requirements, global grid forum GFD-I.067 (2006)

  16. Tracz, W., et al.: Software development using domain-specific software architectures. ACM SEN, 27–38 (1995)

  17. Mattmann, C., et al.: Unlocking the Grid. In: Proceedings CBSE, St. Louis, MO (2005)

  18. Taylor, R.N.: Generalization from domain experience: The superior paradigm for software architecture research?. In: Proceedings ISAW-2, San Francisco, CA (1996)

  19. Taylor, R.N., et al.: Software architecture: Foundations, theory and practice. Wiley (2008)

  20. Finkelstein, A., et al.: Relating requirements and architectures: a study of data grids. J. Grid Comput. 2, 207–222 (2004)

    Article  Google Scholar 

  21. The DataGrid Project: http://eu-datagrid.web.cern.ch/eu-datagrid/ (2006)

  22. Venugopal, S., et al.: A taxonomy of data grids for distributed data sharing, management, and processing. ACM Comput. Surv. 38 (2006)

  23. Yu, J., Buyya, R.: A taxonomy of scientific workflow systems for grid computing. SIGMOD Rec. Spec. Issue Sci. Workflows 34 (2005)

  24. Kazman, R., Carriere, S.J.: Playing detective: reconstructing software architecture from available evidence. JASE 6, 107–138 (1999)

    Google Scholar 

  25. Koschke, R.: Rekonstruktion von software-architekturen. Informatik-Forschung und Entwicklung 19, 127–140 (2005)

    Article  Google Scholar 

  26. Storey, M.-A. D., et al.: On designing an experiment to evaluate a reverse engineering tool. WCRE, Monterey, California (1996)

    Google Scholar 

  27. The portable bookshelf (PBS). Available online at http://www.turing.toronto.edu/pbs (2008)

  28. Medvidovic, N., Jakobac, V.: Using software evolution to focus architectural recovery. JASE 13, 225–256 (2006)

    Google Scholar 

  29. Littlefair, T.: An investigation into the use of softare code metrics in the industrial software development environment. Edith Cowan University, Ph.D, Dissertation (2001)

    Google Scholar 

  30. Wheeler, D.A.: More than a gigabuck: estimating GNU/Linux’s size (2001). http://www.dwheeler.com/sloc/redhat71-v1/redhat71sloc.html

  31. Grid systems software architecture - supplimentary Website, http://sunset.usc.edu/textasciitildesoftarch/grids/ (2008)

  32. Krauter, K., et al.: A taxonomy and survey of grid resource management systems for distributed computing. Softw.: Pract. Experience 32, 135–164 (2001)

    Google Scholar 

  33. Bowman, I.T., et al.: Linux as a case study: its extracted software architecture. ICSE, Los Angeles (1999)

    Google Scholar 

  34. Sim, S.E., et al.: On using a benchmark to evaluate C ++ extractors. In: Proceedings IWPC (2005)

  35. Storey, M.-A.: ShriMP views: an interactive environment for exploring multiple hierarchical views of a Java program. In: in ICSE 2001 Workshop on Software Visualization (2001)

  36. IBM - rational software architect, http://www-01.ibm.com/software/awdtools/architect/swarchitect/(2008)

  37. Robbins, J., Redmiles, D.: Cognitive support, UML adherence, and XMI interchange in Argo/UML. Inf. Softw. Technol. 42, 79–89 (2000)

    Article  Google Scholar 

  38. Understand - source code analysis and metrics, http://www.scitools.com/products/understand/ (2008)

  39. Doxygen (2008). http://www.stack.nl/textasciitildedimitri/doxygen/

  40. Begeman, K.G., et al.: Merging grid technologies. J. Grid Comput. 8.2, 199–221 (2010)

    Article  Google Scholar 

  41. Rimal, Bhaskar, P., et al.: Architectural requirements for cloud computing systems: an enterprise cloud approach. J. Grid Comput. 9.1, 3–26 (2011)

    Article  Google Scholar 

  42. Montes, J., Sánchez, A., Pérez, M.S.: Riding out the storm: How to deal with the complexity of grid and cloud management. J. Grid Comput. 10.3, 349–366 (2012)

    Article  Google Scholar 

  43. Shamsi, J., Muhammad Ali K., Mohammad Ali Q.: Data-intensive cloud computing: requirements, expectations, challenges, and solutions. J. Grid Comput. 11.2, 281–310 (2013)

  44. Mattmann, C., Hart, A., Cinquini, L., Lazio, J., Khudikyan, S., Jones, D., Preston, R., Bennett, T., Butler, B., Harland, D., Glendenning, B., Kern, J., Robnett, J.: Scalable data mining, archiving and big data management for the next generation astronomical telescopes. In: Hu, W., Kaabouch, N. (eds.) Big Data Management, Technologies, and Applications, pp. 196–221. IGI Global (2013). http://www.igi-global.com/book/big-data-management-technologies-applications/

  45. Mell, P., Grance, T.: Perspectives on cloud computing and standards. national institute of standards and technology (NIST), information technology laboratory (2009)

  46. Kapil Bakshi, K.: Cisco cloud computing-data center strategy, architecture, and solutions. Point of view white paper for U.S. Public Sector (2009)

  47. Mohan, T.S., Medvidovic, N., Mattmann, C.: Leveraging domain-specific software architectures for classifying cloud service abstractions. In: Proceedings of the Cloud Futures 2010: Advancing Research with Cloud Computing Workshop, Redmond, WA, April 8–9 (2010)

  48. Stearley, J., Corwell, S., Lord, K.: Bridging the gaps: joining information sources with splunk Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques. USENIX Association (2010)

Download references

Author information

Affiliations

  1. Computer Science Department, University of Southern California, Los Angeles, CA, 90089, USA

    Chris A. Mattmann & Nenad Medvidovic

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA

    Chris A. Mattmann

  3. Google Inc., 1333 2nd Street, Santa Monica, CA, USA

    Ivo Krka & Daniel Popescu

  4. George Mason University, 4400 University Drive, MS 4A5, Fairfax, VA, 22030-4444, USA

    Joshua Garcia

Authors
  1. Chris A. Mattmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivo Krka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Popescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nenad Medvidovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chris A. Mattmann.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mattmann, C.A., Garcia, J., Krka, I. et al. Revisiting the Anatomy and Physiology of the Grid. J Grid Computing 13, 19–34 (2015). https://doi.org/10.1007/s10723-015-9324-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-015-9324-0

Keywords

  • DSSA
  • Physiology
  • Anatomy
  • OODT
  • Software architecture