Journal of Grid Computing

, Volume 13, Issue 1, pp 19–34 | Cite as

Revisiting the Anatomy and Physiology of the Grid

  • Chris A. Mattmann
  • Joshua Garcia
  • Ivo Krka
  • Daniel Popescu
  • Nenad Medvidovic
Open Access


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.


DSSA Physiology Anatomy OODT Software architecture 


  1. 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)Google Scholar
  2. 2.
    Kesselman, C., et al.: The anatomy of the grid: Enabling scalable virtual organizations. J. Supercomputing Applications, 1–25 (2001)Google Scholar
  3. 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)Google Scholar
  4. 4.
    Hughes, J.S., et al.: An ontology-based archive information model for the planetary science community. In: Proceedings Spaceops, Heidelberg, Germany (2008)Google Scholar
  5. 5.
    Bernholdt, D., et al.: The earth system grid: supporting the next generation of climate modeling research. Proc. IEEE 93, 485–495 (2005)CrossRefGoogle Scholar
  6. 6.
    Deelman, E., et al.: Grid-based galaxy morphology analysis for the national virtual observatory. In: Proceedings IEEE Conference on Supercomputing, Phoenix, AZ (2003)Google Scholar
  7. 7.
    Mattmann, C., et al.: A software architecture-based framework for highly distributed and data intensive scientific applications. In: Proceedings ICSE, Shanghai, China (2006)Google Scholar
  8. 8.
    Mattmann, C., et al.: GLIDE: a grid-based, lightweight, infrastructure for data-intensive environments. In: Proceedings EGC, Amsterdam, the Netherlands (2005)Google Scholar
  9. 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)CrossRefGoogle Scholar
  10. 10.
    Atkinson, M.P., et al.: Grid database access and integration: Requirements and functionalities, global grid forum GFD-I.13 (2003)Google Scholar
  11. 11.
    Badia, R., et al.: Use-cases and requirements for grid checkpoint and recovery, global grid forum GFD-I.92 (2007)Google Scholar
  12. 12.
    Bhatia, K.: Peer-to-peer requirements on the open grid services architecture framework, global grid forum GFD-I. 049 (2005)Google Scholar
  13. 13.
    Gamiel, K., et al.: Grid information retrieval requirements, global grid forum GFD-I.027 (2004)Google Scholar
  14. 14.
    Jha, S., Merzky, A.: A requirements analysis for a simple api for grid applications, global grid forum GFD-I.071 (2006)Google Scholar
  15. 15.
    Welch, V., et al.: OGSI authorization requirements, global grid forum GFD-I.067 (2006)Google Scholar
  16. 16.
    Tracz, W., et al.: Software development using domain-specific software architectures. ACM SEN, 27–38 (1995)Google Scholar
  17. 17.
    Mattmann, C., et al.: Unlocking the Grid. In: Proceedings CBSE, St. Louis, MO (2005)Google Scholar
  18. 18.
    Taylor, R.N.: Generalization from domain experience: The superior paradigm for software architecture research?. In: Proceedings ISAW-2, San Francisco, CA (1996)Google Scholar
  19. 19.
    Taylor, R.N., et al.: Software architecture: Foundations, theory and practice. Wiley (2008)Google Scholar
  20. 20.
    Finkelstein, A., et al.: Relating requirements and architectures: a study of data grids. J. Grid Comput. 2, 207–222 (2004)CrossRefGoogle Scholar
  21. 21.
    The DataGrid Project: (2006)
  22. 22.
    Venugopal, S., et al.: A taxonomy of data grids for distributed data sharing, management, and processing. ACM Comput. Surv. 38 (2006)Google Scholar
  23. 23.
    Yu, J., Buyya, R.: A taxonomy of scientific workflow systems for grid computing. SIGMOD Rec. Spec. Issue Sci. Workflows 34 (2005)Google Scholar
  24. 24.
    Kazman, R., Carriere, S.J.: Playing detective: reconstructing software architecture from available evidence. JASE 6, 107–138 (1999)Google Scholar
  25. 25.
    Koschke, R.: Rekonstruktion von software-architekturen. Informatik-Forschung und Entwicklung 19, 127–140 (2005)CrossRefGoogle Scholar
  26. 26.
    Storey, M.-A. D., et al.: On designing an experiment to evaluate a reverse engineering tool. WCRE, Monterey, California (1996)Google Scholar
  27. 27.
    The portable bookshelf (PBS). Available online at (2008)
  28. 28.
    Medvidovic, N., Jakobac, V.: Using software evolution to focus architectural recovery. JASE 13, 225–256 (2006)Google Scholar
  29. 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. 30.
    Wheeler, D.A.: More than a gigabuck: estimating GNU/Linux’s size (2001).
  31. 31.
    Grid systems software architecture - supplimentary Website, (2008)
  32. 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. 33.
    Bowman, I.T., et al.: Linux as a case study: its extracted software architecture. ICSE, Los Angeles (1999)Google Scholar
  34. 34.
    Sim, S.E., et al.: On using a benchmark to evaluate C ++ extractors. In: Proceedings IWPC (2005)Google Scholar
  35. 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)Google Scholar
  36. 36.
  37. 37.
    Robbins, J., Redmiles, D.: Cognitive support, UML adherence, and XMI interchange in Argo/UML. Inf. Softw. Technol. 42, 79–89 (2000)CrossRefGoogle Scholar
  38. 38.
    Understand - source code analysis and metrics, (2008)
  39. 39.
  40. 40.
    Begeman, K.G., et al.: Merging grid technologies. J. Grid Comput. 8.2, 199–221 (2010)CrossRefGoogle Scholar
  41. 41.
    Rimal, Bhaskar, P., et al.: Architectural requirements for cloud computing systems: an enterprise cloud approach. J. Grid Comput. 9.1, 3–26 (2011)CrossRefGoogle Scholar
  42. 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)CrossRefGoogle Scholar
  43. 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)Google Scholar
  44. 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).
  45. 45.
    Mell, P., Grance, T.: Perspectives on cloud computing and standards. national institute of standards and technology (NIST), information technology laboratory (2009)Google Scholar
  46. 46.
    Kapil Bakshi, K.: Cisco cloud computing-data center strategy, architecture, and solutions. Point of view white paper for U.S. Public Sector (2009)Google Scholar
  47. 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)Google Scholar
  48. 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)Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Chris A. Mattmann
    • 1
    • 2
  • Joshua Garcia
    • 4
  • Ivo Krka
    • 3
  • Daniel Popescu
    • 3
  • Nenad Medvidovic
    • 1
  1. 1.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  3. 3.Google Inc.Santa MonicaUSA
  4. 4.George Mason UniversityFairfaxUSA

Personalised recommendations