Grid Computing pp 145-170 | Cite as

Social Grid Agents

  • Gabriele Pierantoni
  • Brian Coghlan
  • Eamonn Kenny
Part of the Computer Communications and Networks book series (CCN)


Social grid agents are a socially inspired solution designed to address the problem of resource allocation in grid computing, they offer a viable solution to alleviating some of the problems associated with interoperability and utilization of diverse computational resources and to modeling the large variety of relationships among the different actors. The social grid agents provide an abstraction layer between resource providers and consumers. The social grid agent prototype is built in a metagrid environment, and its architecture is based on agnosticism both regarding technological solutions and economic precepts proves now useful in extending the environment of the agents from multiple grid middlewares, the metagrid, to multiple computational environments encompassing grids, clouds and volunteer-based computational systems. The presented architecture is based on two layers: (1) Production grid agents compose various grid services as in a supply chain, (2) Social grid agents that own and control the agents in the lower layer engage in social and economic exchange. The design of social grid agents focuses on how to handle the three flows (production, ownership, policies) of information in a consistent, flexible, and scalable manner. Also, a native functional language is used to describe the information that controls the behavior of the agents and the messages exchanged by them.


Service Level Agreement Rank Function Social Agent Production Agent Control Topology 
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.
    Abraham, A., Buyya, R., Nath, B.: Nature heuristics for scheduling jobs on computational grids. In: Proceedings of the 8th IEEE International Conference on Advanced Computing and Communication, Cochin, pp. 45–52 (2000)Google Scholar
  2. 2.
    ArguGRID Project: Retrieved from (2010)
  3. 3.
    Buyya, R.: Economic-Based Distributed Resource Management and Scheduling for Grid Computing. Monash University, Melbourne (2002)Google Scholar
  4. 4.
    Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic models for resource management and scheduling in grid computing. J. Concurr. Comput. Pract. Exp. 14, 1507–1542 (2002)MATHCrossRefGoogle Scholar
  5. 5.
    CATNETS Project: Retrieved from (2010)
  6. 6.
    Davies, A.: Computational intermediation and the evolution of computation as a commodity. Appl. Econ. 36(11), 1131–1142 (2004)CrossRefGoogle Scholar
  7. 7.
    Elmroth, E., Gardfjall, P.: Design and evaluation of a decentralized system for grid-wide fairshare scheduling. In: Proceedings of the 1st International Conference on e-Science and Grid Computing, Melbourne, pp. 221–229 (2005)Google Scholar
  8. 8.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Economic scheduling in grid computing. In: Revised Papers from the 8th International Workshop on Scheduling Strategies for Parallel Processing, Edinburgh, pp. 128–152 (2002)Google Scholar
  9. 9.
    Eymann, S.H., Streitberger, W., Hudert, S.: CATNETS – Open market approaches for self-organizing grid resource allocation. In: Proceedings of the 4th International Conference on Grid Economics and Business Models, Rennes, pp. 176–181 (2007)Google Scholar
  10. 10.
    Foster, I., Kesselman, C., Lee, C., Lindell, B., Nahrstedt, K., Roy, A.: A distributed resource management architecture that supports advanced reservation and co-allocation. In: Proceedings of the 7th International Workshop on Quality of Service, London, pp. 27–36 (1999)Google Scholar
  11. 11.
    Foster, I., Kesselamn, C., Nick, J.M., Tuecke, S.: The Anatomy of the Grid. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)CrossRefGoogle Scholar
  12. 12.
    Foster, I., Kesselamn, C., Nick, J.M., Tuecke, S.: The Physiology of the Grid: an open grid services architecture for distributed systems integration. Retrieved from (2002)
  13. 13.
    Grid Economy Project: Retrieved from (2010)
  14. 14.
    Global Grid Forum: Retrieved from (2010)
  15. 15.
    Gridbus Project: Retrieved from (2010)
  16. 16.
    Minoli, D.: A Network Approach to Grid Computing. Wiley-Interscience, Hoboken (2004)CrossRefGoogle Scholar
  17. 17.
    Nakai, J., Van Der Wijngaart, R.F.: Applicability of markets to global scheduling in grids. Retrieved from (2003)
  18. 18.
    Puschel, B.M.: Economically enhanced resource management for Internet service utilities. Lect. Notes Comput. Sci. 4831, 335–348 (2007)CrossRefGoogle Scholar
  19. 19.
    Sherwani, J., Ali, N., Lotia, N., Hayat, Z., Buyya, R.: Libra: A computational economy-based job scheduling system for clusters. Softw. Pract. Exp. 34(6), 573–590 (2004)CrossRefGoogle Scholar
  20. 20.
    Solomon, M: The ClassAd Language Reference Manual. Retrieved from (2008)
  21. 21.
    SORMA Project: Retrieved from (2010)
  22. 22.
    Wolski, R., Brevik, J., Plank, J.S., Bryan, T.: Grid resource allocation and control using ­computational economics. In: Berman F., Fox G., Hey A.J.G (eds.) Grid Computing: Making the Global Infrastructure a Reality, Wiley and Sons. pp. 747–772 (2003)Google Scholar
  23. 23.
    Wolski, R., Plank, J.S., Brevik, J., Bryan, T.: Analyzing market-based resource allocation strategies for the computational grid. Int. J. High Perform. Comput. Appl. 15, 258–281 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Gabriele Pierantoni
    • 1
  • Brian Coghlan
    • 1
  • Eamonn Kenny
    • 1
  1. 1.Department of Computer ScienceTrinity College DublinDublinIreland

Personalised recommendations