A Multi-agent Task Delivery System for Balancing the Load in Collaborative Grid Environment

  • Mauricio Paletta
  • Pilar Herrero
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


This paper focuses on improving load balancing algorithms in grid environments by means of multi-agent systems. The goal is endowing the environment with an efficient scheduling, taking into account not only the computational capabilities of resources but also the task requirements and resource configurations in a given moment. In fact, task delivery makes use of a Collaborative/Cooperative Awareness Management Model (CAM) which provides information of the environment. Next, a Simulated Annealing based method (SAGE) which optimizes the process assignment. Finally, a historic database which stores information about previous cooperation/collaborations in the environment aiming to learn from experience and infer to obtain more suitable future cooperation/collaboration. The integration of these three subjects allows agents define a system to cover all the aspects related with load-balancing problem in collaborations grid environment.


Negotiation Process Radial Base Function Network Grid Environment Environment Current Condition Task Delivery 
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.
    Bellifemine F., Poggi A., Rimassa G. (1999) JADE — A FIPA-compliant agent framework, Telecom Italia internal technical report, in Proc. International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAM′99), 97–108.Google Scholar
  2. 2.
    Berman F (1999) High-performance schedulers, in The Grid: Blueprint for a New Computing Infrastructure, Ian Foster and Carl Kesselman, (Eds.), Morgan Kaufmann, San Francisco, CA, 279–309.Google Scholar
  3. 3.
    Cao J, Spooner DP, Jarvis SA, Nudd GR (2005) Grid load balancing using intelligent agents, Future Generation Computer Systems, Vol. 21, No. 1, 135–149.CrossRefGoogle Scholar
  4. 4.
    Fangpeng D, Selim GA (2006) Scheduling Algorithms for Grid Computing: State of the Art and Open Problems” Technical Report No. 2006-504, Queen's University, Canada, 55 pages,
  5. 5.
    Fidanova S, Durchova M (2006) Ant Algorithm for Grid Scheduling Problem, Lecture Notes in Computer Science, VIII Distributed Numerical Methods and Algorithms for Grid Computing, 10.1007/11666806, ISSN: 0302-9743, ISBN: 978-3-540-31994-8, Vol. 3743, 405–412.Google Scholar
  6. 6.
    Foundation for Intelligent Physical Agents (2002) FIPA Abstract Architecture Specification, SC00001, Geneva, Switzerland.
  7. 7.
    Herrero P, Bosque JL, Pérez MS (2007) An Agents-Based Cooperative Awareness Model to Cover Load Balancing Delivery in Grid Environments, Lecture notes in computer science 2536, On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops, ISSN: 0302-9743, ISBN: 978-3-540-76887-6, Springer Verlag, Vol. 4805, 64–74.Google Scholar
  8. 8.
    Herrero P, Bosque J L, Pérez MS (2007) Managing Dynamic Virtual Organizations to get Effective Cooperation in Collaborative Grid Environments, Lecture notes in computer science 2536, On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops, ISBN: 978-3-540-76835-7, Springer Verlag, Vol. 4804, 1435–1452.Google Scholar
  9. 9.
    Jin R., Chen W., Simpson T.W. (2001) Comparative Studies of Metamodelling Techniques under Multiple Modeling Criteria, Struct Multidiscip Optim, Vol. 23, 1–13.CrossRefGoogle Scholar
  10. 10.
    McMullan P, McCollum B (2007) Dynamic Job Scheduling on the Grid Environment Using the Great Deluge Algorithm, Lecture Notes in Computer Science, ISSN: 0302-9743, ISBN: 978-3-540-73939-5, Vol. 4671, 10.1007/978-3-540-73940-1, 283–292.Google Scholar
  11. 11.
    Paletta M., Herrero P. (2008) Learning Cooperation in Collaborative Grid Environments to Improve Cover Load Balancing Delivery, in Proc. IEEE/WIC/ACM Joint Conferences on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society E3496, ISBN: 978-0-7695-3496-1, 399–402.Google Scholar
  12. 12.
    Paletta M., Herrero P. (2008) Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment, in Proc. International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 08), Advances in Soft Computing, J.M. Corchado et al. (Eds.), Vol. 50/2009, Springer, ISBN: 978-3-540-85862-1, 1–10.Google Scholar
  13. 13.
    Paletta M., Herrero P. (2008) Towards Fraud Detection Support using Grid Technology, accepted for publication in a Special Issue at Multiagent and Grid Systems — An International Journal. (To be published).Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Mauricio Paletta
    • 1
  • Pilar Herrero
    • 2
  1. 1.Departamento de Ciencia y TecnologíaUniversidad Nacional Experimental de GuayanaCiudad GuayanaVenezuela
  2. 2.Facultad de InformáticaUniversidad Politécnica de Madrid. Campus de Montegancedo S/N.MadridSpain

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