• DocumentCode
    2959581
  • Title

    An LMI-neural network based solution to the load balancing problem for heterogeneous local clusters

  • Author

    Silva, João M M ; Kaszkurewicz, Eugenius

  • Author_Institution
    Brazilian Naval Res. Inst., Rio de Janeiro
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    2302
  • Lastpage
    2309
  • Abstract
    A solution for the load balancing problem in local clusters of heterogeneous processors is proposed within the setting of delayed artificial neural networks, optimal control and linear matrix inequalities (LMI) theory. Based on a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of delayed cellular neural networks model. A systematic method of controller synthesis is derived, based on two coupled linear matrix inequalities - one guaranteeing global convergence and the other guaranteeing performance in the linear region of operation. Simulations and computational experiments show the efficiency of this approach, reducing load balancing time.
  • Keywords
    cellular neural nets; control system synthesis; linear matrix inequalities; neurocontrollers; optimal control; pattern clustering; resource allocation; LMI-neural network based solution; artificial neural networks; controller synthesis; delayed cellular neural networks; heterogeneous local clusters; heterogeneous processors; linear matrix inequalities; load balancing problem; load balancing time; mathematical model; optimal control; Artificial neural networks; Cellular neural networks; Computational modeling; Control system synthesis; Convergence; Linear matrix inequalities; Load management; Mathematical model; Network synthesis; Optimal control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4634116
  • Filename
    4634116