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Gaussian Mixture Models vs. Fuzzy Rule-Based Systems for Adaptive Meta-scheduling in Grid/Cloud Computing

  • R. P. Prado
  • J. Braun
  • J. Krettek
  • F. Hoffmann
  • S. García-Galán
  • J. E. Muñoz Expósito
  • T. Bertram
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 171)

Abstract

Adaptive scheduling strategies are about considering the state of computational grids to obtain efficient and reliable schedules and to prevent the system performance deterioration. In this work, emerging adaptive strategies in grid computing, namely Fuzzy Rule-Based Systems (FRBS) -based strategies and a new adaptive scheduling approach, gaussian scheduling founded on Gaussian Mixture Models (GMMs) are compared. Both types of strategies focus on modeling the state of resources and select the most convenient site of the grid at every scheduling step given the current conditions. FRBSs provide a fuzzy characterization of the grid state and the inference of a suitability index based on their own knowledge given in the form of fuzzy IF-THEN rules. Besides, a GMM can be trained to model a complex probability density distribution indicating the suitability of every site in the grid to be the target of the schedule with the current conditions of its resources. This way the GMM scheduler assigns a probability to every state of the site where a higher probability is associated to a higher suitability of selection. Simulations based on real grid facilities are conducted to test the FRBS and GMM-based models and results are analyzed in terms of accuracy and convergence behaviour of their associated learning processes.

Keywords

Grid Computing Gaussian Mixture Model Schedule Strategy Grid Schedule Central Process Unit 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • R. P. Prado
    • 1
  • J. Braun
    • 2
  • J. Krettek
    • 2
  • F. Hoffmann
    • 2
  • S. García-Galán
    • 1
  • J. E. Muñoz Expósito
    • 1
  • T. Bertram
    • 2
  1. 1.Telecommunication Engineering DepartmentUniversity of JaénLinaresSpain
  2. 2.Control System EngineeringUniversity DortmundDortmundGermany

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