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Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm

  • Ajith Abraham
  • Hongbo Liu
  • Weishi Zhang
  • Tae-Gyu Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

Grid computing is a computing framework to meet the growing computational demands. This paper introduces a novel approach based on Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. The proposed approach is to dynamically generate an optimal schedule so as to complete the tasks within a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of the proposed PSO algorithm with Genetic Algorithm (GA) and Simulated Annealing (SA) approaches.

Keywords

Genetic Algorithm Particle Swarm Optimization Simulated Annealing Computational Grid Particle Swarm Optimization Algorithm 
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 2006

Authors and Affiliations

  • Ajith Abraham
    • 1
    • 3
  • Hongbo Liu
    • 2
  • Weishi Zhang
    • 3
  • Tae-Gyu Chang
    • 1
  1. 1.IITA Professorship Program, School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  2. 2.Department of ComputerDalian University of TechnologyDalianChina
  3. 3.School of Computer ScienceDalian Maritime UniversityDalianChina

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