Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing

  • Muhanad Tahrir Younis
  • Shengxiang Yang
  • Benjamin Passow
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques.

Keywords

Meta-heuristic algorithms Seeded genetic algorithm Ant colony optimization Job scheduling Grid computing Makespan 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Muhanad Tahrir Younis
    • 1
  • Shengxiang Yang
    • 1
  • Benjamin Passow
    • 1
  1. 1.Centre for Computational Intelligence (CCI), School of Computer Science and InformaticsDe Montfort UniversityLeicesterUK

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