Hybrid meta-heuristic algorithms for independent job scheduling in grid computing

Date

2018-05-26

Advisors

Journal Title

Journal ISSN

ISSN

1568-4946

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

The term ’grid computing’ is used to describe an infrastructure that connects geographically distributed computers and heterogeneous platforms owned by multiple organizations allowing their computational power, storage capabilities and other resources to be selected and shared. The job scheduling problem is recognized as being one of the most important and challenging issues in grid computing environments. This paper proposes two strongly coupled hybrid meta-heuristic schedulers. The first scheduler combines Ant Colony Optimisation and Variable Neighbourhood Search in which the former acts as the primary algorithm which, during its execution, calls the latter as a supporting algorithm, while the second merges the Genetic Algorithm with Variable Neighbourhood Search in the same fashion. Several experiments were carried out to analyse the performance of the proposed schedulers in terms of minimizing the makespan using well known benchmarks. The experiments show that the proposed schedulers achieved impressive results compared to other selected approaches from the bibliography.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Hybrid meta-heuristic, Ant colony optimization, Genetic algorithm, Variable neighbourhood search, Job scheduling

Citation

Younis, M.T and Yang, S. (2018) Hybrid meta-heuristic algorithms for independent job scheduling in grid computing. Applied Soft Computing, in press,

Rights

Research Institute

Institute of Artificial Intelligence (IAI)