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

• Published in: Applied soft computing Vol. 72; pp. 498 - 517
• Main Authors: Younis, Muhanad Tahrir, Yang, Shengxiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2018
• Subjects: Ant Colony Optimization; Genetic Algorithm; Hybrid meta-heuristic; Job scheduling; Variable Neighbourhood Search; Ant Colony Optimization; Variable Neighbourhood Search; Hybrid meta-heuristic; Genetic Algorithm; Job scheduling
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.05.032

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 Optimization 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 analyze 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.