Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing

Date

2017-03-25

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

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.

Description

Keywords

Citation

Younis, M., Yang, S. and Passow, B. (2017) Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing. EvoApplications 2017: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol 10199, pp. 177-189

Rights

Research Institute

Institute of Artificial Intelligence (IAI)