Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment

Title:Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment

Volume: 14 Issue: 1

Author(s): G.E. Weiqing*Cui Yanru

Affiliation:

• City College of Dongguan, University of Technology, Dongguan City, Guangdong,China

Keywords: Cloud computing, genetic algorithm, task scheduling, min-min algorithm, max-min algorithm, EIGA scheduling.

Abstract:

Background: Min-min and max-min algorithms were combined on the basis of the traditional genetic algorithm to make up for its shortcomings.

Methods: In this paper, a new cloud computing task-scheduling algorithm that introduces min-min and max-min algorithms to generate initialization population, selects task completion time and load balancing as double fitness functions, and improves the quality of initialization population, algorithm searchability and convergence speed, was proposed.

Results: The simulation results proved that the cloud computing task-scheduling algorithm was superior to and more effective than the traditional genetic algorithm.

Conclusion: The paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Cite this article as:

Weiqing G.E. *, Yanru Cui, Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment, Recent Advances in Electrical & Electronic Engineering 2021; 14 (1) . https://dx.doi.org/10.2174/2352096513999200424075719

DOI https://dx.doi.org/10.2174/2352096513999200424075719	Print ISSN 2352-0965
Publisher Name Bentham Science Publisher	Online ISSN 2352-0973