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

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

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 14 , Issue 1 , 2021


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


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.

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

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 14
ISSUE: 1
Year: 2021
Published on: 22 January, 2021
Page: [13 - 19]
Pages: 7
DOI: 10.2174/2352096513999200424075719
Price: $25

Article Metrics

PDF: 11