Computer Science ›› 2018, Vol. 45 ›› Issue (8): 105-112.doi: 10.11896/j.issn.1002-137X.2018.08.019

• Network & Communication • Previous Articles     Next Articles

Coevolutionary Genetic Algorithm of Cloud Workflow Scheduling Based on Adaptive Penalty Function

XU Jian-rui1,2, ZHU Hui-juan3   

  1. School of Computer Science and Telecommunication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China1
    Zhenjiang Branch,Jiangsu Union Technical Institute,Zhenjiang,Jiangsu 212016,China2
    School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 100049,China3
  • Received:2017-07-04 Online:2018-08-29 Published:2018-08-29

Abstract: The cloud computing provides a more efficient operation environment for the execution of large-scale scienti-fic workflow application.To solve thecost optimization problem of the scientific workflow scheduling in the cloud environment,a workflow scheduling genetic algorithm based on coevolution was proposed.This algorithm introduces an adaptive penalty function into GA with the strict constraints.By the coevolutionary approach,it can adjustthe crossover and mutation probability of population individuals adaptively to accelerate the convergence of the algorithm and prevent the prematurity ofpopulation.The simulation experiment results of four kinds of scientific workflow in reality show that the scheduling scheme obtained by the CGAA algorithm performs better in satisfying the comprehensive perfor-mance of the workflow scheduling deadline constraints and reducing the total execution cost of tasks compared with the same types of algorithms.

Key words: Cloud computing, Coevolution, Genetic algorithm, Scientific workflow, Tasks scheduling

CLC Number: 

  • TP393
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