Computer Science ›› 2018, Vol. 45 ›› Issue (5): 31-37.doi: 10.11896/j.issn.1002-137X.2018.05.005

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Evoluation Genetic Algorithm of Multi-objective Optimization Scheduling on Cloud Workflow

WANG Guo-hao, LI Qing-hua and LIU An-feng   

  • Online:2018-05-15 Published:2018-07-25

Abstract: To implement the synchronous optimization of makespan and execution cost of scientific workflow scheduling in cloud environment,this paper proposed a multi-objective optimization evoluation genetic scheduling algorithm named MOEGA.Based on evoluation genetics,MOEGA defines the encoding mechanism of the mapping between tasks and virtual machines,virtual machines and hosts placement,and designs the fitness function satisfying multi-objective optimization.Meanwhile,for meeting the diversity of population,the crossover operation and mutation operation are introduced into the scheduling scheme,and the heuristics is used to initialize the population.Through the experimental tests of four types of scientific workflow in reality,its performance was compared with the same types of algorithms.The results show that MOEGA not only can meet the deadline constraint of workflow,but also outperforms other algorithms in overall performance of reducing the execution makespan and execution cost.

Key words: Cloud computing,Genetic algorithm,Workflow scheduling,Multi-objective optimization,Fitness function

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