关键词: 工作流, 云计算, 资源优化, 任务调度, 遗传算法

Abstract: To improve Quality of Service (QoS) and reduce operating cost of cloud workflow effectively,the cloud workflow scheduling optimization was researched.Based on analyzing different involving subjects and scheduling links,a cloud workflow scheduling model oriented to QoS and cost-awareness was established.Meanwhile a coding rule dedicated to scheduling scheme was proposed to analyze the scheduling strategy of the proposed model at different stages.A genetic algorithm based on two segment coding of tasks order division was proposed.With tenant leases and virtual machine instance loads as constraints,the population iterative evolution through genetic recombination and mutation processes of two segments was developed,and both cloud service charge saving and cloud resources cost saving were achieved.Simulation experiments were conducted on instances of different sizes,and the results showed that the proposed algorithm could achieve much better solution quality than the two kinds of genetic algorithm based on tasks and virtual machines mapping coding.

Key words: workflow, cloud computing, resources optimization, task scheduling, genetic algorithms