Cloud service deployment optimization method based on multi-objective genetic algorithm

被引：0
作者：
Xie B. [1 ]
Yang Y. [1 ]
Kuang Y. [1 ]
机构：
[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing
来源：
Huazhong Ligong Daxue Xuebao | / 80-83期
关键词：
Cloud computing; Fitness function; Gene code; Multi-objective genetic algorithm; Service deployment;
D O I：
10.13245/j.hust.16S116
中图分类号：
学科分类号：
摘要：
The optimization of service deployment was modeled as multi-objective composition optimization. On the basis of multi-objective genetic algorithm, deployment solutions were converted to gene codes; individuals were selected by roulette mechanism; new generations were produced by single point crossover operator; variations were appeared in preset probability. The fitness function for fit individuals was based on dominant value and sparse value, while this function for unfit individuals was based on dominant value and SLA collisions. The optimization process was also proposed. In simulation experiment the fitness value and other evaluations converge at a fixed and general optimal value gradually when iteration number increases. It shows that the optimization method in this paper can help multi-objective values converge at a general optimal value and give help to infrastructure as a service (SaaS) provider on planning and decision of service deployment. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：80 / 83
页数：3
相关论文
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