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针对现有的部署优化方法在求解云环境中面向服务软件的部署优化问题时,无法处理服务实例和虚拟机实例的伸缩以及无法保障求解质量等问题,本文提出了一种新的部署优化方法.该方法以提高面向服务软件的运行性能和降低运行成本为目标构建问题模型,并设计了一种基于遗传算法的MGA-DO算法对其进行求解.MGA-DO算法采用基于组的编码方式对软件的部署方案进行编码,然后结合基于组的单点交叉操作,实现了在优化过程中对服务实例和虚拟机实例的伸缩.此外,该算法引入现有的部署优化经验,设计了多种局部搜索规则,以进一步提高算法的求解性能.最后,一系列模拟实验表明,相比现有的算法,MGA-DO算法在求解所研究的问题时表现出了更好的性能.
Aiming at the problems existing in the existing deployment optimization methods such as the scalability of service instances and virtual machine instances, and the inability to solve the solution problems in the solution of service-oriented deployment optimization problems in cloud environments, this paper proposes a new deployment optimization approach. The method builds the problem model with the goal of improving the running performance and service running cost of service-oriented software and designs a MGA-DO algorithm based on genetic algorithm to solve the problem.MGA-DO algorithm uses group-based coding method to software , And then combined with the group-based single point crossover operation to achieve the scaling of service instances and virtual machine instances in the optimization process.In addition, the algorithm introduces the existing deployment optimization experience, designs a variety of local search Rules to further improve the performance of the algorithm.Finally, a series of simulation experiments show that compared with the existing algorithms, the MGA-DO algorithm shows better performance in solving the problem under study.