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将服务部署优化问题建模为多目标组合优化问题.在多目标遗传算法的基础上,把部署方案转换为基因编码,用轮盘赌选择机制选择个体,用单点交叉算子产生新的子代,并以设定的概率发生变异.对合适个体考虑支配值和稀疏值设计适应度函数;对不合适个体根据支配值和SLA冲突设计适应度函数.最后给出了优化过程.通过仿真实验可以看出:随着迭代次数的增加,适应度值及各个优化指标值逐渐收敛于一个固定且较优值,说明利用设计的优化算法,能使各个优化目标值较快地收敛到一个较优解,能较好地帮助基础设施即服务(SaaS)提供商在部署应用服务时进行有效规划和决策.
The service deployment optimization problem is modeled as a multi-objective combinatorial optimization problem.On the basis of multi-objective genetic algorithm, the deployment scheme is converted to a genetic code, individuals are selected by the roulette selection mechanism, and a single point crossover operator is used to generate a new child The fitness function is designed considering the dominance and the sparse value of suitable individuals, and the fitness function is designed according to the dominance and SLA conflict to the inappropriate individuals.At last, the optimization process is given.According to the simulation experiment It can be seen that as the number of iterations increases, the fitness values and the values of each optimization index gradually converge to a fixed and superior value, which shows that using the optimized design algorithm, each optimization target value can converge to a better one Solutions can help Infrastructure SaaS providers plan and make decisions effectively when deploying application services.