For the deployment and startup of microservice instances in different resource centres, we propose an optimization problem model based on the evolutionary multi-objective theory. The objective functions of the model consider the computation and storage resource utilization rate, load balancing rate, and actual microservice usage rate in resource service centres. The constraints of the model are the completeness of service, total amount of storage resources, and total number of microservices. In this study, a knowledge-driven evolutionary algorithm (named MGR-NSGA-III) is proposed to solve the problem model and seek the optimal deployment and startup strategy of microservice instances in different resource centres. The proposed model and solution have been evaluated via real data experiments. The results show that our approach is better than the traditional microservice instance deployment and startup strategy. The average computation rate, storage idle rate, and actual microservice idle rate were 13.21%, 5.2%, and 16.67% lower than those in NSGA-III, respectively. After 50, 100, and 150 evolutionary generations in serval operations, the population members in NGR-NSGA-III dominated the population members in NSGA-III 6,270, 3,581, and 7,978 times in average, respectively, which means that NGR-NSGA-III can converge to the optimal solution much quicker than NSGA-III.

为了在不同资源中心中部署和启动微服务实例，我们提出了一种基于进化多目标理论的优化问题模型。该模型的目标函数考虑了资源服务中心的计算和存储资源利用率，负载均衡率以及实际的微服务利用率。该模型的约束条件是服务的完整性，存储资源的总量以及微服务的总数。在这项研究中，提出了一种知识驱动的进化算法（名为MGR-NSGA-III）来解决问题模型，并在不同资源中心中寻求微服务实例的最佳部署和启动策略。通过实际数据实验对提出的模型和解决方案进行了评估。结果表明，我们的方法优于传统的微服务实例部署和启动策略。平均计算率，存储闲置率和实际微服务闲置率分别比NSGA-III低13.21％，5.2％和16.67％。在进行了50、100和150次进化运算后，NGR-NSGA-III的种群成员平均分别占NSGA-III的种群成员的6,270、3,581和7,978倍，这意味着NGR-NSGA-III可以比NSGA-III更快地收敛到最佳解决方案。