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Microservices and Kubernetes are widely used in the development and operations of cloud-native applications. By providing automated placement and scaling, Kubernetes has become the main tool for managing microservices. However, existing work and Kubernetes fail to consider the dynamic competition and availability of microservices as well as the problem of shared dependency libraries among multiple microservice instances. To this end, this article proposes an integer nonlinear microservice placement model for Kubernetes with the goal of cost minimization. Specifically, we calculate the number of instances based on microservice availability and construct a model in which the total resource demand of multiple microservice instances exceeds the appropriate proportion of node resources when dynamic resource competition exists and the size of the shared dependency library is less than the node storage capacity. Finally, this article solves the microservice placement model using an improved genetic algorithm. The experimental results demonstrate that higher throughput is obtained with the same costs and that the same throughput is obtained with lower costs.
Ding et al. (Thu,) studied this question.