Cost-Optimized Microservice Deployment for IoT Application in Cloud-Edge Collaborative Environment

With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user’s cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms.