An Efficient Algorithm for Microservice Placement in Cloud-Edge Collaborative Computing Environment

Microservices along with cloud-edge computing technologies are widely adopted to take advantage of the abundant computing resources of the cloud and the low latency, high bandwidth capabilities of the edge. However, factors such as frequent user requirement changes have made the current deployment scheme not fully adaptable to new requirements, resulting in an increase in average response time. Therefore, the microservice system needs to adjust the microservice deployment scheme online in response to continuously changing user requirements to reduce the average response time, which is known as the cloud-edge collaborative microservice deployment problem. However, existing methods are not able to meet the efficiency requirements and do not fully consider the complex dependencies of microservices and budget constraints in the cloud. To address this problem, this paper proposes a solution to the cloud-edge collaborative microservice deployment problem by modeling it as an NP-hard integer nonlinear programming problem in a cloud-edge environment consisting of private edge clouds and public cloud. An efficient Two-stage Iterated Greedy Optimization (TIGO) algorithm is also proposed and its convergence is proven. Extensive experimental results show that this approach achieves better average response times in less time compared to existing methods.