Microservice Deployment in Cloud-Edge Environment using Enhanced Global Search Grey Wolf Optimizer-Greedy Algorithm

Abstract

The rapid advancement of edge-cloud technologies has made service deployment increasingly crucial. Additionally, benefiting from the reusability of services, complex applications are subdivided into different microservices. Given the constraints of limited resources, heterogeneous servers, and the geographical diversity of users, how to reasonably deploy microservices becomes a significant challenge. In this paper, we propose a microservice deployment model aimed at minimizing users' latency and maximizing edge providers' profits. The model is divided into different scenarios, each with varying trends in user request categories. To seek microservice deployment strategies, we introduce an Enhanced Global Search Grey Wolf Optimizer-Greedy (EGSGWO-G) algorithm designed for microservice deployment-offloading frameworks. This algorithm leverages EGSGWO to search for deployment strategies and evaluates them using greedy service offloading algorithm. Finally, extensive experiments demonstrate that the EGSGWO-G algorithm improves convergence speed by 31.78%, reduces latency by 12.64%, and increases provider profits by 1.30% compared to GWO-G.