Joint Optimization of Microservice and Database Orchestration in Edge Clouds via Multi-Stage Proximal Policy

Microservices as an emerging architectural approach have been widely applied in the development of online applications. However, in large-scale service systems, frequent data communications, complex invocation dependencies, and strict latency requirements pose significant challenges to efficient microservice orchestration. In addition, microservices need to frequently access the database to achieve data persistence, creating a mutual dependency between the two, and this symmetry further increases the complexity of service orchestration and coordinated deployment. In this context, the strong coupling of service deployment, database layout, and request routing makes effective local optimization difficult. However, existing research often overlooks the impact of databases, fails to achieve joint optimization among databases, microservice deployments, and routing, or lacks fine-grained orchestration strategies for multi-instance models. To address the above limitations, this paper proposes a joint optimization framework based on the Database-as-a-Service (DaaS) paradigm. It performs fine-grained multi-instance queue modeling based on queuing theory to account for delays in data interaction, request queuing, and processing. Furthermore this paper proposes a proximal policy optimization algorithm based on multi-stage joint decision-making to address the orchestration problem of microservices and database instances. In this algorithm, the action space is symmetrical between microservices and database deployment, enabling the agent to leverage this characteristic and improve representation learning efficiency through shared feature extraction layers. The algorithm incorporates a two-layer agent policy stability control to accelerate convergence and a three-level experience replay mechanism to achieve efficient training on high-dimensional decision spaces. Experimental results demonstrate that the proposed algorithm effectively reduces service request latency under diverse workloads and network conditions, while maintaining global resource load balancing.