Deep Reinforcement Learning Microservice Deployment Algorithm Based on Simulated Annealing Optimization in Edge Computing Scenarios

Abstract

With the rapid development of microservice architectures, edge computing has become an important paradigm for service deployment. However, microservice deployment in edge environments still faces challenges such as resource constraints, network latency, bandwidth limitations, and load balancing. Achieving efficient and scalable deployment requires effective coordination of distributed resources under dynamic and heterogeneous conditions. Existing studies often overlook the time‐varying characteristics and resource heterogeneity of edge environments and lack a unified optimization framework for service quality, system stability, and migration cost. To address these issues, this paper proposes an efficient microservice deployment algorithm that combines Simulated Annealing (SA) and Deep Reinforcement Learning (DRL). The proposed SA‐DRL hybrid method monitors resource usage and network conditions in real time and dynamically adjusts deployment decisions to optimize service quality and resource utilization. Compared with three representative deployment baselines, the proposed SA‐DRL method achieves better overall performance in terms of convergence speed, resource balance, and robustness. It also maintains high efficiency and adaptability under scenarios of node‐scale expansion and workload increase. Experimental results demonstrate that the proposed method provides an effective solution for microservice deployment optimization in edge computing environments.