Enhance routing efficiency in dynamic edge computing environments through multi-agents optimization

As edge computing continues to evolve, addressing the inherent challenges of low stability in edge devices becomes imperative for optimizing routing efficiency. This paper introduces a novel approach to tackle this issue through the application of Reinforcement Learning (RL) for routing optimization in dynamic edge computing environments. The core problem revolves around the need for an algorithm capable of selecting edge devices strategically to minimize latency. To validate our proposed solution, we conducted experiments utilizing Oliver30 and ry48p datasets. Comparative analysis against traditional heuristic algorithms, such as Simulated Annealing (SA) and Ant Colony Optimization (ACO), demonstrates the superior performance of our RL-based model. The results highlight the effectiveness of leveraging advanced machine learning techniques to enhance routing efficiency in challenging edge computing scenarios.