We propose a framework designed to tackle a multi-objective optimization challenge related to the placement of applications in fog computing, employing a deep reinforcement learning (DRL) approach. Unlike other optimization techniques, such as integer linear programming or genetic algorithms, DRL models are applied in real time to solve similar problem situations after training. Our model comprises a learning process featuring a graph neural network and two actor-critics, providing a holistic perspective on the priorities concerning interconnected services that constitute an application. The learning model incorporates the relationships between services as a crucial factor in placement decisions: Services with higher dependencies take precedence in location selection. Our experimental investigation involves illustrative cases where we compare our results with baseline strategies and genetic algorithms. We observed a comparable Pareto set with negligible execution times, measured in the order of milliseconds, in contrast to the hours required by alternative approaches.

翻译：我们提出了一种采用深度强化学习(DRL)方法的框架，旨在解决雾计算中应用放置的多目标优化挑战。与整数线性规划或遗传算法等其他优化技术不同，DRL模型在训练后可实时应用于解决相似问题场景。我们的模型包含一个由图神经网络和两个行动者-评论者组成的训练过程，能够从全局视角审视构成应用的互联服务优先级。该学习模型将服务间关系作为放置决策的关键因素：依赖度更高的服务在位置选择中具有优先权。实验研究通过典型案例将我们的结果与基线策略和遗传算法进行对比。我们观察到，与替代方法需要数小时相比，本文方法能生成可比较的帕累托解集，且执行时间可忽略不计（毫秒级）。