Hierarchical Collaborative Resource Scheduling in Industrial Internet of Things based on Graph Neural Networks and Deep Reinforcement Learning

The hierarchical cooperative resource scheduling architecture provides a promising direction for efficient collaborative processing of edge computing under the dynamic and intricate landscape of the Industrial Internet of Things (IIoT). However, existing scheduling algorithms often struggle to effectively capture the intricate information features inherent in hierarchical and collaborative domains, leading to suboptimal solutions. To tackle this challenge, we introduce a novel hierarchical cooperative resource scheduling framework based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL). We first leverage hierarchical GNN to facilitate seamless information exchange among internal nodes and adjacent nodes between layers in the hierarchical structure and transform it into node embeddings. These meticulously designed embeddings are then input into the policy model of DRL for the iterative learning process to generate higher-quality solutions by leveraging global feature information. Experiment results unequivocally demonstrate the superiority of our approach over baselines in terms of scheduling performance. Furthermore, our model exhibits robust generalization capabilities across various scenarios.