A Trusted Edge Scheduling Method for Computing Power Network Based on Deep Reinforcement Learning

In multi-party collaborative scenarios where computing power networks and edge intelligence work in synergy, trusted edge scheduling methods optimize resource allocation through intelligent scheduling, ensuring that computing tasks of each participant are executed on independent edge nodes, thus effectively improving computing efficiency and resource utilization. However, distributed edge nodes and their supporting communication networks usually require independent deployment and operation and maintenance, making efficient scheduling a critical challenge. This paper proposes a trusted edge scheduling method based on deep reinforcement learning, which realizes privacy protection through smart contract technology. Computing and network resources are stored on the blockchain, scheduling is performed using the Edge Routing Model (ERM) in smart contracts, and the hash values of scheduling results are recorded on the blockchain. ERM aims to meet users’ computing demands while selecting the optimal routing strategy to maximize network bandwidth utilization. The method adopts deep reinforcement learning with a prioritized sampling mechanism to constrain the action space, and combines graph neural networks to learn features of network topology, thereby enhancing the generalization ability. Experimental results show that, compared with traditional scheduling models, ERM can schedule edge resources more efficiently under both homogeneous and heterogeneous topologies and exhibits favorable scalability.