Resource Scheduling and Optimization Algorithm of Communication Network for Edge Computing
Traditional resource scheduling strategies fail to fully utilize the computing and storage resources of edge nodes, leading to resource waste and overload of some nodes. This paper collects network topology and edge node resource information data for preprocessing. Then, a state space and action space are defined to record all possible system states and scheduling decisions in the edge computing environment. A two-layer deep Q-network model is constructed for action selection and Q-value calculation. Using 10-fold cross-validation and averaging, the optimized resource utilization reaches 85.18%, communication latency is $\mathbf{7 9. 1 m s}$, load balancing is improved to 0.841, and energy consumption and rejection rate are reduced to varying degrees, fully demonstrating the advantages of this algorithm in optimizing communication network resource scheduling in edge computing environments.