The present application relates to the technical field of computing
resource scheduling, in particular to a computing network task dynamic scheduling method and
system. Historical scheduling trajectories are obtained to form a historical scheduling trajectory dataset; a high-quality causal trajectory set is formed based on the historical scheduling trajectory dataset; the high-quality causal trajectory set is divided into M trajectory subsets, and M sub-strategy networks are trained; during training, a
loss function weight is configured based on a trajectory quality weight; a meta-strategy network integrating the M sub-strategy networks is constructed and trained using a model-independent meta-learning framework; a dynamic weight vector is dynamically generated according to an environment state
feature vector and a task
feature vector, and a scheduling action is output based on the dynamic weight vector; the meta-strategy network is deployed to a computing
network scheduler to process real-time scheduling requests containing environment state feature vectors and task feature vectors. The present application can achieve high accuracy, strong robustness and fast adaptability in computing network task scheduling.