Hao Fang

In modern operating systems, CPU scheduling policy selection and evaluation still rely mainly on heuristic methods, especially at the single-processor level or the abstract ready-queue level, and there is still a lack of systematic modeling and interpretable analysis for complex workload patterns. Traditional approaches are easy to implement and respond quickly in specific scenarios, but they often fail to remain stable under dynamic workloads and high-dimensional features, which can harm generalization. In this work, we build a simulation dataset that covers five typical scheduling policies, redesign a deep learning framework for scheduling policy identification, and propose the MCFCTransformer-DD model. The model extends the standard Transformer with multiscale convolution, frequency-domain augmentation, and cross-attention to capture both low-frequency and high-frequency signals, learn local and global patterns, and model multivariate dependencies. We also introduce a Dendrite Network, or DD, into scheduling policy identification and decision support for the first time, and its gated dendritic structure provides a more transparent nonlinear decision boundary that reduces the black-box nature of deep models and helps mitigate overfitting. Experiments show that MCFCTransformer-DD achieves 94.50% accuracy, a 94.65% F1 score, and an AUROC of 1.00, which indicates strong policy identification performance and strong potential for decision support.