Information-Entropy-Driven Fault Propagation Modeling for Probabilistic Network Performance Prediction

2026-09-08

AI Summary

该研究针对网络故障引发的性能突变问题，提出基于信息熵的故障传播模型IEFP及FEMNet框架，实现复杂故障情景下的概率性网络性能预测。

Abstract

Network faults can trigger cascading effects that cause abrupt and nonstationary performance degradation. Existing learning-based performance predictors mainly focus on normal operation or treat fault-induced topology and routing changes as static inputs, and typically produce deterministic point estimates. They overlook fault-propagation dynamics and uncertainty in performance evolution. The predefined-rule and purely data-driven propagation models lack a unified representation of fault definition, propagation mechanism, and impact quantification. Additionally, generic denoisers in conditional diffusion models fail to incorporate fault propagation into uncertainty modeling. To address these limitations, we propose an information-entropy-driven fault propagation paradigm (IEFP) that characterizes fault propagation via relative entropy, mutual information and transfer entropy. We then design a fault-aware graph message-passing mechanism that propagation contexts modulate network representation learning. We further develop FEMNet, which employs this mechanism as a tailored denoiser within a conditional diffusion model to enable probabilistic network performance prediction under complex fault scenarios. Compared with the strongest baselines, IEFP improves fault-prediction performance, while FEMNet reduces errors in both point and probabilistic KPI prediction.

Problem

• fault propagation
• uncertainty modeling
• network performance prediction

Innovation

• information-entropy-driven
• fault propagation
• graph message-passing
• conditional diffusion model
• probabilistic prediction