发表机构
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对网络故障级联效应导致的性能退化预测问题，提出信息熵驱动的故障传播范式IEFP及基于条件扩散模型的FEMNet，实现概率性网络性能预测并降低预测误差。
AI 中文摘要
网络故障可能引发级联效应，导致性能出现突发的、非平稳的退化。现有的基于学习的性能预测器主要关注正常操作，或将故障引起的拓扑和路由变化视为静态输入，且通常产生确定性的点估计。它们忽略了故障传播动态和性能演化中的不确定性。基于预定义规则和纯数据驱动的传播模型缺乏对故障定义、传播机制和影响量化的统一表示。此外，条件扩散模型中的通用去噪器未能将故障传播纳入不确定性建模。为解决这些局限性，我们提出了一种信息熵驱动的故障传播范式（IEFP），通过相对熵、互信息和传递熵来表征故障传播。然后，我们设计了一种故障感知的图消息传递机制，使传播上下文能够调节网络表示学习。我们进一步开发了FEMNet，该模型在条件扩散模型中将此机制用作定制去噪器，以实现复杂故障场景下的概率性网络性能预测。与最强基线相比，IEFP提升了故障预测性能，而FEMNet在点预测和概率性KPI预测方面均降低了误差。
英文摘要
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.
