Problem
Research questions and friction points this paper is trying to address.
fault propagation
uncertainty modeling
network performance prediction
Information-Entropy-Driven Fault Propagation Modeling for Probabilistic Network Performance Prediction
๐ค AI Summary
่ฏฅ็ ็ฉถ้ๅฏน็ฝ็ปๆ
้ๅผๅ็ๆง่ฝ็ชๅ้ฎ้ข๏ผๆๅบๅบไบไฟกๆฏ็ต็ๆ
้ไผ ๆญๆจกๅIEFPๅFEMNetๆกๆถ๏ผๅฎ็ฐๅคๆๆ
้ๆ
ๆฏไธ็ๆฆ็ๆง็ฝ็ปๆง่ฝ้ขๆตใ
๐ 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
information-entropy-driven
fault propagation
graph message-passing
conditional diffusion model
probabilistic prediction
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