Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems

TL;DR

This paper presents a transfer learning-based algorithm with domain adversarial mechanisms for fault root cause identification in cloud systems, improving accuracy and robustness under challenging conditions like class imbalance and structural differences.

Contribution

Introduces a novel transfer learning approach with shared feature extraction and domain adversarial training for fault diagnosis in complex cloud environments.

Findings

Outperforms existing methods in accuracy, F1-Score, and AUC.

Maintains high performance under class imbalance and structural heterogeneity.

Enhances robustness and generalization in real-world cloud scenarios.

Abstract

This paper addresses the challenge of fault root cause identification in cloud computing environments. The difficulty arises from complex system structures, dense service coupling, and limited fault information. To solve this problem, an intelligent identification algorithm based on transfer learning is proposed. The method introduces a shared feature extraction module and a domain adversarial mechanism to enable effective knowledge transfer from the source domain to the target domain. This improves the model's discriminative ability and generalization performance in the target domain. The model incorporates a pseudo-label selection strategy. When labeled samples are lacking in the target domain, high-confidence predictions are used in training. This enhances the model's ability to recognize minority classes. To evaluate the stability and adaptability of the method in real-world scenarios, experiments are designed under three conditions: label scarcity, class imbalance, and heterogeneous node environments. Experimental results show that the proposed method outperforms existing mainstream approaches in several key metrics, including accuracy, F1-Score, and AUC. The model demonstrates stronger discriminative power and robustness. Notably, under extreme class imbalance and significant structural differences in the target domain, the model still maintains high performance. This validates the effectiveness and practical value of the proposed mechanisms in complex cloud computing systems.