标题： 面向云计算系统故障根本原因识别的领域对抗迁移学习 显示英文标题

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

摘要： 本文解决了云计算环境中故障根本原因识别的挑战。 困难来自于复杂的系统结构、密集的服务耦合和有限的故障信息。 为了解决这个问题，提出了一种基于迁移学习的智能识别算法。 该方法引入了一个共享特征提取模块和领域对抗机制，以实现从源域到目标域的有效知识迁移。 这提高了模型在目标域中的判别能力和泛化性能。 该模型结合了一种伪标签选择策略。 当目标域中缺乏标记样本时，在训练中使用高置信度的预测结果。 这增强了模型识别少数类的能力。 为了评估该方法在现实场景中的稳定性和适应性，设计了三种条件下的实验：标签稀缺、类别不平衡和异构节点环境。 实验结果表明，所提出的方法在多个关键指标上优于现有的主流方法，包括准确率、F1分数和AUC。 该模型表现出更强的判别能力和鲁棒性。 值得注意的是，在极端类别不平衡和目标域显著结构差异的情况下，该模型仍保持高性能。 这验证了所提出机制在复杂云计算系统中的有效性和实用价值。 显示英文摘要

摘要： 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.