Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true root cause is challenging. In this paper, we propose a novel algorithm named NetRCA to deal with this problem. Firstly, we extract effective derived features from the original raw data by considering temporal, directional, attribution, and interaction characteristics. Secondly, we adopt multivariate time series similarity and label propagation to generate new training data from both labeled and unlabeled data to overcome the lack of labeled samples. Thirdly, we design an ensemble model which combines XGBoost, rule set learning, attribution model, and graph algorithm, to fully utilize all data information and enhance performance. Finally, experiments and analysis are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge to demonstrate the superiority and effectiveness of our approach.
翻译:网络断层的根源本地化对于网络运行和维护至关重要。然而,由于网络架构和无线环境复杂,且标签数据有限,准确确定真正根源的本地化具有挑战性。在本文中,我们提出了名为 NetRCA 的新型算法来解决这一问题。首先,我们通过考虑时间、方向、归属和互动特点,从原始原始数据中提取有效的衍生特征。第二,我们采用了多变量时间序列和标签传播,从标签和未标签数据中生成新的培训数据,以克服标签样本的缺乏。第三,我们设计了一个混合模型,将XGBoost、规则设定学习、归属模型和图表算法结合起来,以充分利用所有数据信息,提高性能。最后,我们从ICASSP 2022 AIOps 挑战中对真实世界数据集进行了实验和分析,以展示我们方法的优越性和有效性。