Affiliations: [a] Information Engineering College, Nanchang University, Jiangxi, China | [b] Department of Computer Engineering, Honam University, Gwangju, South Korea | [c] Beijing Institute of Technology, Beijing, China | [d] Nanjing Audit University, Jiangsu, China | [e] Beijing University of Technology, Beijing, China | [f] Industrial Institute of Artificial Intelligence, Nanchang University, Jiangxi, China
Abstract: Localizing the root cause of network faults is crucial to network operation and maintenance. Operational expenses will be saved if the root cause can be identified accurately. However, due to the complicated wireless environments and network architectures, accurate root cause localization of network falut meets the difficulties including missing data, hybrid fault behaviors, and short of well-labeled data. In this study, global and local features are constructed to make new feature representation for data sample, which can highlight the temporal characteristics and contextual information of the root cause analysis data. A hybrid tree model (HTM) ensembled by CatBoost, XGBoost and LightGBM is proposed to interpret the hybrid fault behaviors from several perspectives and discriminate different root causes. Based on the combination of global and local features, a semi-supervised training strategy is utilized to train the HTM for dealing with short of well-labeled data. The experiments are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge, and the results show that the global and local feature based HTM achieves the best model performance comparing with other models. Meanwhile, our solution achieves third place in the competition leaderboard which shows the model effectiveness.
Keywords: Global feature, local feature, hybrid tree model, root cause analysis, network fault localization
