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.

本文提出了一种名为 NetRCA 的算法来解决网络错误本源的准确定位问题，该算法从原始数据中提取有效的派生特征，采用多元时间序列相似性和标签传播来增加数据，最后采用合奏模型来提高性能，并在 ICASSP 2022 AIOps Challenge 数据集上进行实验验证。