Chaoli Zhang, Zhiqiang Zhou, Yingying Zhang, Linxiao Yang, Kai He, Qingsong Wen, Liang Sun
备注：
Accepted by ICASSP 2022. NetRCA is the solution of the First Place of 2022 ICASSP AIOps Challenge. All authors are contributed equally, and Qingsong Wen is the team leader (Team Name: MindOps). The website of 2022 ICASSP AIOps Challenge is https://www.aiops.sribd.cn/home/introduction
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.
String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems
Huawei Montreal Research Centre, Huawei Technologies Canada Co., LTD（华为蒙特利尔研究中心，华为技术加拿大有限公司）；Department of Electrical & Computer Engineering, University of Toronto（电气与计算机工程系，多伦多大学）
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Arizona（亚利桑那大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；Huazhong University of Science and Technology（华中科技大学）；University of Waterloo（滑铁卢大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
ADVA Optical Networking SE（ADVA光网络公司）；Kiel University (CAU)（基尔大学（克里斯蒂安-阿尔布雷希茨大学））
Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protection
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany（模式识别实验室，弗里德里希-亚历山大-埃森哲大学埃尔朗根-纽伦堡，德国）；Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany（电气能源系统研究所，弗里德里希-亚历山大-埃森哲大学埃尔朗根-纽伦堡，德国）；Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden, Germany（电气工程、媒体与计算机科学系，奥斯特拜尔技术大学阿姆伯-魏登，德国）
Order Matters! An Empirical Study on Large Language Models' Input Order Bias in Software Fault Localization
Software Performance, Analysis, \ Reliability (SPEAR) Lab\ University Montreal Canada；DePaul University Chicago USA；University of Manitoba Winnipeg Canada；Software Performance, Analysis, \ Reliability (SPEAR) Lab\ University；DePaul University；University of Manitoba
BALANCE: Bayesian Linear Attribution for Root Cause Localization
Ant Group（蚂蚁集团）；Ocean Base（OceanBase（海洋基））
根本原因分析（RCA）在分布式数据系统的维护和操作中起着不可或缺的作用，因为它在故障检测和系统恢复之间架起了一座桥梁。现有的工作主要研究多维定位或基于图的根本原因定位。本文开辟了利用最近开发的可解释人工智能（XAI）的RCA的目的框架的可能性。特别是，我们提出了BALANCE（BALANCE Linear Attribution for root CausE localization），它通过XAI中的归因镜头来阐述RCA问题，并试图通过候选根本原因的行为来解释目标KPI中的异常。平衡由三个创新组成部分。首先，我们提出了一个贝叶斯多共线性特征选择（BMFS）模型预测目标KPI给定的候选根本原因，以向前的方式，同时促进稀疏性，并同时注意候选根本原因之间的相关性。其次，我们引入归因分析，以向后的方式计算每个候选人的归因得分。第三，如果有多个KPI，我们合并与每个KPI相关的估计根本原因。我们广泛评估了一个合成数据集以及三个现实世界的RCA任务，即坏SQL本地化，容器故障定位和故障类型诊断Exathlon的建议的平衡方法。结果表明，BALAANCE在准确性方面优于最先进的（SOTA）方法，并且运行时间最少，并且对于实际任务至少实现了比SOTA方法显着更高的准确性的$6\%$。BALANCE已被部署到生产环境中，以解决现实世界的RCA问题，在线结果进一步倡导将其用于分布式数据系统中的实时诊…
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京国家信息科学与技术研究中心（BNRist））；Tianjin University（天津大学）；Nankai University（南开大学）；Bizseer（必示科技）；China Construction Bank（中国建设银行）
