SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice
In real-world microservice systems, the newly deployed service - one kind of change service, could lead to a new type of minority fault. Existing state-of-the-art (SOTA) methods for fault localization rarely consider the imbalanced fault classification in change service. This paper proposes a novel method that utilizes decision rule sets to deal with highly imbalanced data by optimizing the F1 score subject to cardinality constraints. The proposed method greedily generates the rule with maximal marginal gain and uses an efficient minorize-maximization (MM) approach to select rules iteratively, maximizing a non-monotone submodular lower bound. Compared with existing fault localization algorithms, our algorithm can adapt to the imbalanced fault scenario of change service, and provide interpretable fault causes which are easy to understand and verify. Our method can also be deployed in the online training setting, with only about 15% training overhead compared to the current SOTA methods. Empirical studies demonstrate the superior performance of our algorithm to existing fault localization algorithms in terms of both accuracy and model interpretability.
Wed 30 OctDisplayed time zone: Pacific Time (US & Canada) change
13:30 - 15:00 | Anomaly and fault detectionResearch Papers / NIER Track at Compagno Chair(s): Xing Hu Zhejiang University | ||
13:30 15mTalk | SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice Research Papers Rui Ren DAMO Academy, Alibaba Group Hangzhou, China, Jingbang Yang DAMO Academy, Alibaba Group Hangzhou, China, Linxiao Yang DAMO Academy, Alibaba Group Hangzhou, China, Xinyue Gu DAMO Academy, Alibaba Group Hangzhou, China, Liang Sun DAMO Academy, Alibaba Group Hangzhou, China | ||
13:45 15mTalk | ART: A Unified Unsupervised Framework for Incident Management in Microservice Systems Research Papers Yongqian Sun Nankai University, Binpeng Shi Nankai University, Mingyu Mao Nankai University, Minghua Ma Microsoft Research, Sibo Xia Nankai University, Shenglin Zhang Nankai University, Dan Pei Tsinghua University | ||
14:00 15mTalk | Detecting and Explaining Anomalies Caused by Web Tamper Attacks via Building Consistency-based Normality Research Papers Yifan Liao Shanghai Jiao Tong University / National University of Singapore, Ming Xu Shanghai Jiao Tong University / National University of Singapore, Yun Lin Shanghai Jiao Tong University, Xiwen Teoh National University of Singapore, Xiaofei Xie Singapore Management University, Ruitao Feng Singapore Management University, Frank Liauw Government Technology Agency Singapore, Hongyu Zhang Chongqing University, Jin Song Dong National University of Singapore DOI Pre-print | ||
14:15 15mTalk | End-to-End AutoML for Unsupervised Log Anomaly Detection Research Papers Shenglin Zhang Nankai University, Yuhe Ji Nankai University, Jiaqi Luan Nankai University, Xiaohui Nie Computer Network Information Center at Chinese Academy of Sciences, Zi`ang Cheng Nankai University, Minghua Ma Microsoft Research, Yongqian Sun Nankai University, Dan Pei Tsinghua University | ||
14:30 10mTalk | Trident: Detecting SQL Injection Attacks via Abstract Syntax Tree-based Neural Network NIER Track Yuanlin Li Tsinghua University, Zhiwei Xu Tsinghua University, Min Zhou Tsinghua University, Hai Wan Tsinghua University, Xibin Zhao Tsinghua University | ||
14:40 10mTalk | A vision on a methodology for the application of an Intrusion Detection System for satellites NIER Track Sébastien Gios UCLouvain, Charles-Henry Bertrand Van Ouytsel UCLouvain, Mark Diamantino Caribé Telespazio - ESA, Axel Legay Université Catholique de Louvain, Belgium DOI | ||