This program is tentative and subject to change.

Wed 17 Jul 2024 16:18 - 16:36 at Baobá 8 - Fault Diagnosis and Root Cause Analysis 1

Detecting failures and identifying their root causes promptly and accurately is crucial for ensuring the availability of microservice systems. A typical failure troubleshooting pipeline for microservices consists of two phases: anomaly detection and root cause analysis. While various existing works on root cause analysis require accurate anomaly detection, there is no guarantee of accurate estimation with anomaly detection techniques. Inaccurate anomaly detection results can significantly affect the root cause localization results. To address this challenge, we propose BARO, an end-to-end approach that integrates anomaly detection and root cause analysis for effectively troubleshooting failures in microservice systems. BARO leverages the Multivariate Bayesian Online Change Point Detection technique to model the dependency within multivariate time-series metrics data, enabling it to detect anomalies more accurately. BARO also incorporates a novel nonparametric statistical hypothesis testing technique for robustly identifying root causes, which is less sensitive to the accuracy of anomaly detection compared to existing works. Our comprehensive experiments conducted on three popular benchmark microservice systems demonstrate that BARO consistently outperforms state-of-the-art approaches in both anomaly detection and root cause analysis.

This program is tentative and subject to change.

Wed 17 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

16:00 - 18:00
Fault Diagnosis and Root Cause Analysis 1Research Papers / Industry Papers at Baobá 8
16:00
18m
Talk
A Quantitative and Qualitative Evaluation of LLM-based Explainable Fault Localization
Research Papers
Sungmin Kang Korea Advanced Institute of Science and Technology, Gabin An Korea Advanced Institute of Science and Technology, Shin Yoo Korea Advanced Institute of Science and Technology
Pre-print
16:18
18m
Talk
BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection
Research Papers
Luan Pham RMIT University, Huong Ha RMIT University, Hongyu Zhang Chongqing University
Pre-print
16:36
18m
Talk
Fault Diagnosis for Test Alarms in Microservices Through Multi-source Data
Industry Papers
Shenglin Zhang Nankai University, Jun Zhu Nankai University, Bowen Hao Nankai University, Yongqian Sun Nankai University, Xiaohui Nie CNIC, CAS, Jingwen Zhu Nankai University, Xilin Liu Huawei Cloud, Xiaoqian Li Huawei Cloud, Yuchi Ma Huawei Cloud Computing Technologies CO., LTD., Dan Pei Tsinghua University
16:54
18m
Talk
Costs and Benefits of Machine Learning Software Defect Prediction: Industrial Case Study
Industry Papers
Szymon Stradowski Wroclaw University of Science and Technology & NOKIA, Lech Madeyski Wroclaw University of Science and Technology
17:12
18m
Talk
Chain-of-Event: Interpretable Root Cause Analysis for Microservices through Automatically Learning Weighted Event Causal Graph
Industry Papers
Yao Zhenhe Tsinghua University, Changhua Pei Computer Network Information Center at Chinese Academy of Sciences, Wenxiao Chen Tsinghua University, Hanzhang Wang Walmart Global Tech, Liangfei Su eBay, USA, Huai Jiang eBay, USA, Zhe Xie Tsinghua University, Xiaohui Nie CNIC, CAS, Dan Pei Tsinghua University
17:30
18m
Talk
ChangeRCA: Finding Root Causes from Software Changes in Large Online Systems
Research Papers
Guangba  Yu Sun Yat-sen University, Pengfei Chen Sun Yat-sen University, Zilong He Sun Yat-sen University, Qiuyu Yan Tencent, Yu Luo Tencent, Fangyuan Li Tencent, Zibin Zheng Sun Yat-sen University
DOI Pre-print