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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia

The complexity and dynamism of microservices pose significant challenges to system reliability, and thereby, automated troubleshooting is crucial. However, two significant issues rest in existing approaches: (1) Microservices generate traces, system logs, and key performance indicators (KPIs), but existing approaches usually consider traces only, failing to understand the system fully as traces cannot depict all anomalies; (2) Troubleshooting microservices generally contains two main phases, i.e., anomaly detection and root cause localization. Existing studies regard these two phases as independent, ignoring their close correlation. Even worse, inaccurate detection results can deeply affect localization effectiveness. To overcome these limitations, we propose Eadro, the first end-to-end framework to integrate anomaly detection and root cause localization based on multi-source data for troubleshooting large-scale microservices. The key insights of Eadro are the anomaly manifestations on different data sources and the close connection between detection and localization. Thus, Eadro models intra-service behaviors and inter-service dependencies from traces, logs, and KPIs, all the while leveraging the shared knowledge of the two phases via multi-task learning. Experiments on two widely-used benchmark microservices demonstrate that Eadro outperforms state-of-the-art approaches by a large margin, and show the effectiveness of integrating multi-source data. We also release our code and data to facilitate future research.

Fri 19 May

Displayed time zone: Hobart change

11:00 - 12:30
11:00
15m
Talk
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention
Technical Track
Cheryl Lee The Chinese University of Hong Kong, Tianyi Yang The Chinese University of Hong Kong, Zhuangbin Chen Chinese University of Hong Kong, China, Yuxin Su Sun Yat-sen University, Yongqiang Yang Huawei Technologies, Michael Lyu The Chinese University of Hong Kong
Pre-print
11:15
15m
Talk
Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models
Technical Track
Toufique Ahmed University of California at Davis, Supriyo Ghosh Microsoft, Chetan Bansal Microsoft Research, Thomas Zimmermann Microsoft Research, Xuchao Zhang Microsoft, Saravanakumar Rajmohan Microsoft 365
Pre-print
11:30
15m
Talk
Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data
Technical Track
Cheryl Lee The Chinese University of Hong Kong, Tianyi Yang The Chinese University of Hong Kong, Zhuangbin Chen Chinese University of Hong Kong, China, Yuxin Su Sun Yat-sen University, Michael Lyu The Chinese University of Hong Kong
Pre-print
11:45
15m
Talk
LogReducer: Identify and Reduce Log Hotspots in Kernel on the Fly
Technical Track
Guangba  Yu Sun Yat-Sen University, Pengfei Chen Sun Yat-Sen University, Pairui Li Tencent Inc., Tianjun Weng Tencent Inc., Haibing Zheng Tencent, Yuetang Deng Tencent, Zibin Zheng School of Software Engineering, Sun Yat-sen University
Pre-print
12:00
15m
Talk
TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems
SEIP - Software Engineering in Practice
Zhengran Zeng Southern University of Science and Technology, Yuqun Zhang Southern University of Science and Technology, Yong Xu Microsoft Research, Minghua Ma Microsoft Research, Bo Qiao Microsoft Research, Wentao Zou , Qingjun Chen , Meng Zhang , Xu Zhang Microsoft Research, Hongyu Zhang The University of Newcastle, Xuedong Gao , Hao Fan , Saravan Rajmohan Microsoft 365, Qingwei Lin Microsoft Research, Dongmei Zhang Microsoft Research
12:15
7m
Talk
ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems
Journal-First Papers
Danny Weyns KU Leuven, M. Usman Iftikhar KU Leuven / Linnaeus University
12:22
7m
Talk
Auto-Logging: AI-centred Logging Instrumentation
NIER - New Ideas and Emerging Results
Jasmin Bogatinovski Technical University Berlin, Odej  Kao Technische Universität Berlin
Pre-print