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

Modern systems generate a massive amount of logs to detect and diagnose system faults, which incurs expensive storage cost and runtime overhead. After investigating real-world production logs, we observe that most of the logging overhead is due to a small number of log templates, referred to as log hotspots. Therefore, we conduct a systematical study about log hotspots in an industrial system $\mathcal{W}$, which motivates us to identify log hotspots and reduce them on the fly. In this paper, we propose \textit{LogReducer}, a non-intrusive and language-independent log reduction framework based on eBPF (Extended Berkeley Packet Filter), consisting of both online and offline processes. After two months of serving the offline process of \textit{LogReducer} in $\mathcal{W}$, the log storage overhead has dropped from 19.7 PB per day to 12.0 PB (i.e., about a 39.08% decrease). Practical implementation and experimental evaluations in the test environment demonstrate that the online process of \textit{LogReducer} can control the logging overhead of hotspots while preserving logging effectiveness. Moreover, the log hotspot handling time can be reduced from average 9 days in production to 10 minutes in the test with the help of \textit{LogReducer}.

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