Details

Autor(en) / Beteiligte

• Jing, Pengkun
• Han, Yanni
• Sun, Jiyan
• Lin, Tao
• Hu, Yanjie

Titel

AutoRoot: A Novel Fault Localization Schema of Multi-dimensional Root Causes

Ist Teil von

• 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021, p.1-7

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• The key challenge for large scale software system maintenance is to minimize the troubleshooting time when severe system anomaly (e.g., server failure, link congestion, software bugs) happens. It often takes hours for operators to manually locate the fault and thus degrades the service performance in terms of user experience and economics. Previous root cause localization algorithms are usually time-consuming and error-prone. In this paper, we present AutoRoot, a fast and accurate multi-dimensional root cause localization algorithm. Specifically, AutoRoot uses an adaptive density clustering to improve the accuracy and an effective filtering mechanism to reduce the search time. Extensive experiments using multiple real data traces validate the performance of AutoRoot compared with existing algorithms.