AutoRoot: A Novel Fault Localization Schema of Multi-dimensional Root Causes
Pengkun Jing, Yanni Han, Jiyan Sun, Tao Lin, Yanjie Hu
Abstract
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.