Department of Computer Science
Identifying recurrent and unknown performance issues
© 2014 IEEE.For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.
automated diagnosis, duplication detection, Issue identification, metrics, performance
Source Publication Title
14th IEEE International Conference on Data Mining
Lim, Meng-Hui, Jian-Guang Lou, Hongyu Zhang, Qiang Fu, Andrew Beng Jin Teoh, Qingwei Lin, Rui Ding, and Dongmei Zhang. "Identifying recurrent and unknown performance issues." 14th IEEE International Conference on Data Mining (2014): 320-329.