Performance issue diagnosis for online service systems

Qiang Fu, Jian Guang Lou, Qing Wei Lin, Rui Ding, Dongmei Zhang, Zihao Ye, Tao Xie

Research output: Contribution to journalConference articlepeer-review


Monitoring and diagnosing performance issues of an online service system are critical to assure satisfactory performance of the system. Given a detected performance issue and collected system metrics for an online service system, engineers usually need to make great efforts to conduct diagnosis by first identifying performance issue beacons, which are metrics that pinpoint to the root causes. In order to reduce the manual efforts, in this paper, we propose a new approach to effectively detecting performance issue beacons to help with performance issue diagnosis. Our approach includes techniques for mining system metric data to address limitations when applying previous classification-based approaches. Our evaluations on both a controlled environment and a real production environment show that our approach can more effectively identify performance issue beacons from system metric data than previous approaches.

Original languageEnglish (US)
Article number6424866
Pages (from-to)273-278
Number of pages6
JournalProceedings of the IEEE Symposium on Reliable Distributed Systems
StatePublished - 2012
Externally publishedYes
Event31st IEEE International Symposium on Reliable Distributed Systems, SRDS 2012 - Irvine, CA, United States
Duration: Oct 8 2012Oct 11 2012


  • class association rule
  • monitoring data analysis
  • performance issue diagnosis

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications


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