Earticle

초록

영어

Log data is a valuable resource for failure prediction and troubleshooting in large-scale systems. However, with the rapid growth of the system scale and the popularity of various applications in productional environments, the volume of logs emerged per day becomes huge, posing serious challenges for storage and analysis. To solve these problems, we propose an online log filtering mechanism to eliminate the redundant and noisy log records through event filtering and instance filtering, aiming to minimize the log size without losing important information required for the fault diagnosis. Our proposed log filtering is evaluated on a real log data derived from a productional cloud computing system, observing that over 76% of the storage space are saved without losing important information.

목차

Abstract
 1. Introduction
 2. Background
  2.1. Overview of the System Log
  2.2. Anomaly Detection Algorithm
 3. Filtering Procedures
  3.1. Event Filtering
  3.2. Instance Filtering
 4. Experiments
  4.1. Evaluation Metric
  4.2. Results and Analysis
 5. Related Work
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Li Zhou [ School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou, China ]
• Baojin Zhu [ School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou, China ]
• Xiaopeng Zheng [ China Academy of Launch Vehicle Technology, Beijing, China ]
• Liye Zhang [ China Academy of Launch Vehicle Technology, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605