Failure Categorization for Problem Diagnosis on Exception-Based Software Systems

DOI

10.2991/iccsee.2013.32How to use a DOI?

Keywords

log analysis, job clustering, message categorization, problem diagnosis

Abstract

Traditionally, distributed system software developers print log messages when creating a program to track the runtime status of a system to help identify where problems may have occurred while the program is running. People often use system logs produced by distributed systems for troubleshooting and problem diagnosis. However, there may be thousands of failed jobs occurring within a short time. Manually inspecting these jobs one by one to detect anomalies is unfeasible due to the increasing scale and complexity of distributed systems. Since many failed jobs may have the same cause, there is a great demand for automatic job categorization techniques based on log analysis to help developers prioritize job investigation. Described herein is an unstructured log analysis technique for job categorization. In the technique, we propose a novel algorithm to categorize log messages into different categories without heavily relying on application specific knowledge, based on which jobs can be categorized.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuhai Li
PY  - 2013/03
DA  - 2013/03
TI  - Failure Categorization for Problem Diagnosis on Exception-Based Software Systems
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 122
EP  - 125
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.32
DO  - 10.2991/iccsee.2013.32
ID  - Li2013/03
ER  -