Abstract
Computer problem diagnosis remains a serious challenge to users and support professionals. Traditional troubleshooting methods relying heavily on human intervention make the process inefficient and the results inaccurate even for solved problems, which contribute significantly to user's dissatisfaction. We propose to use system behavior information such as system event traces to build correlations with solved problems, instead of using only vague text descriptions as in existing practices. The goal is to enable automatic identification of the root cause of a problem if it is a known one, which would further lead to its resolution. By applying statistical learning techniques to classifying system call sequences, we show our approach can achieve considerable accuracy of root cause recognition by studying four case examples. © 2006 Authors.
| Original language | English |
|---|---|
| Pages (from-to) | 375-388 |
| Journal | Operating Systems Review (ACM) |
| Volume | 40 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Oct 2006 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- root cause analysis
- support vector machine
- system call sequences