TY - GEN
T1 - Automated known problem diagnosis with event traces
AU - Yuan, Chun
AU - Lao, Ni
AU - Wen, Ji-Rong
AU - Li, Jiwei
AU - Zhang, Zheng
AU - Wang, Yi-Min
AU - Ma, Wei-Ying
N1 - 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].
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Root cause analysis
KW - Support vector machine
KW - System call sequences
UR - http://www.scopus.com/inward/record.url?scp=34748888898&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34748888898&origin=recordpage
U2 - 10.1145/1217935.1217972
DO - 10.1145/1217935.1217972
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1595933220
SN - 9781595933225
T3 - Proceedings of the 2006 EuroSys Conference
SP - 375
EP - 388
BT - Proceedings of the 2006 EuroSys Conference
T2 - 2006 EuroSys Conference
Y2 - 18 April 2006 through 21 April 2006
ER -