TY - GEN
T1 - Runtime prediction of failure modes from system error logs
AU - Mohamed, Atef
AU - Zulkernine, Mohammad
N1 - Predicting potential failure occurrences during runtime is important to achieve system resilience and avoid hazardous consequences of failures. Existing failure prediction techniques in software systems involve forecasting failure counts, effects, and occurrences. Most of these techniques predict failures that may occur in future runtime intervals and only few techniques predict them at runtime.
PY - 2013
Y1 - 2013
N2 - Predicting potential failure occurrences during runtime is important to achieve system resilience and avoid hazardous consequences of failures. Existing failure prediction techniques in software systems involve forecasting failure counts, effects, and occurrences. Most of these techniques predict failures that may occur in future runtime intervals and only few techniques predict them at runtime. However, they do not estimate the failure modes and they require extensive instrumentation of source code. In this paper, we provide an approach for predicting failure occurrences and modes during system runtime. Our methodology utilizes system error log records to craft runtime error-spread signature. Using system error log history, we determine a predictive function (estimator) for each failure mode based on these signatures. This estimator can be used to predict a failure mode eventuality measure (a probability of failure mode occurrence) from system error log during system runtime. An experimental evaluation using PostgreSQL opensource database is provided. Our results show high accuracy of failure occurrence and mode predictions.
AB - Predicting potential failure occurrences during runtime is important to achieve system resilience and avoid hazardous consequences of failures. Existing failure prediction techniques in software systems involve forecasting failure counts, effects, and occurrences. Most of these techniques predict failures that may occur in future runtime intervals and only few techniques predict them at runtime. However, they do not estimate the failure modes and they require extensive instrumentation of source code. In this paper, we provide an approach for predicting failure occurrences and modes during system runtime. Our methodology utilizes system error log records to craft runtime error-spread signature. Using system error log history, we determine a predictive function (estimator) for each failure mode based on these signatures. This estimator can be used to predict a failure mode eventuality measure (a probability of failure mode occurrence) from system error log during system runtime. An experimental evaluation using PostgreSQL opensource database is provided. Our results show high accuracy of failure occurrence and mode predictions.
KW - failure mode
KW - failure prediction
KW - regression analysis
KW - runtime error log
KW - software reliability
UR - http://www.scopus.com/inward/record.url?scp=84885209573&partnerID=8YFLogxK
U2 - 10.1109/ICECCS.2013.41
DO - 10.1109/ICECCS.2013.41
M3 - Conference article
SN - 9780769550077
T3 - Proceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
SP - 232
EP - 241
BT - Proceedings - 2013 International Conference on Engineering of Complex Computer Systems, ICECCS 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Engineering of Complex Computer Systems, ICECCS 2013
Y2 - 17 July 2013 through 19 July 2013
ER -