Predicting Critical Problems from
Execution Logs of a Large-Scale Software System
Beszédes, Lajos Jenő Fülöp and Tibor
The possibility of automatically predicting runtime
failures in large-scale distributed systems such as critical slowdown
is highly desirable, since this way a significant amount of manual
effort can be saved. Based on the analysis of execution logs, a large
amount of information can be gained for the purpose of prediction.
Existing approaches - which are often based on achievements in Complex
Event Processing - rarely employ intelligent analyses such as machine
learning for the prediction. Predictive Analytics on the other hand,
deals with analyzing past data in order to predict future events. We
have developed a framework for our industrial partner to predict
critical failures in their large-scale telecommunication software
system. The framework is based on some existing solutions but include
novel techniques as well. In this work, we overview the methods and
present initial experimental evaluation.
Analytics, Complex Event Processing, predicting runtime failures,
machine learning, execution log processing.