On the Difficulties of Supervised Event Prediction based on Unbalanced Real-World Data in Multi-System Monitoring
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings 10th Symposium on Software Performance (SSP 2019)
Original Kurzfassung:
Online failure prediction of performance-critical
events is an important task in fault management of
software systems. In this paper, we extend our previous multi-system event prediction by analyzing its
performance on unbalanced, real-world data, which
represents a realistic online scenario. We train a random forest classifier with different data preprocessing
configurations, including data augmentation to cope
with the extreme class imbalance. The results reveal
that the prediction quality of the tested multi-system
model drops significantly compared to the balanced
scenario. Although our supervised event prediction
approach as well as different data preprocessing configurations turned out to be ineffective, we consider
the insights of our work valuable for the community.