Can we Predict Performance Events with Time Series Data from Monitoring Multiple Systems?

Andreas Schörgenhumer, Mario Kahlhofer, Paul Grünbacher, Hanspeter Mössenböck

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering, 2019

Predicting performance-related events is an important part of proactive fault management. As a result, many approaches exist for the context of single systems. Surprisingly, despite its potential benefits, multi-system event prediction, i.e., using data from multiple, independent systems, has received less attention. We present ongoing work towards an approach for multi-system event prediction that works with limited data and can predict events for new systems. We present initial results showing the feasibility of our approach. Our preliminary evaluation is based on 20 days of continuous, preprocessed monitoring time series data of 90 independent systems. We created five multi-system machine learning models and compared them to the performance of single-system machine learning models. The results show promising prediction capabilities with accuracies and F1-scores over 90% and false-positive-rates below 10%.