Schörgenhumer, AndreasKahlhofer, MarioChalupar, PeterMössenböck, HanspeterGrünbacher, PaulKelter, Udo2023-02-272023-02-272019https://dl.gi.de/handle/20.500.12116/40467The prediction of failures and other mission-critical events plays an important role in operating today’s software systems and has drawn the attention of many researchers. Event prediction is particularly challenging if multiple systems are involved. In this paper, we thus present an event prediction model which utilizes time series monitoring data from multiple software systems to predict performance events. Our approach incorporates a comprehensive, multi-system data preprocessing framework for creating various feature vector sets, which we then use to train a random forest classifier to evaluate our multi-system event prediction. Our preliminary evaluation based on data from monitoring 250 systems over a period of 20 days shows promising results.enpredictionfailuresprediction modeltime seriesperformanceText/Conference Paper0720-8928