Failure Prediction for Availability Enhancement

tele-TASK-Aufzeichnung des Vortrags

The availability of software and hardware systems can be increased by preventive measures which are triggered by failure prediction mechanisms. We present and evaluate two techniques which model and predict the occurrence of failures as a function of discrete and continuous measurements of system variables. We employ two modelling approaches: a Markov chain model and a function approximation technique utilising universal basis functions (UBF). The presented modelling methods are data driven rather than analytical and can handle large amounts of variables and data. They offer the potential to capture the underlying dynamics of highly-dimensional and noisy systems.

Both modelling techniques have been applied to real data of a commercial telecommunication platform. The data includes event-based log files and measured system states. Results are presented in terms of precision, recall, F-Measure and cumulative cost. We compare our results to standard techniques such as linear ARMA models. Our findings suggest significantly improved forecasting performance compared to alternative approaches. By using the presented modelling techniques the system availability may be improved by an order of magnitude. 

Sprache:    Englisch
Gastgeber: Prof. Dr. Christoph Meinel
Ort:            HS 1
Datum:       Donnerstag, 09. November 2006
Zeit:           16:00