Advanced Failure Prediction in Complex Software Systems

Abstract The availability of software systems can be increased by preventive measures which are triggered by failure prediction mechanisms. In this paper we present and evaluate two non-parametric 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: an extended 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. Both modelling techniques have been applied to real data of a commercial telecommunication platform. The data includes event-based log files and time continuously 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 software availability may be improved by an order of magnitude. Keywords failure prediction, preventive maintenance, non-parametric data-based modelling, stochastic modelling, universal basis functions (UBF), discrete time Markov chai