Predicting failures of computer systems: a case study for a telecommunication system

The goal of online failure prediction is to forecast imminent failures while the system is running. This paper compares similar events prediction (SEP) with two other well-known techniques for online failure prediction: a straightforward method that is based on a reliability model and dispersion frame technique (DFT). SEP is based on recognition of failure-prone patterns utilizing a semi-Markov chain in combination with clustering. We applied the approaches to real data of a commercial telecommunication system. Results are presented in terms of precision, recall, F-measure and accumulated runtime-cost. The results suggest a significantly improved forecasting performance.