Towards Identifying the Best Variables for Failure Prediction Using Injection of Realistic Software Faults

Predicting failures at runtime is one of the most promising techniques to increase the availability of computer systems. However, failure prediction algorithms are still far from providing satisfactory results. In particular, the identification of the variables that show symptoms of incoming failures is a difficult problem. In this paper we propose an approach for identifying the most adequate variables for failure prediction. Realistic software faults are injected to accelerate the occurrence of system failures and thus generate a large amount of failure related data that is used to select, among hundreds of system variables, a small set that exhibits a clear correlation with failures. The proposed approach was experimentally evaluated using two configurations based on Windows XP. Results show that the proposed approach is quite effective and easy to use and that the injection of software faults is a powerful tool for improving the state of the art on failure prediction.