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.
The 16th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2010), February 2010
DOI
Cited by
Year 2012 : 1 citations
Atef S Mohamed, "Software Architecture-Based Failure Prediction", PhD Thesis, Queen's University, Kingston, Ontario, Canada, September 2012.
Year 2011 : 4 citations
Roberto Natella, “Achieving Representative Faultloads in Software Fault Injection”, PhD Thesis, Universita' Degli Studi di Napoli Federico II, Italy, November 2011.
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2. Antonio Bovenzi, Francesco Brancati, Stefano Russo, Andrea Bondavalli, "A Statistical Anomaly-Based Algorithm for On-line Fault Detection in Complex Software Critical Systems", 30th International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2011, Naples, Italy, September 2011.
3. Antonio Bovenzi, Domenico Cotroneo, Roberto Pietrantuono, Gabriella Carrozza, “Error detection framework for complex software systems”, 13th European Workshop on Dependable Computin, EWDC '11, Pisa, Italy, May 11-12, 2011.
