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2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN) (2010)
Chicago, IL, USA
June 28, 2010 to July 1, 2010
ISBN: 978-1-4244-7500-1
pp: 507-516
Javier Alonso , Barcelona Supercomputing Center, Dept. of Computer Architecture, Technical University of Catalonia, Spain
Jordi Torres , Barcelona Supercomputing Center, Dept. of Computer Architecture, Technical University of Catalonia, Spain
Josep Ll. Berral , Dept. of Software, Technical University of Catalonia, Spain
Ricard Gavalda , Dept. of Software, Technical University of Catalonia, Spain
ABSTRACT
The growing complexity of software systems is resulting in an increasing number of software faults. According to the literature, software faults are becoming one of the main sources of unplanned system outages, and have an important impact on company benefits and image. For this reason, a lot of techniques (such as clustering, fail-over techniques, or server redundancy) have been proposed to avoid software failures, and yet they still happen. Many software failures are those due to the software aging phenomena. In this work, we present a detailed evaluation of our chosen machine learning prediction algorithm (M5P) in front of dynamic and non-deterministic software aging. We have tested our prediction model on a three-tier web J2EE application achieving acceptable prediction accuracy against complex scenarios with small training data sets. Furthermore, we have found an interesting approach to help to determine the root cause failure: The model generated by machine learning algorithms.
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CITATION

J. Alonso, R. Gavalda, J. L. Berral and J. Torres, "Adaptive on-line software aging prediction based on machine learning," 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN), Chicago, IL, USA, 2010, pp. 507-516.
doi:10.1109/DSN.2010.5544275
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