Research Article
Proactive Software Rejuvenation Based on Machine Learning Techniques
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@INPROCEEDINGS{10.1007/978-3-642-12636-9_13, author={Dimitar Simeonov and D. Avresky}, title={Proactive Software Rejuvenation Based on Machine Learning Techniques}, proceedings={Cloud Computing. First International Conference, CloudComp 2009 Munich, Germany, October 19--21, 2009 Revised Selected Papers}, proceedings_a={CLOUDCOMP}, year={2012}, month={5}, keywords={proactive rejuvenation virtualisation machine learning techniques feature selection sparsity software aging (memory leaks) validation}, doi={10.1007/978-3-642-12636-9_13} }
- Dimitar Simeonov
D. Avresky
Year: 2012
Proactive Software Rejuvenation Based on Machine Learning Techniques
CLOUDCOMP
Springer
DOI: 10.1007/978-3-642-12636-9_13
Abstract
This work presents a framework for detecting anomalies in servers leading to crash such as memory leaks in aging systems and proactively rejuvenating them.
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