Proactive Software Rejuvenation Based on Machine Learning Techniques

Dimitar Simeonov; D. Avresky

Cloud Computing. First International Conference, CloudComp 2009 Munich, Germany, October 19–21, 2009 Revised Selected Papers

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

Dimitar Simeonov¹^,*, D. Avresky¹^,*

1: IRIANC

*Contact email: simeonov.dimitar@gmail.com, autonomic@irianc.com

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.

Keywords: proactive rejuvenation virtualisation machine learning techniques feature selection sparsity software aging (memory leaks) validation

Published: 2012-05-28

: http://dx.doi.org/10.1007/978-3-642-12636-9_13