DocumentCode :
2283920
Title :
Using machine learning for non-intrusive modeling and prediction of software aging
Author :
Andrzejak, Artur ; Silva, Luis
Author_Institution :
Zuse Inst. Berlin, Berlin
fYear :
2008
fDate :
7-11 April 2008
Firstpage :
25
Lastpage :
32
Abstract :
The wide-spread phenomenon of software (running image) aging is known to cause performance degradation, transient failures or even crashes of applications. In this work we describe first a method for monitoring and modeling of performance degradation in SOA applications, particularly application servers. This method works for a large class of the aging processes caused by resource depletion (e.g. memory leaks). It can be deployed non-intrusively in a production environment, under arbitrary service request distributions. Based on this schema we investigate in the second part of the paper how machine learning (classification) algorithms can be used for proactive detection of performance degradation or sudden drops caused by aging. We leverage the predictive power of these algorithms with several techniques to make the measurement-based aging models more adaptive and more robust against transient failures. We evaluate several state-of-the-art classification methods for their accuracy and computational efficiency in this scenario. The studies are performed on a data set generated by a TPC-W benchmark instrumented with a memory leak injector. The results show that the probing method yields accurate aging models with low overhead and the machine learning approach gives statistically significant short-term predictions of degrading application performance. Both approaches can be used directly to fight aging via adaptive software rejuvenation (restart of the application), for operator alerting, or for short-term capacity planning.
Keywords :
learning (artificial intelligence); resource allocation; software architecture; software performance evaluation; storage management; system monitoring; SOA applications; adaptive software rejuvenation; application crash; application servers; capacity planning; machine learning; memory leak injector; memory leaks; nonintrusive modeling; performance degradation; probing method; resource depletion; service request distribution; software aging prediction; system monitoring; transient failure; Aging; Application software; Computer crashes; Condition monitoring; Degradation; Machine learning; Machine learning algorithms; Predictive models; Service oriented architecture; Software performance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network Operations and Management Symposium, 2008. NOMS 2008. IEEE
Conference_Location :
Salvador, Bahia
ISSN :
1542-1201
Print_ISBN :
978-1-4244-2065-0
Electronic_ISBN :
1542-1201
Type :
conf
DOI :
10.1109/NOMS.2008.4575113
Filename :
4575113
Link To Document :
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