Abstract
Supervisory control is the main means to assure a high level performance and availability of large IT infrastructures. Applied control theory is used in physical and virtualization based clustering, autonomic-, self-healing and cloud computing, but similar problems arise in any distributed environment.
The selection of a compact, but sufficiently characteristic set of control variables is one of the core problems both for design and run-time complexity. Most results in the literature are based on a single algorithm for variable selection, but our measurements indicate that no single algorithm can generate faithful estimates for all the different operational domains.
We propose to use a combination of different model extraction techniques on benchmark-like data logs. The main advantages of this multi-paradigm approach are twofold: it provides good parameter estimators for predictive control in a simple way; and supports the identification of the actual operational domain facilitating context-aware adaptive control, diagnostics and repair.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Paljak, G.J., Kocsis, I., Égel, Z., Tóth, D., Pataricza, A. (2010). Sensor Selection for IT Infrastructure Monitoring. In: Vasilakos, A.V., Beraldi, R., Friedman, R., Mamei, M. (eds) Autonomic Computing and Communications Systems. AUTONOMICS 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11482-3_9
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DOI: https://doi.org/10.1007/978-3-642-11482-3_9
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