The identification of significant changes in systemresource behaviors is mandatory for an efficient managementof data centers. As the dimension of modern data centersincreases, the evaluation of state change detections throughtraditional algorithms becomes computationally intractable.We propose a novel approach that characterizes the statisticalproperties of the resource measures coming from systemmonitors, classifies them, and signals a change only whenthere is modification of the resource classification. This methoddiminishes the computational complexity and reaches the samedetection accuracy of traditional approaches as demonstratedby several results obtained in real enterprise data centers.
Detecting behavioral variations in system resources of large data centers / Casolari, Sara; Colajanni, Michele; Tosi, Stefania. - STAMPA. - (2011), pp. 371-378. (Intervento presentato al convegno 11th IEEE International Conference on Computer and Information Technology, CIT 2011 and 11th IEEE International Conference on Scalable Computing and Communications, SCALCOM 2011 tenutosi a Pafos, cyp nel 2011-August) [10.1109/CIT.2011.22].
Detecting behavioral variations in system resources of large data centers
CASOLARI, Sara;COLAJANNI, Michele;TOSI, STEFANIA
2011
Abstract
The identification of significant changes in systemresource behaviors is mandatory for an efficient managementof data centers. As the dimension of modern data centersincreases, the evaluation of state change detections throughtraditional algorithms becomes computationally intractable.We propose a novel approach that characterizes the statisticalproperties of the resource measures coming from systemmonitors, classifies them, and signals a change only whenthere is modification of the resource classification. This methoddiminishes the computational complexity and reaches the samedetection accuracy of traditional approaches as demonstratedby several results obtained in real enterprise data centers.
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