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K-Medoids based Clustering of PlanetLab’s Slice-Centric Data |
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PP: 2373-2384 |
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Author(s) |
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Aun Haider,
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Abstract |
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| This paper investigates the application of widely used K-Medoids based clustering algorithm on data collected through
CoMon facility for the PlanetLab testbed. The averaged values of various metrics in passively collected slice-centric data has been
considered for clustering purposes. Various groups of slices, depicting similar resource usage patterns have been identified in original
data set. These clusters have been represented in reduced dimensional space formed by first two principal components of original data
set. In order to capture variations in pattern of resource usage by various slices at a PlanetLab node, clustering of standard deviations
of various metrics have also been carried out. Further, combining averaged and standard deviation, clustering has also been performed
on index of dispersion computed from the original data set. It has been found that K-medoid based clustering can effectively split
the original data space into various sub-spaces of different resource usage behaviour of slices. Thus, it can lead to better resource
management and control in publicly available testbeds. |
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