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The impact of resources adjustment appears on the collected data, which makes the SVM classifier unsuitable in the light of the new adjustment in the VMs resources.
The detection algorithm applies the filter of resources adjustment effect (obtained from executing Algorithm 1) on the testing dataset before passing it to the SVM classifier.
The traditional-SVM uses the SVM classifier directly, without applying any filtering strategy that can cope with the effect of the resources adjustment on the detection performance.
This makes the SVM classifier be trained not only on the original infrastructure but also on the new infrastructure after resources adjustment.
Fig. 11 Resources adjustment.
Then, for each possible resources adjustment adj, the algorithm applies resources adjustment of value adj, to see the effect of adj on j's system metrics.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com