


Volume 20 No 20 (2022)
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ENERGY-EFFICIENT TASK OFFLOADING BASED ON KNN AND NAIVE BAYES WITH ENERGY HARVESTING
Nithiavathy. R, Siva Ilakkiya. P, Subaharini.V
Abstract
Virtual Machines are scheduled to hosts in
Cloud architectures based on their immediate useful
resource use rather than their usual and long-term
usage. In many circumstances, scheduling and site
procedures are computationally expensive and have an
impact on the overall performance of deployed virtual
machines. In this work, a Cloud VM scheduling set of
rules is presented that takes into account previously
established VM useful resource use over time by
analysing beyond VM usage ranges in order to time
table VMs while improving overall performance by
employing the KNN and Naive Bayes category
approach. The Euclidean distance of KNN is measured
after which digital gadget is scheduled at the bodily
gadget. The Cloud control techniques, like VM
placement, have an effect on already deployed
structures so the purpose is to limit such overall
performance degradation. Moreover, overloaded VMs
generally tend to thieve sourcesfrom neighboring VMs,
so the painting maximizes VMs actual CPU usage. The
idea of VM scheduling consistent with useful resource
tracking statistics extracted from beyond useful
resource utilizations (which includes PMs and VMs).
When K-NN & NB classifiers are used instead of
Support Vector Machine (SVM) classifiers, the
physical device's dependability is reduced. The
undertaking accomplished with the aid of using 28
bodily machines while the use of SVM is decreased with
the aid of using 24 bodily gadgets with the aid of using
the use of KNN &NB classifier set of rules additionally
the mistake costs receives reduced with the aid of using
0.025%.
Keywords
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