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%.
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