QOS-Aware Task Scheduling in An Edge Environment

In Edge frameworks, Virtual Machines (VM) are used for the asset usage as per the planning by the users. The most common example for this is accessible RAM, disregarding about the long haul usage. The planning and situation processes are computationally of high cost and the influence of execution is conveyed. In this paper, we propose a Edge VM planning calculation that considers previously running VM asset usage over the long run by the dissecting past VM usage levels to plan the current VMs. This is done by advancing the execution in the utilization of K-Means Nearest Neighbor (KNN) and Naive Bayes characterization procedure. The Euclidean distance of KNN is estimated and then the virtual machine is booked on the actual machine. The Edge in the board processes the VM situation & influences the previously sent frameworks so the point is to limit such execution corruption. In addition to this, over-burden of VMs will quite often take the assets from the adjoining VMs, so that the work boosts VMs genuine CPU usage. The outcome of our experimental setup shows the refinement in the conventional instant-based actual machine determination which learns the framework conduct as well as it adjusts over the long run. It also improvises the counts of the actual machines which gets diminished by the utilization of K-NN and NB classifier rather than traditional usage of Support Vector Machine (SVM) classifier.