Open AccessForecasting Cloud Resource Provisioning System using Supervised Machine Learning
Author(s) -
Frishta Mirzad*,
Muhammad Rukunuddin Ghalib
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8886.038620
Subject(s) - cloud computing , computer science , provisioning , machine learning , artificial intelligence , resource (disambiguation) , time series , quality (philosophy) , distributed computing , data mining , operating system , computer network , philosophy , epistemology
One of the biggest challenges cloud computing faces is forecasting correctly the resource use for future demands. Consumption of cloud resources is consistently changing, making it difficult for algorithms to forecast to make precise predictions. Using of the machine learning in cloud computing leads to many benefits. Such as chances of the enhancement in the quality of the service via forecasting future burden of works and responding automatically with dynamic scaling. This motivates the work presented in this paper to predict CPU use of host machines for a single time and multiple times. This paper uses three supervised machine-learning algorithms to classify and predict CPU utilization because of their capability to keep data and predict accurate time series issues. It is tried to forecast CPU usage with better accuracy while comparing to traditional methods.