Forecasting Cloud Resource Provisioning System using Supervised Machine Learning
Frishta Mirzad1, Muhammad Rukunuddin Ghalib2
1Suganya. G*, Department of Computer Science, Bharathiar University, Coimbatore, India.
2Porkodi R Department of Computer Science, Bharathiar University, Coimbatore, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3591-3596 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8886038620/2020©BEIESP | DOI: 10.35940/ijrte.F8886.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Cloud Computing, Virtualizations, Resource-Provisioning Policies, Machine Learning.
Scope of the Article: Machine Learning.