EEF-OCS: Energy Efficient Framework based on Hybrid Learning for Optimal Cloud Selection.

Authors

  • Om Prakash, Muzaffar Azim, S. M. K. Quadri

Keywords:

Cloud computing, energy efficient, Grid Computing, QWS, machine learning

Abstract

Nowadays, choosing a reliable cloud provider has grown to be quite difficult due to the exponential growth of cloud services. A thorough evaluation of cloud services from numerous angles necessitates an accurate decision-making process. Further study is required to provide more authentic decision making outcomes because of the enormous complexity and limits of current methodologies, which undermine the credibility of the energy efficient cloud selection process.  This work aims to improve Hybrid machine learning (ML)-based Energy Efficient Framework. Methods: In this paper, we present a machine learning-based method for predicting Energy Efficient Cloud Selection (EEF-OCS), in which we use our proposed model to analyse various risk factors and predict Energy Efficient Cloud Selection, and we compare this method to other ML approaches like Logistic Regression, KNN, Decision Tree and MLP. Results: Among ML approaches, our suggested model EEF-OCS has produced the best prediction. We were able to get an accuracy of 91.78%, a precision of 92.00%, a recall of 91.78%, and f1 score of 91.71%.

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Author Biography

Om Prakash, Muzaffar Azim, S. M. K. Quadri

1Om Prakash, 2Muzaffar Azim, 3S.M.K. Quadri

1opkushwaha2013@gmail.com, Research Scholar, FTK-Centre for IT, Jamia Millia Islamia,New Delhi, India

2mazim@jmi.ac.in, System Analyst, FTK-Centre for IT, Jamia Millia Islamia, New Delhi, India

3quadrismk@jmi.ac.in, Professor, Department of Computer Science, Jamia Millia Islamia, New Delhi, India

 

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Optimal cloud selection flow chart

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Published

16.04.2023

How to Cite

Om Prakash, Muzaffar Azim, S. M. K. Quadri. (2023). EEF-OCS: Energy Efficient Framework based on Hybrid Learning for Optimal Cloud Selection. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 103 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2756