Dynamic Virtual Machine Migration using Ensemble Regressor based controller to reduce the Green Energy wastage and Optimal Utilization of Resources towards Green Computing

Agrawal, Lalit Rakesh (2022) Dynamic Virtual Machine Migration using Ensemble Regressor based controller to reduce the Green Energy wastage and Optimal Utilization of Resources towards Green Computing. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the current era, the cloud has emerged as the main source for providing services related to storage, computation, and networking solutions. To provide hassle-free services to the users, various data centers in different regions of the world are established by cloud providers. These data center consumes a high amount of electricity and emits a high amount of carbon. Due to Environmental issues, Govt strict policies, and saving energy costs, many cloud providers are switching toward the green computing solution. Green Computing Solution motivates to utilize the renewable source of energy for power generation. With the dependency on the renewable source of energy such as Solar, Wind, and thermal energy, there are certain challenges associated such as Dynamic load generation by the users, Weather unpredictability, etc. To solve such challenges, in this research, we are proposing an AI-based controller for forecasting the energy to efficiently distribute the load and utilization of resources. After a certain set of experiments with multiple machine learning algorithms such as Linear regressor, Support vector regressor, Bayesian regression, and Ensemble regressor over the simulated cloud computing environment. We have achieved the optimal outcomes from the Ensemble regressor method, which helps to reduce the green energy wastage and provides accurate prediction to perform Virtual machine migration among the different sets of clusters.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Tamara Malone
Date Deposited:	08 Dec 2022 11:42
Last Modified:	08 Mar 2023 14:28
URI:	https://norma.ncirl.ie/id/eprint/5981

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