Efficient Resources Allocation and Energy Reduction with Virtual Machines for Cloud Computing 
Anand Mehta
Anand Mehta*, Researcher Scholar, Department of Computer Science and Engineering, United States of America.
Manuscript received on August 03, 2019. | Revised Manuscript received on December 31, 2021. | Manuscript published on December 30, 2021. | PP: 52-58 | Volume-11, Issue-2, December 2021 | Retrieval Number: 100.1/ijitee.J99820881019 | DOI: 10.35940/ijitee.J9982.1211221
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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: Cloud computing is an internet provisioned method for sharing the resources on demand by network management, storage, services, applications and the serves that necessitate management optimal effort. VMM (virtual machine migration) plays a major role in enhancing the resource utilization, application isolation, processing nodes, fault tolerance in VMs for enhancing nodes portability and for maximizing the efficiency of physical server. For balancing the clouds with resources for the enhanced performance, varied users are served with application deployment in the cloud environment is considered as the major task. The user can rent or request the resources when it becomes significant. The emphasis of this paper is on different energy VM energy efficient module as per machine learning methods. While allocating the VMs to the host machines, MBFD (Modified Best Fit Decreasing) is considered and the classification of host machine capability such as overloaded, normal loaded and underloaded is executed according to SVM (Support vector machine). SVM is utilized as a classifier for analyzing the MBFD algorithm and for the classification of the host as per the job properties. In this procedure, the numbers of jobs that are not allocated are examined via simulation which is computed by means of time consumption, energy consumption and a total number of migrations.
Keywords: Cloud computing, Virtual machine, Resource allocation, VM allocation, MBFD, SVM, Time consumption, Energy consumption, Total number of migrations evaluation.
Scope of the Article: Cloud Computing