Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers
Narander Kumar1, Surendra Kumar2
1Narander Kumar*, Department of Computer Science, B.B.A University (A Central University), Lucknow, India.
2Surendra Kumar, Department of Computer Science, B.B.A University (A Central University), Lucknow, India.
Manuscript received on July 08, 2019. | Revised Manuscript received on August 22, 2019. | Manuscript published on August 30, 2019. | PP: 4932-4937 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9241088619/2019©BEIESP | DOI: 10.35940/ijeat.F9241.088619
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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: Including the expanding fame of the cloud model and quick multiplication of cloud frameworks there are expanding concerns about energy utilization and subsequent effect of cloud as a supporter of worldwide CO2 discharges. Until now, little is thought about how to fuse energy utilization and CO2 worries into cloud application. Energy consumption has become an important cost factor for computing resources. In this research article, we proposed an algorithm to VM Allocation and VM migrations in the context of power utilisation in the data centers. This mechanism is to minimize the energy utilization in the cloud computing environment. We validate our results with the help of prediction based faster energy efficient VMs approach and modified Best Fit approach which shows the faster assignments and increase the performance when consumption of the energy is optimised. As well as we also simulate our results in the cloudsim in the multiple numbers of host and virtual machine to reduce the energy consumptions.
Keywords: VM Allocation, VM Migration, Energy Consumption, Prediction Based Algorithm, Modified Best Fit Approach, Cloudsim.