EeadSelfCloud: Energy Efficient Adaptive Depth Self Cloud Mechanism for VM Migration in Data Centers
Sebagenzi Jason1, Suchithra R2.
1Sebagenzi Jason*, Research Scholar, Department of Computer Science, Jain University
2Suchithra R., PhD, Department MSc IT, Jain University.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2236-2244 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8732078919/2019©BEIESP | DOI: 10.35940/ijitee.I8732.0881019
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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, with its great potential in low cost and demanding services, is a good computing platform. Modern data centers for cloud computing are facing the difficulty of consistently increasing complexity because of the expanding quantity of clients and their enlarging resource demands. A great deal of efforts are currently focused on giving the cloud framework with autonomic behavior , so it can take decision about virtual machine (VM) management over the datacenter without intervention of human beings. Most of the self-organizing solutions results in eager migration, which attempts to diminish the amount of working servers virtual machines. These self-organizing resolution produce needless migration due to unpredictable workload. So also it consume huge amounts of electrical energy during unnecessary migration process. To overcome this issue, this project develop one novel VM migration scheme called eeadSelfCloud. The proposed schema is used to change the virtual machine in a cloud center that requires a lot of factors, such as basic requirements for resources during virtual machine setup, dynamic resource allocation, top software loading, software execution, and power saving at the Data Center. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are taken as constraint for measuring the proposed approach. The analysis report depicted that the proposed approach performs best than the other existing approaches.
Keywords: Cloud Computing; Data centre; VM Placement; VM Migration; Energy Efficient Self Organization Cloud
Scope of the Article: Data Analytics