Network-Conscious VM Placement for Energy Efficiency in Green Data Centres through Dynamic VM Consolidation
A.V. Sajitha1, A.C. Subhajini2
1A.V. Sajitha, Research Scholar, Department of Computer Applications, Noorul Islam Centre for Higher Education, Noorul Islam University, Kumaracoil (Tamil Nadu), India.
2A.C. Subhajini, Assistant Professor, Department of Computer Applications, Noorul Islam Centre for Higher Education, Noorul Islam University, Kumaracoil (Tamil Nadu), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 18 February 2019 | Manuscript Published on 04 March 2019 | PP: 100-106 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2013017519/19©BEIESP
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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: In the present scenario, cloud computing environment grants all the resources in scalable manner to every users in pay-per-use processing model over the Internet through various data centers. An energy consumption of these resources have to be addressed in many issues in the cloud. A key strategy of virtual machine (VM) management is a live VM migration in data center networks. One of the significant problems of cloud provider is the energy cost. VM migration and placement has been shown as an efficient approach for energy saving. In this paper, we are proposing an algorithm, Modified Energy Conscious Greeny Cloud Dynamic Algorithm (MECGCD), goes for preventing unnecessary traffics in a datacenter network, and excessive energy consumption (EC) started from wrong routing management and improper VM allocation. In this paper, we observe at the issue of how to choose the host for VM placement and to migrate VMs from abnormal loaded hosts such as under loaded or over loaded to another and switching off the idle host machine into sleep mode. VM placement be determined the host machines by shortest distance, minimum EC and maximum bandwidth usage in the cloud environment. The evaluation of experiments confirmed that the proposed algorithm minimizes EC and network traffic in a cloud data center in a quotable manner than other existing algorithms.
Keywords: Cloud Computing, Haversine, Data Center, Live VM Migration, Energy Consumption.
Scope of the Article: Data Mining