• S.BRINDHA
• S Subasree

It is a challenging task to propose an efficient Cost-Effective Network Topology to provide con-sistent performance for large networks that consists of more number of Servers, Routers and Nodes. From its earlier literature survey, we found that a few popular Server-Centric Network Topologies such as BCube, Bidimensional Compound Network (BCN) and FiConn were proposed. These three topologies were studied thoroughly and noticed that all these topologies were are not expandable and also it was noticed that it is needed to spent considerable cost to expand further. This is consid-ered as the major issue and concern to expand or upgrade the Data Centre. It is also observed that, as almost all Industries and Institutes needed powerful Data Centers for their Business demands and growth, the Data Centre Network (DCN) is much researched and improved. This massive usage of DCN, the Energy Consumption is significantly higher side. Thus, to manage the massive power demand, the Software Defined Network (SDN) Model was proposed to control Resources by Turning Off or On in accordance with the traffic demands. The Software Defined Networking (SDN) is the current promising solution for controlling the resources of DCN and still it is noticed that the Power Efficiency is one of the major challenges. To address the above mentioned issues, it is highly needed to propose an efficient model to improve the power efficiency for Data Centre Network (DCN). This Research work focusses on the issues of Energy Saving and maintaining high QoS. To achieve the same, we proposed an efficient Technique called Intelligent Flow Forecast Technique for Distributed Centre Networks(IFF-DCN) that will predict the Resources Utilization and Traffic Demands as well in advance and accordingly resources will be Turn On or Turn Off. To improve its efficiency further, the Virtual Machine (VM) Dynamic Migration Technology (VMDMS) was proposed. The proposed VMDMS is implemented and Simulated. The performances of the proposed Model were studied thoroughly. From the simulation results, it was noticed that the proposed model VMDMS achieves higher performances as compared with the existing IFF-DCN in terms of Energy Efficiency, Server Resource Utilization, and Server/Physical Machine Failure Rate.

Virtual Machine, Physical Machine, BCube Connected Crossbars (BCCC), Data Centre Network (DCN), Energy Efficiency, Software Defined Networking (SDN), ARIMA.

"Virtual Machine Dynamic Migration Strategy based Intelligent Flow Forecast Technique for Cloud Data Centers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.368-376, April-2019, Available :http://www.jetir.org/papers/JETIR1904C60.pdf

"Virtual Machine Dynamic Migration Strategy based Intelligent Flow Forecast Technique for Cloud Data Centers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp368-376, April-2019, Available at : http://www.jetir.org/papers/JETIR1904C60.pdf