IFF-DCN : an intelligent flow forecast technique for distributed centre networks in cloud data centers

  • Authors

    • Ms. S. Brindha Nehru Group of Institutions
    • N. K. Sakthivel
    • S. Subasree
    2019-05-27
    https://doi.org/10.14419/ijet.v7i4.14620
  • Bcube Connected Crossbars (BCCC), Data Centre Network (DCN), Energy Efficiency, Elastic Multi-Controller (E3MC), Software De-Fined Networking (SDN), ARIMA.

  • Abstract

    It is a challenging task to propose an efficient Cost-Effective Network Topology to provide consistent 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 considered 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 earlier proposed an Energy Efficient and Elastic Multi-Controller (E3MC) which is performing better jointly with BCube Connected Crossbars (BCCC). The results encouraged to achieve high energy efficiency. However, to improve the performances of Cloud Setups in term of demanded high QoS, the Cloud Data Centers needed an efficient SLA to respond Users’ demands by consuming less amount of Energy. This is considered as one of the major challenges which didn’t address by our previous Model E3MC. 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. The proposed IFF-DCN is implemented and Simulated. The performances of the proposed Model were studied thoroughly. From the simulation results, it was noticed that the proposed model achieves higher performances as compared with the existing E3MC-BCCC in terms of Energy Efficiency, Path length, Server Resource Utilization, Throughput, Link Failure Rate and Server Failure Rate.

     

     

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  • How to Cite

    S. Brindha, M., K. Sakthivel, N., & Subasree, S. (2019). IFF-DCN : an intelligent flow forecast technique for distributed centre networks in cloud data centers. International Journal of Engineering & Technology, 7(4), 7040-7046. https://doi.org/10.14419/ijet.v7i4.14620

    Received date: 2018-06-23

    Accepted date: 2018-07-03

    Published date: 2019-05-27