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Automata Approach to Reduce Power Consumption in Smart Grid Cloud Data Center

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Cognitive Computing and Information Processing (CCIP 2017)

Abstract

Today, in the cloud data center reduction in power consumption is one of the most difficult tasks in the Information and Communication Technology. In the cloud, data center includes reservoirs which process the power to meet user needs while computing. Presently. Data centers have become increasingly popular by user acceptance. The challenging task is to reduce high energy consumption in data center servers in the cloud computing environment which results in increased cost of maintenance and CO2 emission. In this paper, automata approach has been proposed and then compared with various approaches which optimize the hardware, software components reducing the power utilization in the data center servers using virtualization technology. Dynamic voltage and frequency scaling approach based on the learning automata are used to reach a compromise and decrease the energy consumption in the data center. Power can be saved by shutting down the servers during idle period and consumption is maximum during peak period.

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References

  1. Dabbagh, M., Hamdaoui, B., Guizani, M., & Rayes, A.: Energy-efficient resource allocation and provisioning framework for cloud data centers (2015)

    Google Scholar 

  2. Cisco global cloud index: frocast and methodology. Cicso Inc., White Paper (2016)

    Google Scholar 

  3. Gandhi, A., Balter, H., Das, R., Lefurgy, C.: Optimal power allocation in server farms. In: Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems. ACM, New York (2009)

    Google Scholar 

  4. Peer1 hosting site puts a survey on “Visualized: ring around the world of data center power usage”. www.engadget.com

  5. Kaplan, J., Forrest, W., Kindler, N.: Revolutionizing data center energy efficiency, Technical report McKinsey & Company (2010)

    Google Scholar 

  6. Brooks, D., Martonosi, M.: Dynamic thermal management for high-performance microprocessors. In: The Seventh International Symposium on High-Performance Computer Architecture, Article (CrossRef Link) (2001)

    Google Scholar 

  7. Ye, K., Huang, D., Jiang, X., Chen, H., Wu, S.: Virtual machine based energy-efficient data center architecture for cloud computing: a performance perspective. In: IEEE/ACM International Conference on Green Computing and Communications, on Cyber, Physical and Social Computing, Article (CrossRef Link) (2010)

    Google Scholar 

  8. Jayasimha, S.R., Usha, J., Srivani, S.G.: Minimizing power consumption and improve the quality of service in the data center, 9(November) (2016). http://doi.org/10.17485/ijst/2016/v9i43/104388

  9. Kansal, A., Zhao, F., Liu, J., Kothari, N., Bhattacharya, A.: Virtual machine power metering and provisioning. In: Proceedings of the 1st ACM Symposium on Cloud Computing (2010)

    Google Scholar 

  10. Jayasimha, S.R., Usha, J., Srivani, S.G.: Analysis of power consumption under different workload conditions in the data center, (ICECS). IEEE (2016)

    Google Scholar 

  11. Jayasimha, S.R., Narasimha Prasad, D., Hamsa, K., Sumithra Devi, K.A.: Prevention of data from the data leakage in cloud computing. In: ISRASE, Firse International Conference on Recent Advances in Science and Engineering (ISRASE 2014) (2014)

    Google Scholar 

  12. Jayasimha, S.R., Hamsa, K.: A survey on efficient broadcast, geocasting and scalability recital in MANET’S, Icon RFW-2014 (2014)

    Google Scholar 

  13. Computing, S.: An Energy Efficient VM Allocation Approach for Data Centers. http://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.62

  14. Giacobbe, M., Celesti, A., Fazio, M., Villari, M., Puliafito, A.: An approach to reduce energy costs through virtual machine migrations in cloud federation (2015)

    Google Scholar 

  15. Goyal, A.: Bio inspired approach for load balancing to reduce energy consumption in cloud data center (2015)

    Google Scholar 

  16. Tripathi, A.: Storage cost efficient approach for cloud storage and intermediate data sets in clouds (2015)

    Google Scholar 

  17. Yang, T., Lee, Y.C., Zomaya, A.Y.: Collective energy-efficiency approach to data center networks planning, 7161(c), 1–12 (2015). http://doi.org/10.1109/TCC.2015.2511732

  18. Wang, S., Zhou, A.O., Hsu, C., Member, S., Xiao, X., Yang, F., Member, S.: Provision of data-intensive services through energy- and QoS-aware virtual machine placement in national cloud data centers, 4(2) (2016)

    Google Scholar 

  19. Chisca, D.S., Casti, I., Barry, M.: On energy-and cooling-aware data centre workload management (2015). http://doi.org/10.1109/CCGrid.2015.141

  20. Deguchi, T., Taniguchi, Y., Hasegawa, G., Nakamura, Y.: Impact of workload assignment on power consumption in software-defined data center infrastructure (2014)

    Google Scholar 

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Correspondence to S. R. Jayasimha .

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Usha, J., Jayasimha, S.R., Srivani, S.G. (2018). Automata Approach to Reduce Power Consumption in Smart Grid Cloud Data Center. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_23

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  • DOI: https://doi.org/10.1007/978-981-10-9059-2_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-9058-5

  • Online ISBN: 978-981-10-9059-2

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