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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dabbagh, M., Hamdaoui, B., Guizani, M., & Rayes, A.: Energy-efficient resource allocation and provisioning framework for cloud data centers (2015)
Cisco global cloud index: frocast and methodology. Cicso Inc., White Paper (2016)
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)
Peer1 hosting site puts a survey on “Visualized: ring around the world of data center power usage”. www.engadget.com
Kaplan, J., Forrest, W., Kindler, N.: Revolutionizing data center energy efficiency, Technical report McKinsey & Company (2010)
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)
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)
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
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)
Jayasimha, S.R., Usha, J., Srivani, S.G.: Analysis of power consumption under different workload conditions in the data center, (ICECS). IEEE (2016)
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)
Jayasimha, S.R., Hamsa, K.: A survey on efficient broadcast, geocasting and scalability recital in MANET’S, Icon RFW-2014 (2014)
Computing, S.: An Energy Efficient VM Allocation Approach for Data Centers. http://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.62
Giacobbe, M., Celesti, A., Fazio, M., Villari, M., Puliafito, A.: An approach to reduce energy costs through virtual machine migrations in cloud federation (2015)
Goyal, A.: Bio inspired approach for load balancing to reduce energy consumption in cloud data center (2015)
Tripathi, A.: Storage cost efficient approach for cloud storage and intermediate data sets in clouds (2015)
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
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)
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
Deguchi, T., Taniguchi, Y., Hasegawa, G., Nakamura, Y.: Impact of workload assignment on power consumption in software-defined data center infrastructure (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-10-9059-2_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9058-5
Online ISBN: 978-981-10-9059-2
eBook Packages: Computer ScienceComputer Science (R0)