A comprehensive review of energy efficiency in cloud computing environment

  • Authors

    • Jayasimha S R
    • Usha J
    • Srivani Iyengar S G
    2018-08-24
    https://doi.org/10.14419/ijet.v7i3.29.18805
  • Energy Efficiency, KVM, Power, SSH.

  • Abstract

    High energy consumption in the cloud has become a huge problem in the data center. Energy represents direct significant cost in the operation of the data center. In Information Technology, infrastructure, Internet applications are in more demand. Cloud computing provides IT resources in the form of infrastructure, platform and application by providing services through the Internet Technology. This leads to more energy being consumed as cloud is used to provide IT services from the IT resources to the IT industry and to the Organizations. To analyze power consumed in the data center, applications are deployed in cloud and tested using different workload conditions. Virtualization depicts more energy utilization in the cloud data center. In this paper discussed about the comparison of cloud and cloud computing, cloud type providers, component performance through secured shell. Identified the various levels of energy consumptions in the cloud. the different techniques which is used to reduce the power consumption in the server and workload consolidation using various parameters are considered.

     

     

  • References

    1. [1] Usha, J., Jayasimha, S. R., & Srivani, S. G. (2018). Cognitive Computing and Information Processing (Vol. 801). Springer Singapore. http://doi.org/10.1007/978-981-10-9059-2.

      [2] Beloglazov, A., Lee, Y. C., & Zomaya, A. (n.d.). A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems (Vol. 82). http://doi.org/10.1016/B978-0-12-385512-1.00003-7.

      [3] Jalali, F., Khodadustan, S., Gray, C., Hinton, K., & Suits, F. (2017). Greening IoT with Fog: A Survey. Proceedings - 2017 IEEE 1st International Conference on Edge Computing, EDGE 2017, 25–31. http://doi.org/10.1109/IEEE.EDGE.2017.13.

      [4] Midha, S., Prof, A., & Tripathi, K. (2017). Cloud Deep Down – SWOT Analysis.

      [5] Singh, S., Swaroop, A., Kumar, A., & Anamika. (2017). A survey on techniques to achive energy efficiency in cloud computing. Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2016, 1281–1285. http:// doi. org/10.1109 /CCAA. 2016. 7813915.

      [6] You, X., Li, Y., Zheng, M., Zhu, C., & Yu, L. (2017). A survey and taxonomy of energy efficiency relevant surveys in cloud-related environments. IEEE Access, 5, 14066–14078. http://doi.org/10.1109/ACCESS.2017.2718001

      [7] Aswal, M. S. (2016). A Comparative Study of Resource Allocation Strategies for a Green Cloud, (October), 621–625.

      [8] Dayarathna, M., Wen, Y., & Fan, R. (2016). Data Center Energy Consumption Modeling: A Survey. IEEE Communications Surveys & Tutorials, 18(1), 732–794. http:// doi.org/ 10.1109/COMST .2015. 2481183.

      [9] Kachris, C., & Soudris, D. (2016). A survey on reconfigurable accelerators for cloud computing. FPL 2016 - 26th International Conference on Field-Programmable Logic and Applications. http://doi.org/10.1109/FPL.2016.7577381.

      [10] Kaur, S., & Bawa, S. (2016). A review on energy aware VM placement and consolidation techniques. Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016, 2016. http://doi.org/10.1109/INVENTIVE.2016.7830219.

      [11] Erol-kantarci, M., & Mouftah, H. T. (2015). Energy-Efficient Information and Communication Infrastructures in the Smart Grid : A Survey on Interactions and Open Issues, 17(1), 179–197.

      [12] Sharma, S. K., Gupta, P. K., & Malekian, R. (2015). Energy efficient software development life cycle - An approach towards smart computing. 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), 1–5. http:// doi.org /10.1109/ CGVIS.2015. 7449881.

      [13] Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.-M., & Vasilakos, A. V. (2014). Cloud Computing. ACM Computing Surveys, 47(2), 1–36. http://doi.org/10.1145/2656204.

      [14] Yin, S., Xiao, Z., Li, K., Huang, J., Ruan, X., Zhu, X., & Qin, X. (2016). RESS : A Reliable Energy-Efficient Storage System, 1193–1198. http://doi.org/10.1109/ICPADS.2016.156.

      [15] Eichhorn, F., Dargie, W., Möbius, C., & Rybina, K. (2016). HAECubie : A Highly Adaptive and Energy-Efficient Computing Demonstrator, (in 2011).

      [16] Quintiliani, A., Chinnici, M., Chiara, D. De, Division, E.-I. C. T., & Anguillarese, C. R. C. V. (2016). Understanding “workload-related†metrics for energy efficiency in Data Center, 830–837.

      [17] Al-qawasmeh, A. M., Pasricha, S., Maciejewski, A. A., & Siegel, H. J. (2015). Power and Thermal-Aware Workload Allocation in Heterogeneous Data Centers, 64(2), 477–491.

      [18] Yin, S., Li, X., Li, K., & Chester, W. (2015). REED : A Reliable Energy-Efficient RAID. http://doi.org/10.1109/ICPP.2015.74.

      [19] Chisca, D. S., Casti, I., & Barry, M. (2015). On Energy- and Cooling-Aware Data Centre Workload Management. http:// doi. org/ 10.1109 /CCGrid. 2015.141.

      [20] Arroba, P., & Moya, J. M. (2015). DVFS-Aware Consolidation for Energy-Efficient Clouds, 3–4. http://doi.org/10.1109/PACT.2015.59.

      [21] Deguchi, T., Taniguchi, Y., Hasegawa, G., & Nakamura, Y. (2014). Impact of workload assignment on power consumption in software-defined data center infrastructure, 420–425.

  • Downloads

  • How to Cite

    S R, J., J, U., & Iyengar S G, S. (2018). A comprehensive review of energy efficiency in cloud computing environment. International Journal of Engineering & Technology, 7(3.29), 249-252. https://doi.org/10.14419/ijet.v7i3.29.18805

    Received date: 2018-09-02

    Accepted date: 2018-09-02

    Published date: 2018-08-24