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An Energy Efficient and QoS Achieved Through MapReduce in Cloud Environment

  • Sandeep RaiEmail author
  • Aishwarya Namdev
  • Praneet Saurabh
  • Rajesh Boghey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)

Abstract

This paper presents an energy-efficient and quality of service achieved through MapReduce in cloud environment. Although decrease in operating costs residue to be a key desire for movement to Cloud environments, Power consumption is a big thing for data centers and cloud service providers. Many big data applications execute on Hadoop MapReduce framework for handling large workloads. Cloud computing, Virtual machine allocation policy is an important aspect while dealing with multiple component architectures. Data allocation, privacy allocation, resource optimization are challenging task in any architecture. Traditional algorithm faces either a long computation time or high cost over the computation. In this approach which is Hadoop driven matrix based architecture is used for data processing. A hybrid combination of both the approach makes user able to process data effectively with time and cost. The approach is performed on Ubuntu 16.04, HDFS and Java API and maximum power consumption of 112 W as a result.

Keywords

Power consumption Quality of service Twitter analysis VM allocation and VM scheduling 

References

  1. 1.
    Rathi, R., Lohiya, S.: Big data and hadoop. Int. J. Adv. Res. Comput. Sci. Technol. (IJARCST 2014) 2, 214–217 (2014)Google Scholar
  2. 2.
    Patokar, A.A., Patil, V.M.: Efficient analysis of big data by using Hadoop in cloud computing by map reducing. In: National Conference on Innovative Trends in Science and Engineering (NC-ITSE), vol. 4(7), pp. 378–381 (2016)Google Scholar
  3. 3.
    Osman, R., Pérez, J. F., Casale, G.: Quantifying the impact of replication on the quality-of-service in cloud databases. In: 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS), Vienna, pp. 286–297 (2017)Google Scholar
  4. 4.
    Energy star enterprise storage specification. United States Environmental Protection Agency, http://www.energystar.gov/index.cfm?c=new specs. Enterprise storage, under development, Apr. 2009
  5. 5.
    Green Grid data center efficiency metrics: PUE and DCIE. White Paper, The Green Grid, December (2008)Google Scholar
  6. 6.
    Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: ISCA ’07: Proceedings of the 34th Annual International Symposium on Computer Architecture, pp. 13–23. ACM, New York, USA (2007)Google Scholar
  7. 7.
    Su, C.-S., Yen, T.-C.: An SDN based cloud computing architecture and its mathematical model. Inf. Sci. Electron. Electr. Eng. (ISEEE), 1728–1731 (2014)Google Scholar
  8. 8.
    Dhavapriya, M., Yasodha, N.: Big data analytics: challenges and solutions using Hadoop, MapReduce and big table. Int. J. Comput. Sci. Trends Technol. (IJCST) 4(1), 5–14 (2016)Google Scholar
  9. 9.
    Ghemawat, S., Dean, J.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sandeep Rai
    • 1
    Email author
  • Aishwarya Namdev
    • 1
  • Praneet Saurabh
    • 2
  • Rajesh Boghey
    • 1
  1. 1.Department of Computer Science & EngineeringTechnocrats Institute of Technology (Excellence)BhopalIndia
  2. 2.Department of Computer Science & EngineeringTITBhopalIndia

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