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A Cost-Effective Data Node Management Scheme for Hadoop Clusters in Cloud Environment

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Machine Learning and Metaheuristics Algorithms, and Applications (SoMMA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1203))

Abstract

MapReduce framework in Hadoop is used to analyze the large set of data in a distributed storage system. MapReduce jobs are designate to the task node to perform the map-reduce operation based upon the scheduler. Each node has slots (virtual core) to process a task using the map and reduce operation. Map tasks done separately prior to the Reduce task. The different execution order of jobs and different slot configuration in the clusters affect the CPU performance significantly. In this paper, we have stated effective DataNode assignment techniques for resource allocation in the Hadoop MapReduce job. We performed various operations on Amazon EC2 and physical machine to demonstrate that our proposed technique helps to choose optimized node selection for assignment of DataNodes in the Hadoop cluster. This significantly scales down the cost of the node and increases the job execution performance in the Hadoop cluster.

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Author information

Authors and Affiliations

  1. Information and Communication Engineering, Anna University, Chennai, Tamilnadu, India

    B. S. Vidhyasagar

  2. Department of ECE, Kings Engineering College, Chennai, Tamilnadu, India

    J. Raja Paul Perinbam

  3. Department of CSE, KCG College of Technology, Chennai, Tamilnadu, India

    M. Krishnamurthy

  4. Department of CSE, SRM Institute of Science and Technology, Chennai, Tamilnadu, India

    J. Arunnehru

Authors
  1. B. S. Vidhyasagar
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  2. J. Raja Paul Perinbam
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  3. M. Krishnamurthy
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  4. J. Arunnehru
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Corresponding author

Correspondence to B. S. Vidhyasagar .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management - Kerala (IIITM-K), Trivandrum, India

    Sabu M. Thampi

  2. Simon Fraser University, Burnaby, BC, Canada

    Ljiljana Trajkovic

  3. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  4. Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Wrocław University of Technology, Wrocław, Poland

    Michal Wozniak

  6. Università degli Studi di Firenze, Florence, Italy

    Stefano Berretti

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Vidhyasagar, B.S., Perinbam, J.R.P., Krishnamurthy, M., Arunnehru, J. (2020). A Cost-Effective Data Node Management Scheme for Hadoop Clusters in Cloud Environment. In: Thampi, S., Trajkovic, L., Li, KC., Das, S., Wozniak, M., Berretti, S. (eds) Machine Learning and Metaheuristics Algorithms, and Applications. SoMMA 2019. Communications in Computer and Information Science, vol 1203. Springer, Singapore. https://doi.org/10.1007/978-981-15-4301-2_3

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  • DOI: https://doi.org/10.1007/978-981-15-4301-2_3

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  • Online ISBN: 978-981-15-4301-2

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