OPTIMIZATION OF WORK LOAD USING MAP REDUCE FRAMEWORK: Review Study

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Published: 2018-01-30
Authors:
Aasheesh Raizada
Research Scholar, IET, Mangalayatan University
Manoj Rana
Associate Professor, IET, Mangalayatan University Aligarh
Pankaj Kumar Varshney
Associate Professor, Department of Computer Science, IITM Janakpuri
DOI: https://doi.org/10.65301/iitm.2018.9.1.499
Keywords:
Big Data, Map Reduce, HADOOP, HDFS, YARN, Optimization, Workload.

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Volume 9 Issue 1 January-June 2018

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Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Abstract

The term Optimize is “to make perfect”. It’s means choosing the best element from some set of available  alternatives. Within the past few years, organizations in diverse industries have adopted Mapreduce framework  for large-scale data processing. As we know that Mapreduce has developed to new users for important new  workloads have emerged which feature many small, short, and increasingly interactive jobs in addition to the  large, long-running batch jobs. In this paper researchers try to focus on optimization of workload in different  field such as e-commerce, media and data handling. Mapreduce workloads are driven by interactive analysis, and  make heavy use of query like programming frameworks on top of Mapreduce. Mapreduce frameworks can  achieve much higher performance by adapting to the characteristics of their workloads

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