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OPTIMIZATION OF WORK LOAD USING MAP REDUCE FRAMEWORK: Review Study
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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
Title: OPTIMIZATION OF WORK LOAD USING MAP REDUCE FRAMEWORK: Review Study
Description:
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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