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A genetic algorithm-based job scheduling model for big data analytics.

Qinghua Lu1, Shanshan Li1, Weishan Zhang1

  • 1College of Computer and Communication Engineering, China University of Petroleum, Qingdao, China.

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|July 19, 2016
PubMed
Summary
This summary is machine-generated.

We developed a genetic algorithm-based job scheduling model to enhance big data analytics efficiency. This approach optimizes continuous MapReduce jobs, reducing energy consumption and improving performance in Hadoop environments.

Keywords:
Big dataGenetic algorithmHadoopJob schedulingMapReduce

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Area of Science:

  • Computer Science
  • Data Science
  • Algorithm Optimization

Background:

  • Big data analytics (BDA) applications process vast datasets using scalable infrastructure.
  • Hadoop's MapReduce framework is a mature solution for BDA, but job scheduling is often sequential and inefficient.
  • Continuous BDA jobs consume high energy and lack optimized execution strategies.

Purpose of the Study:

  • To propose a novel genetic algorithm-based job scheduling model for big data analytics applications.
  • To improve the efficiency and reduce energy consumption of continuous MapReduce jobs.
  • To enhance overall big data processing performance.

Main Methods:

  • Developed a genetic algorithm-based job scheduling model.
  • Integrated an estimation module to predict cluster performance for analytics jobs.
  • Leveraged the Hadoop MapReduce framework for implementation and evaluation.

Main Results:

  • The proposed model demonstrates improved efficiency for big data analytics jobs.
  • The genetic algorithm approach effectively schedules continuous and interdependent jobs.
  • The estimation module accurately predicts cluster performance.

Conclusions:

  • The genetic algorithm-based job scheduling model is a feasible and accurate solution for optimizing BDA applications.
  • This approach offers significant improvements in efficiency and energy consumption.
  • The model provides a valuable contribution to the field of big data processing and job scheduling.