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MRPack: Multi-Algorithm Execution Using Compute-Intensive Approach in MapReduce.

Muhammad Idris1, Shujaat Hussain1, Muhammad Hameed Siddiqi1

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Summary
This summary is machine-generated.

MapReducePack (MRPack) enables executing multiple algorithms in one Hadoop MapReduce job, significantly boosting Big Data processing efficiency. This innovation reduces execution time by 200% and I/O costs by 50%.

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Area of Science:

  • Computer Science
  • Data Science
  • Distributed Systems

Background:

  • Big Data is generated at exponential rates, posing challenges for processing.
  • Hadoop MapReduce (MR) is a standard framework but executes only one algorithm per job.
  • Existing MR implementations limit computational efficiency for complex Big Data tasks.

Purpose of the Study:

  • To introduce MapReducePack (MRPack), a novel variation of Hadoop MapReduce.
  • To enable the execution of multiple related algorithms within a single MapReduce job.
  • To enhance the compute-intensiveness of MapReduce while preserving its data-intensive nature.

Main Methods:

  • MRPack dynamically manages task assignment and intermediate data for multiple algorithms.
  • It employs multi-key and skew mitigation strategies for efficient intermediate data handling.
  • The system increases computational load on the Hadoop cluster.

Main Results:

  • MRPack demonstrates significant time, I/O, and memory efficiency compared to default MapReduce.
  • Execution time is reduced by up to 200%.
  • I/O costs are decreased by approximately 50%.

Conclusions:

  • MRPack offers a substantial performance improvement for Big Data processing.
  • The approach effectively utilizes cluster computing resources for complex analytical tasks.
  • MRPack represents an efficient advancement in distributed data processing frameworks.