Research on Mapreduce Framework Based on Serverless Computing

Shuheng Zhou

2025

Abstract

As an important framework for handling big data, MapReduce has the ability to simplify complex computing tasks by means of parallel computing. However, the conventional MapReduce framework suffers from issues like high resource consumption and limited real-time capabilities. Fortunately, serverless computing has the advantage of automatic expansion and shrinking, fast power-up, low latency, and high efficiency of resource utilization, providing methods to improve such drawbacks. Therefore, this study tries to improve the behavior of the MapReduce framework through serverless computing. This study successfully implements the “word count” function of the adjusted framework through the ALiYun serverless computing platform and compares its performance with that of conventional MapReduce. In accordance with the experimental results, despite occupying more space, serverless computing evidently cuts function execution time, effectively improving the efficiency of the framework. Specifically, the adjusted framework works 19. 42 times faster than conventional MapReduce. Research has shown that improving the MapReduce framework through serverless computing can significantly improve computing efficiency, especially in terms of processing speed, demonstrating the potential of serverless computing in big data processing.

Paper Citation

in Harvard Style

Zhou S. (2025). Research on Mapreduce Framework Based on Serverless Computing. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 133-136. DOI: 10.5220/0013679900004670

in Bibtex Style

@conference{icdse25,
author={Shuheng Zhou},
title={Research on Mapreduce Framework Based on Serverless Computing},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={133-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013679900004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Research on Mapreduce Framework Based on Serverless Computing
SN - 978-989-758-765-8
AU - Zhou S.
PY - 2025
SP - 133
EP - 136
DO - 10.5220/0013679900004670
PB - SciTePress