Real-Time Big Data Stream Processing Using GPU with Spark Over Hadoop Ecosystem

  • M. Mazhar Rathore
  • Hojae Son
  • Awais Ahmad
  • Anand Paul
  • Gwanggil Jeon
Article
Part of the following topical collections:
  1. Special Issue on Programming Models and Algorithms for Data Analysis in HPC Systems

Abstract

In this technological era, every person, authorities, entrepreneurs, businesses, and many things around us are connected to the internet, forming Internet of thing (IoT). This generates a massive amount of diverse data with very high-speed, termed as big data. However, this data is very useful that can be used as an asset for the businesses, organizations, and authorities to predict future in various aspects. However, efficiently processing Big Data while making real-time decisions is a quite challenging task. Some of the tools like Hadoop are used for Big Datasets processing. On the other hand, these tools could not perform well in the case of real-time high-speed stream processing. Therefore, in this paper, we proposed an efficient and real-time Big Data stream processing approach while mapping Hadoop MapReduce equivalent mechanism on graphics processing units (GPUs). We integrated a parallel and distributed environment of Hadoop ecosystem and a real-time streaming processing tool, i.e., Spark with GPU to make the system more powerful in order to handle the overwhelming amount of high-speed streaming. We designed a MapReduce equivalent algorithm for GPUs for a statistical parameter calculation by dividing overall Big Data files into fixed-size blocks. Finally, the system is evaluated while considering the efficiency aspect (processing time and throughput) using (1) large-size city traffic video data captured by static as well as moving vehicles’ cameras while identifying vehicles and (2) large text-based files, like twitter data files, structural data, etc. Results show that the proposed system working with Spark on top and GPUs under the parallel and distributed environment of Hadoop ecosystem is more efficient and real-time as compared to existing standalone CPU-based MapReduce implementation.

Keywords

Big Data Hadoop Spark GPU MapReduce 

Notes

Acknowledgements

This study was supported by the Brain Korea 21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • M. Mazhar Rathore
    • 1
  • Hojae Son
    • 1
  • Awais Ahmad
    • 2
  • Anand Paul
    • 1
  • Gwanggil Jeon
    • 3
  1. 1.School of Computer Science and EngineeringKyungpook National UniversityDaeguKorea
  2. 2.Department of Information and Communication EngineeringYeungnam UniversityGyeongbukKorea
  3. 3.Department of Embedded Systems EngineeringIncheon National UniversityIncheonKorea

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