Speed-based load balancer for scheduling reduce tasks to process intermediate data of MapReduce applications on cloud computing

Tzu Chi Huang, Kuo Chih Chu, Ce Kuen Shieh, Ming Fong Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

MapReduce is a programming model used to develop applications on cloud computing. However, MapReduce strongly relies on the runtime system to handle issues of task managements in order to get a better performance. While most research works focus on scheduling Map tasks to improve performances, MapReduce is identified by this paper to have the potential for performance improvements through the Speed-based Load Balancer (SLB) for scheduling Reduce tasks. According to observations on experiments of Inverted Index, Radix Sort and Word Count, MapReduce can use SLB to outperform the native scheduler used by Hadoop in the runtime system.

Original languageEnglish
Title of host publicationProceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450337359
DOIs
Publication statusPublished - 2015 Oct 7
EventASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan
Duration: 2015 Oct 72015 Oct 9

Publication series

NameACM International Conference Proceeding Series
Volume07-09-Ocobert-2015

Other

OtherASE BigData and SocialInformatics, ASE BD and SI 2015
CountryTaiwan
CityKaohsiung
Period15-10-0715-10-09

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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    Huang, T. C., Chu, K. C., Shieh, C. K., & Tsai, M. F. (2015). Speed-based load balancer for scheduling reduce tasks to process intermediate data of MapReduce applications on cloud computing. In Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015 [a49] (ACM International Conference Proceeding Series; Vol. 07-09-Ocobert-2015). Association for Computing Machinery. https://doi.org/10.1145/2818869.2818880