Software metrics for green parallel computing of big data systems

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

1 Citation (Scopus)

Abstract

Big Data is typically organized around a distributed file system on top of which the parallel algorithms can be executed for realizing the Big Data analytics. In general, the parallel algorithms can be mapped in different alternative ways to the computing platform. Hereby each alternative will perform differently with respect to the environmentally relevant parameters such as energy and power consumption. Existing studies on deployment of parallel computing algorithms have mainly focused on addressing general computing metrics such as speedup with respect to serial computing and efficiency of the use of the computing nodes. In this paper, we report on the elicitation of green metrics for big data systems that are required when analyzing deployment alternatives. To this end we use the existing systematic literature reviews and identify, and discuss the important green computing metrics for big data systems.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages345-348
ISBN (Print)9781509026227
DOIs
Publication statusPublished - 5 Oct 2016
Event5th IEEE International Congress on Big Data, BigData Congress 2016 - San Francisco, United States
Duration: 27 Jun 20162 Jul 2016

Conference

Conference5th IEEE International Congress on Big Data, BigData Congress 2016
CountryUnited States
CitySan Francisco
Period27/06/162/07/16

Keywords

  • Big data
  • Green computing
  • Metrics
  • Parallel computing component

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  • Cite this

    Gurbuz, H. G., & Tekinerdogan, B. (2016). Software metrics for green parallel computing of big data systems. In Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016 (pp. 345-348). [7584960] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataCongress.2016.54