Benchmarking and performance modelling of MapReduce communication pattern

Sheriffo Ceesay, Adam David Barker*, Yuhui Lin*

*Corresponding author for this work

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

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

Understanding and predicting the performance of big data applications running in the cloud or on-premises could help minimise the overall cost of operations and provide opportunities in efforts to identify performance bottlenecks. The complexity of the low-level internals of big data frameworks and the ubiquity of application and workload configuration parameters makes it challenging and expensive to come up with comprehensive performance modelling solutions. In this paper, instead of focusing on a wide range of configurable parameters, we studied the low-level internals of the MapReduce communication pattern and used a minimal set of performance drivers to develop a set of phase level parametric models for approximating the execution time of a given application on a given cluster. Models can be used to infer the performance of unseen applications and approximate their performance when an arbitrary dataset is used as input. Our approach is validated by running empirical experiments in two setups. On average, the error rate in both setups is ±10% from the measured values.
Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2019)
EditorsJinjun Chen, Laurence T. Yang
PublisherIEEE Computer Society
Pages127-134
Number of pages8
ISBN (Electronic)9781728150116
ISBN (Print)9781728150123
DOIs
Publication statusPublished - 27 Jan 2020
Event2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) - Novotel Sydney Central, Sydney, Australia
Duration: 11 Dec 201913 Feb 2020
Conference number: 11
http://2019.cloudcom.org/

Publication series

NameIEEE International Conference on Cloud Computing Technology and Science
PublisherIEEE
ISSN (Electronic)2330-2186

Conference

Conference2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
Abbreviated titleCloudCom
Country/TerritoryAustralia
CitySydney
Period11/12/1913/02/20
Internet address

Keywords

  • Communication Pattern
  • Big Data
  • MapReduce
  • Modelling

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