On the Performance of MapReduce: A Stochastic Approach Conference Paper

abstract

• 2014 IEEE. MapReduce is a highly acclaimed programming paradigm for large-scale information processing. However, there is no accurate model in the literature that can precisely forecast its run-time and resource usage for a given workload. In this paper, we derive analytic models for shared-memory MapReduce computations, in which the run-time and disk I/O are expressed as functions of the workload properties, hardware configuration, and algorithms used. We then compare these models against trace-driven simulations using our high-performance MapReduce implementation.

name of conference

• 2014 IEEE International Conference on Big Data (Big Data)

authors

• Loguinov, Dmitri

published proceedings

• 2014 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)

author list (cited authors)

• Ahmed, S. T., & Loguinov, D.

citation count

• 5

complete list of authors

• Ahmed, Sarker Tanzir||Loguinov, Dmitri

editor list (cited editors)

• Lin, J. J., Pei, J., Hu, X., Chang, W. o., Nambiar, R., Aggarwal, C. C., ... Pyne, S.

publication date

• October 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2639-1589ISSN  Journal

keywords

• Big Data
• Disk I/o
• External Sort
• Mapreduce

Digital Object Identifier (DOI)

• 10.1109/bigdata.2014.7004212

International Standard Book Number (ISBN) 13

• 9781479956654

start page

• 49

end page

• 54

URL

• https://ieeexplore.ieee.org/xpl/conhome/6973861/proceeding