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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Sensors and Materials,
Volume 29, Number 11 (2017)

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pp. 1497-1512
S&M1444
https://doi.org/10.18494/SAM.2017.1659
Published: November 24, 2017

Context-Aware Task Assignment for MapReduce in Heterogeneous Clouds [PDF]

Wei-Tsung Su, Wei-Fan Pan, and Chao-Chun Chen

(Received April 06, 2017; Accepted August 4, 2017)

Keywords: heterogeneous cloud, MapReduce, task assignment, resource-proportional

The MapReduce programming model is designed to process large data sets based on parallel computing among multiple computer nodes (CNs). Because the data size is considerably increased (data are collected from sensors in most cases), the optimization problem of task assignment becomes important to improve the performance of MapReduce. Unfortunately, this problem is even more difficult in heterogeneous clouds in which the CNs have different capabilities and available resources. In this paper, the context-aware task assignment (CATA) approach is proposed to improve the performance of MapReduce in a twofold manner. First, CATA takes the resource demands for different types of jobs into account. Second, CATA can assign tasks to CNs according to their capabilities and available resources in a resource-proportional manner. The experimental results show that CATA can efficiently reduce the job execution time by 10 to 40%.

Corresponding author: Chao-Chun Chen


Cite this article
Wei-Tsung Su, Wei-Fan Pan, and Chao-Chun Chen, Context-Aware Task Assignment for MapReduce in Heterogeneous Clouds, Sens. Mater., Vol. 29, No. 11, 2017, p. 1497-1512.


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