Context-aware task assignment for MapReduce in heterogeneous clouds

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

Research output: Contribution to journalArticle

Abstract

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%.

Original languageEnglish
Pages (from-to)1497-1512
Number of pages16
JournalSensors and Materials
Volume29
Issue number11
DOIs
Publication statusPublished - 2017 Jan 1

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resources
Parallel processing systems
Sensors
programming
optimization
sensors

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Materials Science(all)

Cite this

Su, Wei Tsung ; Pan, Wei Fan ; Chen, Chao-Chun. / Context-aware task assignment for MapReduce in heterogeneous clouds. In: Sensors and Materials. 2017 ; Vol. 29, No. 11. pp. 1497-1512.
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Context-aware task assignment for MapReduce in heterogeneous clouds. / Su, Wei Tsung; Pan, Wei Fan; Chen, Chao-Chun.

In: Sensors and Materials, Vol. 29, No. 11, 01.01.2017, p. 1497-1512.

Research output: Contribution to journalArticle

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