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2015•Frontiers in artificial intelligence and applicationsRequires access

A Task Scheduling Policy for Heterogeneous MapReduce Cluster

Chiu Chui-Ming, Huang Sheng-Wei, Huang Tzu-Chi, Shieh Ce-Kuen, Tsai Ming-Fong, Chen Lien-Wu

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Abstract

MapReduce is a programming model and its associated run-time system proposed in 2004, which can process large scale of data in clusters with simple program logic. MapReduce has a potential problem running on a cooperative cluster which is combined with machines having different configurations. The problem will cause unexpected performance degradations and should be avoided. In this paper, a task scheduling policy is proposed to take higher utilization of all computing nodes in heterogeneous clusters.

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What this paper is about

MapReduce is a programming model and its associated run-time system proposed in 2004, which can process large scale of data in clusters with simple program logic. MapReduce has a potential problem running on a cooperative cluster which is combined with machines having different configurations. The problem will cause unexpected performance degradations and should be avoided. In this paper, a task scheduling policy is proposed to take higher utilization of all computing nodes in heterogeneous clusters.

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Available abstract

MapReduce is a programming model and its associated run-time system proposed in 2004, which can process large scale of data in clusters with simple program logic. MapReduce has a potential problem running on a cooperative cluster which is combined with machines having different configurations. The problem will cause unexpected performance degradations and should be avoided. In this paper, a task scheduling policy is proposed to take higher utilization of all computing nodes in heterogeneous clusters.

Key concepts: Computer science, Scheduling (production processes), Distributed computing, Cluster (spacecraft), Parallel computing, Task (project management), Operating system, Mathematical optimization

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