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Title: TMaR: a two‑stage MapReduce scheduler for heterogeneous environments
Authors: Maleki, Neda
Faragardi, Hamid Reza
Rahmani, Amir Masoud
Conti, Mauro
Lofstead, Jay
Keywords: MapReduce
Heterogeneous systems
Cloud computing
Issue Date: 7-Oct-2020
Citation: Human-centric Computing and Information Sciences
Series/Report no.: Vol. 10;№ 42
Abstract: In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task fnish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shufing trafc. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network trafc. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network trafc and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.
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