TMaR: a two-stage MapReduce scheduler for heterogeneous environments

Human-centric Computing and Information Sciences

Neda Maleki¹  Hamid Reza Faragardi²  Mauro Conti³  Amir Masoud Rahmani⁴  Jay Lofstead⁵ 

[1] Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;Department of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden;Department of Mathematics, University of Padua, Padua, Italy;Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam;Faculty of Information Technology, Duy Tan University, 550000, Da Nang, Vietnam;Department of Computer Science, Khazar University, Baku, Azerbaijan;Sandia National Laboratories, Albuquerque, NM, USA;

关键词: MapReduce;    Hadoop;    Heterogeneous systems;    Scheduling;    Performance;    Shuffling;    Power;    Cloud computing;   

DOI  :  10.1186/s13673-020-00247-5

来源: Springer

【 摘 要 】

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 finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic 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%.

【 授权许可】

CC BY