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Performance Improvement of MapReduce for Heterogeneous Clusters Based on Efficient Locality and Replica Aware Scheduling (ELRAS) Strategy

Abstract

MapReduce is a parallel programming model for processing the data-intensive applications in a cloud environment. The scheduler greatly influences the performance of MapReduce model while utilized in heterogeneous cluster environment. The dynamic nature of cluster environment and computing workloads affect the execution time and computational resource usage in the scheduling process. Further, data locality is essential for reducing total job execution time, cross-rack communication, and to improve the throughput. In the present work, a scheduling strategy named efficient locality and replica aware scheduling (ELRAS) integrated with an autonomous replication scheme (ARS) is proposed to enhance the data locality and performs consistently in the heterogeneous environment. ARS autonomously decides the data object to be replicated by considering its popularity and removes the replica as it is idle. The proposed approach is validated in a heterogeneous cluster environment with various realistic applications that are IO bound, CPU bound and mixed workloads. ELRAS improves the throughput by a factor about 2 as compared with the existing FIFO and it also yields near optimal data locality, reduce the execution time, and effective utilization of resources. The simplicity of ELRAS algorithm proves its feasibility to adopt for a wide range of applications.

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Acknowledgements

The author(s) greatly acknowledge the support of Department of Computer Science and Engineering, Anna University—Regional Campus, Tirunelveli, India for providing the computing facilities to complete this research work successfully.

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Correspondence to J. V. Bibal Benifa.

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Bibal Benifa, J.V., Dejey Performance Improvement of MapReduce for Heterogeneous Clusters Based on Efficient Locality and Replica Aware Scheduling (ELRAS) Strategy. Wireless Pers Commun 95, 2709–2733 (2017). https://doi.org/10.1007/s11277-017-3953-5

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Keywords

  • MapReduce programming model
  • Data locality
  • Heterogeneous clusters
  • Virtualization