Hybrid Job-Driven Scheduling for Heterogeneous MapReduce Clusters

Authors(2) :-J. Sivarani, T. Subramanyam

It is cost-efficient for a tenant with a limited budget to establish a heterogeneous virtual MapReduce clusters by renting various virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, and MapReduce still performs poorly on heterogeneous clusters, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant perspective. JoSS provide not only job level scheduling, but also Map-task level scheduling and Reduce-task level scheduling; The deployment of MapReduce in data canters and clouds present several challenges, improve data locality for both map-level task and reduce-level task, avoid job starvation and improve job execution performance. Two variations of JoSS-Task and JoSS-Job are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations (JoSS-T and JoSS-J) with current scheduling algorithms supported by Hadoop. The result shows that the two variations crush the opposite tested algorithms in terms of map and reduce data locality , and network overhead while not acquisition significant overhead. Additionally, the two variations area unit severally appropriate for various MapReduce-workload eventualities and supply the most effective job performance among all tested algorithms.

Authors and Affiliations

J. Sivarani
Department of Computer Science, Sri Padmavathi University, Tirupati, India
T. Subramanyam
Asst. Professor, Department of Computer Science Sri Padmavathi University, Tirupati, India

MapReduce, Hadoop, Map-task Scheduling, Reduce-task Scheduling, Heterogeneous virtual MapReduce clusters

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 330-341
Manuscript Number : CSEIT172566
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

J. Sivarani, T. Subramanyam , "Hybrid Job-Driven Scheduling for Heterogeneous MapReduce Clusters", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.330-341, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT172566

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