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Survey of Task Scheduling Method for Mapreduce Framework in Hadoop

Nilam Kadale, U. A. Mande Published in Network Application

IJAIS Proceedings on 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013)
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ncipet1343
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  1. Nilam Kadale and U A Mande. Article: Survey of Task Scheduling Method for Mapreduce Framework in Hadoop. IJAIS Proceedings on 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013) NCIPET(2):8-10, November 2013. BibTeX

    @article{key:article,
    	author = "Nilam Kadale and U. A. Mande",
    	title = "Article: Survey of Task Scheduling Method for Mapreduce Framework in Hadoop",
    	journal = "IJAIS Proceedings on 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013)",
    	year = 2013,
    	volume = "NCIPET",
    	number = 2,
    	pages = "8-10",
    	month = "November",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Nowadays cloud computing widely used for parallel and distributed data processing. Such as hadoop is recently mostly used for parallel and large data processing. In hadoop, mapreduce framework is programming model is allowed to process terabytes of data in very less time. Mapreduce framework uses a task scheduling method to schedule task. There are various method available for scheduling task in mapreduce framework. Survey of various task scheduling method of mapreduce framework is discussed in following sections.

Reference

  1. Dynamic Priority Scheduler for Hadoop. http://issues. apache. org/jira/browse/HADOOP-476
  2. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. In Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6, pages 10–10,Berkeley, CA, USA, 2004. USENIX Association.
  3. M. Zaharia, A. Konwinski, A. D. Joseph, R. H. Katz,and I. Stoica. Improving mapreduce performance in heterogeneous environments. Technical Report UCB/EECS-2008-99, EECS Department, Universityof California, Berkeley, Aug 2008.
  4. M. Zaharia, A. Konwinski, A. D. Joseph, R. Katz, and I. Stoica, "Improving MapReduce performance in heterogeneous environments," in Proc. of OSDI'08. Berkeley, CA, USA:pp. 29-42,2012.
  5. M. Zaharia, D. Borthakur, J. S. Sen, K. Elmeleegy, S. Shenker, and I. Stoica. Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. In Proceedings of the 5th European conference on Computer systems, EuroSys '10, pages 265–278, New York, NY, USA, 2010. ACM.
  6. M. Zaharia, D. Borthakur, J. S. Sen, K. Elmeleegy, S. Shenker, and I. Stoica. Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. In Proceedings of the 5th European conference on Computer systems, EuroSys '10, pages 265{278, New York, NY, USA, 2010. ACM.
  7. Thomas Sandholm and Kevin Lai. Dynamic proportional share scheduling in hadoop. In JSSPP '10: 15th Workshop on Job Scheduling Strategies for Parallel Processing, April,2010.
  8. X. Zhang, Y. Feng, S. Feng, J. Fan and M. Zhong. An Effective Data Locality Aware Task Scheduling Method for MapReduce Framework in Heterogeneous Environments. In Proceedings of the International Conference on Cloud and Service Computing, pp. 206-2055, 2011.
  9. X. Zhang, Z. Zhong, B. Tu, S. Feng, and J. Fan. Improving data locality of mapreduce by scheduling in homogeneous computing environments. In Proceedings of IEEE 9th International Symposium on Parallel and Distributed Processing with Applications, pages 120–126, Busan, Korea, 2011. IEEE.
  10. X. Zhang, G. Wang, Z. Yang,Y. Ding "A Two-phase Execution Engine of Reduce Tasks In Hadoop MapReduce"2012.
  11. Yahoo. Hadoop at yahoo, 2010.
  12. Zhenhua Guo, Geoffrey Fox, Mo Zhou "Investigation of Data Locality in MapReduce" 2012.

Keywords

Scheduler, task scheduling, mapreduce performance.