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An Algorithm for Partitioning Community Graph into Sub-community Graphs Using Graph Mining Techniques Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Performance Improvement of MapReduce Framework by Identifying Slow TaskTrackers in Heterogeneous Hadoop Cluster

Authors : Nenavath Srinivas Naik, Atul Negi, V. N. Sastry

Published in: Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Publisher: Springer India

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Abstract

MapReduce is presently recognized as a significant parallel and distributed programming model with wide acclaim for large scale computing. MapReduce framework divides a job into map, reduce tasks and schedules these tasks in a distributed manner across the cluster. Scheduling of tasks and identification of “slow TaskTrackers” in heterogeneous Hadoop clusters is the focus of recent research. MapReduce performance is currently limited by its default scheduler, which does not adapt well in heterogeneous environments. In this paper, we propose a scheduling method to identify “slow TaskTrackers” in a heterogeneous Hadoop cluster and implement the proposed method by integrating it with the Hadoop default scheduling algorithm. The performance of this method is compared with the Hadoop default scheduler. We observe that the proposed approach shows modest but consistent improvement against the default Hadoop scheduler in heterogeneous environments. We see that it improves by minimizing the overall job execution time.

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Literature
1.
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRef
2.
Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2010)CrossRef
3.
Rasooli, A., Down, D.G.: An adaptive scheduling algorithm for dynamic heterogeneous hadoop systems. In: Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research, pp. 30–44. Canada (2011)
4.
Zaharia, M., Borthakur, D., Sarma, J.S., Elmeleegy, K., Shenker, S., Stoica, I.: Job Scheduling for Multi-User MapReduce Clusters. Technical Report, University of California, Berkeley (2009)
5.
Dawei, J., Beng, C.O., Lei, S., Sai, W.: The Performance of MapReduce: An In-depth Study. VLDB (2010)
6.
Zaharia, M., Konwinski, A., Joseph, A.D., Katz, R., Stoica, I.: Improving mapreduce performance in heterogeneous environments. In: 8th Usenix Symposium on Operating Systems Design and Implementation, pp. 29–42. ACM Press, New York (2008)
7.
Tan, J., Meng, X., Zhang, L.: Delay Tails in Mapreduce Scheduling. Technical Report, IBM T. J. Watson Research Center, New York (2011)
8.
Ekanayake, J., Pallickara, S., Fox, G.: MapReduce for data intensive scientific analyses. In: Proceedings of the 2008 IEEE Fourth International Conference on eScience, pp. 277–284 (2008)
9.
Rasooli, A., Down, D.G.: A hybrid scheduling approach for scalable heterogeneous Hadoop systems. In: Proceeding of the 5th Workshop on Many-Task Computing on Grids and Supercomputers, pp. 1284–1291 (2012)
10.
Nanduri, R., Maheshwari, N., Reddyraja, A., Varma, V.: Job aware scheduling algorithm for mapreduce framework. In: Proceedings of the 3rd International Conference on Cloud Computing Technology and Science, pp. 724–729, Washington, USA (2011)
11.
Zhenhua, G., Geo, R.F., Zhou, M., Yang, R.: Improving resource utilization in MapReduce. In: IEEE International Conference on Cluster Computing, pp. 402–410 (2012)
12.
Rasooli, A., Down, D.G.: COSHH: a classification and optimization based scheduler for heterogeneous Hadoop systems. J. Future Gener. Comput. Syst. 1–15 (2014)
13.
Naik, N.S., Negi, A., Sastry, V.N.: A review of adaptive approaches to MapReduce scheduling in heterogeneous environments. In: IEEE International Conference on Advances in Computing, Communications and Informatics, pp. 677–683, Delhi, India (2014)
14.
Shengsheng, H., Jie, H., Jinquan, D., Tao, X., Huang, B.: The HiBench benchmark suite: characterization of the MapReduce-based data analysis. In: IEEE 26th International Conference on Data Engineering Workshops, pp. 41–51 (2010)
Metadata
Title
Performance Improvement of MapReduce Framework by Identifying Slow TaskTrackers in Heterogeneous Hadoop Cluster
Authors
Nenavath Srinivas Naik
Atul Negi
V. N. Sastry
Copyright Year
2016
Publisher
Springer India
Book
Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Print ISBN: 978-81-322-2528-7

Electronic ISBN: 978-81-322-2529-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-81-322-2529-4_49

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