MRScheduling: An Effective Technique for Multi-Tenant Meeting Deadline in MapReduce

Article Preview

Abstract:

The multi-tenant jobs scheduling problem based on MapReduce framework has become more and more significant in contemporary society. Existing scheduling approach or algorithm no longer fit well in scenario that numerous jobs were submitted by multiple users at the same time. Therefore, taken enlarging jobs’ throughput for MapReduce into account, we firstly propose an MRScheduling which focuses on meeting job’s respective deadline. Considering the various parameters which are related to job execution time of a MapReduce’s job, we present a simply time-cost model, for the aim that quantifying the number of job’s assigned map slots and reduce slots. Then, an MRScheduling algorithm is discussed in details. Finally, we perform our approach on both real data and synthetic data on real distributed cluster to verify its effectiveness and efficiency.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

4482-4486

Citation:

Online since:

September 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Dean, Jeffrey, and Sanjay Ghemawat. MapReduce: simplified data processing on large clusters., Communications of the ACM 51. 1 (2008): 107-113.

DOI: 10.1145/1327452.1327492

Google Scholar

[2] MapReduce: A Flexible Data Processing Tool.

Google Scholar

[3] Bryant R E. Data-Intensive Supercomputing: the Case for DISC[R].

Google Scholar

[4] Nykiel T, Potamias M, Mishra C, et al. MRShare: sharing across multiple queries in MapReduce[J]. Proceedings of the VLDB Endowment, 2010, 3(1-2): 494-505.

DOI: 10.14778/1920841.1920906

Google Scholar

[5] Ghemawat S, Gobioff H, Leung S T. The Google file system[C]/ACM SIGOPS Operating Systems Review. ACM, 2003, 37(5): 29-43.

DOI: 10.1145/1165389.945450

Google Scholar

[6] Kc K, Anyanwu K. Scheduling hadoop jobs to meet deadlines[C]/Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on. IEEE, 2010: 388-392.

DOI: 10.1109/cloudcom.2010.97

Google Scholar

[7] Lin X, Lu Y, Deogun J, et al. Real-time divisible load scheduling for cluster computing[C]/Real Time and Embedded Technology and Applications Symposium, 2007. RTAS'07. 13th IEEE. IEEE, 2007: 303-314.

DOI: 10.1109/rtas.2007.29

Google Scholar