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Mathematical Problems in Engineering
Volume 2015, Article ID 408921, 7 pages
Research Article

A Replication-Based Mechanism for Fault Tolerance in MapReduce Framework

College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China

Received 20 October 2014; Accepted 31 January 2015

Academic Editor: Hui-Huang Hsu

Copyright © 2015 Yang Liu and Wei Wei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Dean and S. Ghemawat, “MapReduce: simplified data processingon large clusters,” in Proceedings of the 6th Symposiumon Operating Systems Design & Implementation, vol. 3, pp. 102–111, 2004.
  2. U. Hoelzle and L. A. Barroso, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Morgan and Claypool Publishers, 2009.
  3. T. White, Hadoop: The Definitive Guide, O'Reilly, 2009.
  4. Apache Hadoop,
  5. D. Borthakur, The Hadoop Distributed File System: Architecture and Design,
  6. S. Ghemawat, H. Gobioff, and S. T. Leung, “The Google file system,” SIGOPS Operating Systems Review, vol. 37, no. 5, pp. 29–43, 2003. View at Google Scholar
  7. B. Selic, “Fault tolerance techniques for distributed systems,”
  8. F. Wang, J. Qiu, J. Yang, B. Dong, X. Li, and Y. Li, “Hadoop high availability through metadata replication,” in Proceedings of the 1st International Workshop on Cloud Data Management (CloudDB '09), pp. 37–44, ACM, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. Amazon Elastic Mapreduce,
  11. J. Dean, “Experiences with MapReduce, an abstraction for large-scale computation,” in Proceedings of the Internet Conference on Parallel Architectures and Computation Techniques (PACT '06), vol. 8, pp. 5–10, Seattle, Wash, USA, 2006.
  12. S. Y. Ko, I. Hoque, B. Cho, and I. Gupta, “On availability of intermediatedata in cloud computations,” in Proceedings of the The USENIX Workshop onHot Topics in Operating Systems (HotOS '09), vol. 5, pp. 32–38, Ascona, Switzerland, 2009.
  13. J.-A. Quiané-Ruiz, C. Pinkel, J. Schad, and J. Dittrich, “RAFTing MapReduce: fast recovery on the RAFT,” in Proceedings of the IEEE 27th International Conference on Data Engineering (ICDE '11), vol. 3, pp. 589–600, IEEE, Hannover, Germany, April 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Hui, Q. Kan, S. Xian-He, and L. Ying, “Performance under failures of mapreduce applications,” in IEEE/ACM International Symposium on Cluster Cloud and Grid Computing, pp. 608–609, Newport Beach, Calif, USA, 2011.
  15. A. Martin, T. Knauth, S. Creutz et al., “Low-overhead fault tolerance for high-throughput data processing systems,” in Proceedings of the 31st International Conference on Distributed Computing Systems (ICDCS '11), pp. 689–699, Minneapolis, Minn, USA, June 2011. View at Publisher · View at Google Scholar
  16. S. Y. Ko, I. Hoque, B. Cho, and I. Gupta, “Making cloud intermediate data fault-tolerant,” in Proceedings of the 1st ACM Symposium on Cloud Computing, vol. 1, pp. 181–192, Indianapolis, Ind, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. Q. Zheng, “Improving mapreduce fault tolerance in the cloud,” in Proceedings of the IEEE International Symposium on Parallel Distributed Processing Workshops and Phd Forum (IPDPSW '10), vol. 1, pp. 1–6, New York, NY, USA, 2010.
  18. J. Liu, X. G. Luo, B. N. Li, X. M. Zhang, and F. Zhang, “An intelligent job scheduling system for web service in cloud computing,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 12, pp. 2956–2961, 2013. View at Publisher · View at Google Scholar
  19. S. Hong, S.-p. Chen, J. Chen, and G. Kai, “Research and simulation of task scheduling algorithm in cloud computing,” Telkomnika, vol. 11, no. 11, pp. 1923–1931, 2013. View at Google Scholar