About this Journal Submit a Manuscript Table of Contents
Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 396387, 15 pages
http://dx.doi.org/10.1155/2012/396387
Research Article

A Cloud-Computing-Based Data Placement Strategy in High-Speed Railway

1State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2School of Traffic and Transportation, Beijing JiaoTong University, Beijing 100044, China

Received 28 August 2012; Revised 31 October 2012; Accepted 21 November 2012

Academic Editor: Wuhong Wang

Copyright © 2012 Hanning Wang et al. 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. Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing: state-of-the-art and research challenges,” Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7–18, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Miceli, M. Miceli, S. Jha, H. Kaiser, and A. Merzky, “Programming abstractions for data intensive computing on clouds and grids,” in Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'09), pp. 478–483, Shanghai, China, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Liua and L. J. Dong, “Research on cloud data storage technology and its architecture implementation,” Procedia Engineering, vol. 29, pp. 133–137, 2012. View at Publisher · View at Google Scholar
  4. J. Ma, “Managing metadata for digital projects,” Library Collections, Acquisition and Technical Services, vol. 30, no. 1-2, pp. 3–17, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Wang, W. Zhang, H. Guo, H. Bubb, and K. Ikeuchi, “A safety-based approaching behavioural model with various driving characteristics,” Transportation Research C, vol. 19, no. 6, pp. 1202–1214, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Wang, Vehicle’s Man-Machine Interaction Safety and Driver ASSiStance, China Communications Press, Beijing, China, 2012.
  7. V. T. Tran, G. Antoniu, B. Nicolae, L. Bougé, and O. Tatebe, “Towards a grid file system based on a large-scale BLOB management service,” in Grids, P2P and Services Computing, pp. 7–19, 2010.
  8. L. Amsaleg, M. J. Franklin, A. Tomasic, and T. Urhan, “Improving responsiveness for wide-area data access,” Data Engineering, vol. 20, pp. 3–11, 1997.
  9. A. Deshpande and Z. Ives, “Adaptive query processing,” Foundations and Trends in Databases, vol. 1, no. 1, pp. 1–140, 2007. View at Zentralblatt MATH
  10. S. Ghemawat, H. Gobioff, and S. T. Leung, “The google file system,” in Proceedings of the 19th ACM Symposium on Operating Systems Principles (SOSP'03), pp. 29–43, ACM, New York, NY, USA, October 2003. View at Scopus
  11. Apache Hadoop, http://hadoop.apache.org/.
  12. “Hadoop distributed file system,” http://hadoop.apache.org/docs/r0.18.0/hdfs_design.pdf.
  13. Amazon Elastic MapReduce, http://aws.amazon.com/elasticmapreduce/.
  14. J. Tao, M. Kunze, A. C. Castellanos, L. Wang, D. Kramer, and W. Karl, “Scientific cloud computing: early definition and experience,” in Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC'08), pp. 825–830, Dalian, China, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Zhai, M. Liu, J. Zhai, and X. Ma, “Cloud versus in-house cluster: evaluating Amazon cluster compute instances for running MPI applications,” in Proceedings of the SC'11 State of the Practice Reports, 2011.
  16. C. H. C. Evangelinos, “Cloud computing for parallel scientific HPC applications: feasibility of running coupled atmosphere-ocean climate models on Amazon's EC2,” in Proceedings of the 1st Workshop on Cloud Computing and Its Applications (CCA'08), October 2008.
  17. P. H. Carns, I. W. Ligon, R. Ross, and R. Thakur, “PVFS: a parallel file system for linux clusters,” in Proceedings of the 4th Annual Linux Showcase and Conference, Atlanta, Ga, USA, 2000.
  18. “Cloud computing with parallel storage,” http://www.panasas.com/blog/cloud-computing-with-parallel-storage.
  19. D. Yuan, Y. Yang, X. Liu, and J. Chen, “A data placement strategy in scientific cloud workflows,” Future Generation Computer Systems, vol. 26, no. 8, pp. 1200–1214, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Maheshwari, R. Nanduri, and V. Varma, “Dynamic energy efficient data placement and cluster reconfiguration algorithm for MapReduce framework,” Future Generation Computer Systems, vol. 28, no. 1, pp. 119–127, 2012. View at Publisher · View at Google Scholar
  21. 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
  22. R. Chaiken, B. Jenkins, P. A. Larson, and B. Ramsey, “SCOPE: easy and efficient parallel processing of massive data sets,” in Proceedings of the VLDB Endowment VLDB Endowment Hompage Archive, vol. 1-2, pp. 1265–1276, 2008. View at Zentralblatt MATH
  23. D. Karger, E. Lehman, T. Leighton, and R. Panigrahy, “Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web,” in Proceedings of the 29th annual ACM Symposium on Theory of Computing (STOC'97), pp. 654–663, ACM, New York, NY, USA, 1999.
  24. J. Dhok, N. Maheshwari, and V. Varma, “Learning based opportunistic admission control algorithm for MapReduce as a service,” in Proceedings of the 3rd India Software Engineering Conference (ISEC'10), pp. 153–160, ACM, Mysore, India, February 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. K. H. Kim, R. Buyya, and J. Kim, “Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters,” in Proceedings of the 7th IEEE International Symposium on Cluster Computing and the Grid (CCGrid'07), pp. 541–548, Rio De Janeiro, Brazil, May 2007. View at Publisher · View at Google Scholar · View at Scopus