A data placement strategy for data-intensive applications in cloud

被引：24
|
作者：
Zheng P. [1 ]
Cui L.-Z. [1 ]
Wang H.-Y. [1 ]
Xu M. [1 ]
机构：
[1] School of Computer Science and Technology, Shandong University
来源：
Jisuanji Xuebao/Chinese Journal of Computers | 2010年 / 33卷 / 08期
关键词：
Cloud computing; Data dependency; Data placement; Data-intensive; Process;
D O I：
10.3724/SP.J.1016.2010.01472
中图分类号：
学科分类号：
摘要：
With the development of information technology, data-intensive applications in cloud have been used in more and more fields. Because of the decentralized data centers in cloud, these applications now are facing some new challenges in data placement which mainly include how to reduce the time cost of data movements between data centers, how to deal with the data dependencies, and how to keep a relative load balancing of data centers. This paper proposes a data placement strategy, the three stages of which address the three challenges above respectively. Simulation shows that the strategy can effectively reduce the time cost of data movements across data centers during the application's execution.
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页码：1472 / 1480
页数：8
相关论文
共 15 条
  • [1] Deelman E., Chervenak A., Data management challenges of data-intensive scientific workflows, Proceedings of the IEEE International Symposium on Cluster Computing and the Grid(CCGRID), pp. 687-692, (2008)
  • [2] Deelman E., Blythe J., Gil Y., Kesselman C., Mehta G., Patil S., Su M.H., Vahi K., Livny M., Pegasus: Mapping scientific workflows onto the grid, Proceedings of the European Across Grids Conference(AxGrids), pp. 11-20, (2004)
  • [3] Ludascher B., Altintas I., Berkley C., Higgins D., Jaeger E., Jones M., Lee E.A., Scientific workflow management and the Kepler system, Concurrency and Computation: Practice and Experience, 18, 10, pp. 1039-1065, (2005)
  • [4] Oinn T., Addis M., Ferris J., Marvin D., Senger M., Greenwood M., Carver T., Glover K., Pocock M.R., Wipat A., Li P., Taverna: A tool for the composition and enactment of bioinformatics workflows, Bioinformatics, 20, 17, pp. 3045-3054, (2004)
  • [5] Ghemawat S., Gobioff H., Leung S.T., The google file system, ACM SIGOPS Operating Systems Review, 37, 5, pp. 29-43, (2003)
  • [6] Wang L., Tao J., Kunze M., Castellanos A.C., Kramer D., Karl W., Scientific cloud computing: Early definition and experience, Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications(HPCC), pp. 825-830, (2008)
  • [7] Wieczorek M., Prodan R., Fahringer T., Scheduling of scientific workflows in the ASKALON grid environment, SIGMOD Record, 34, 3, pp. 56-62, (2005)
  • [8] Baru C., Moore R., Rajasekar A., Wan M., The SDSC storage resource broker, Proceedings of the IBMCentre for Advanced Studies Conference, pp. 1-12, (1998)
  • [9] Churches D., Gombas G., Harrison A., Maassen J., Robinson C., Shields M., Taylor I., Wang I., Programming scientific and distributed workflow with Triana services, Concurrency and Computation: Practice and Experience, 18, pp. 1021-1037, (2006)
  • [10] Chervenak A., Deelman E., Foster I., Guy L., Hoschek W., Iamnitchi A., Kesselman C., Kunszt P., Ripeanu M., Schwartzkopf B., Stockinger H., Stockinger K., Tierney B., Giggle: A framework for constructing scalable replica location services, Proceedings of the ACM/IEEE Conference on Supercomputing, pp. 1-17, (2002)
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