The Community for Technology Leaders
RSS Icon
Issue No.04 - April (2008 vol.41)
pp: 33-40
Yong Xue , State Key Laboratory of Remote Sensing Science
Wei Wan , State Key Laboratory of Remote Sensing Science
Yingjie Li , Graduated University of the Chinese Academy of Sciences
Jie Guang , State Key Laboratory of Remote Sensing Science
Linyian Bai , State Key Laboratory of Remote Sensing Science
Ying Wang , State Key Laboratory of Remote Sensing Science
Jianwen Ai , State Key Laboratory of Remote Sensing Science
The remote sensing information service grid node (RSIN) is a tool for dealing with climate change and quantitative environmental monitoring. Based on the high-throughput computing grid, RSIN enables a workflow management system for data placement. The accompanying unified data-and-computation-schedule algorithm helps load balancing between and within workflow steps.
data-intensive computing, distributed system, grid computing, remote sensing
Yong Xue, Wei Wan, Yingjie Li, Jie Guang, Linyian Bai, Ying Wang, Jianwen Ai, "Quantitative Retrieval of Geophysical Parameters Using Satellite Data", Computer, vol.41, no. 4, pp. 33-40, April 2008, doi:10.1109/MC.2008.132
1. C.O. Justice et al., "An Overview of MODIS Land Data Processing and Product Status," Remote Sensing of Environment, vol. 83, 2002, pp. 3–15.
2. I. Foster, C. Kesselman, and S. Tuecke, "The Anatomy of the Grid: Enabling Scalable Virtual Organizations," Int'l J. Supercomputing Applications, vol. 15, 2001, pp. 1–10.
3. G. Kola et al., "DISC: A System for Distributed Data Intensive Scientific Computing," Proc. 1st Workshop Real, Large Distributed Systems (WORLDS 04), 2004; full_papers/kolakola.pdf.
4. K. Ranganathan and I. Foster, "Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications," Proc. 11th IEEE Int'l Symp. High-Performance Distributed Computing (HPDC 02), IEEE Press, 2002, p. 352.
5. E. Deelman et al., "Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems," Scientific Programming J., vol. 13, 2005, pp. 219–237.
6. R. Raman, M. Livny, and M. Solomon, "Matchmaking: Distributed Resource Management for High-Throughput Computing," Proc. 7th IEEE Int'l Symp. High-Performance Distributed Computing, IEEE Press, 1998, pp. 140–146.
7. J. Frey and T. Tannenbaum, "Condor-G: A Computation Management Agent for Multi-Institutional Grid," Cluster Computing, vol. 5, 2001, pp. 237–246.
8. T. Kosar and M. Livny, "Stork: Making Data Placement a First-Class Citizen in the Grid," Proc. 24th IEEE Int'l Conf. Distributed Computing Systems (ICDCS 04), IEEE Press, 2004, pp. 342–349.
9. M. Jain and C. Dovrolis, "End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with Tcp Throughput," IEEE/ACM Trans. Networking, vol. 11, 2003, pp. 537–549.
10. M. Silberstein et al., "Scheduling Mixed Workloads in Multi-Grids: The Grid Execution Hierarchy," Proc. 15th IEEE Symp. High-Performance Distributed Computing (HPDC 06), IEEE Press, 2006, pp. 291–302.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool