EScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform

Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010

Volumn , Issue , 2010, Pages

  Li, Jie ^a   Humphrey, Marty ^a   Agarwal, Deb ^b   Jackson, Keith ^b   Van Ingen, Catharine ^c   Ryu, Youngryel ^d  

^a University of Virginia   (United States)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^c MICROSOFT RESEARCH   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Cloud computing; EScience; Large scale data intensive applications

Indexed keywords

ANALYTICAL RESULTS; CLOUD COMPUTING; COMPUTATIONAL REQUIREMENTS; COST-EFFICIENT; DATA AVAILABILITY; DATA COMPLEXITY; DATA-INTENSIVE APPLICATION; DESIGNING SOFTWARES; E-SCIENCE; END USERS; INCONSISTENT DATA; LARGE SCALE DATA; LOW-COST SENSORS; RE-PROJECTION; SATELLITE DATA; SCIENCE DATA PROCESSING; SCIENTIFIC COMPUTATION; VIRTUAL MACHINES;

DISTRIBUTED COMPUTER SYSTEMS; DISTRIBUTED PARAMETER NETWORKS; METADATA; PIPELINE PROCESSING SYSTEMS; PIPELINES; RADIOMETERS; SOFTWARE ARCHITECTURE; SOFTWARE DESIGN; WINDOWS OPERATING SYSTEM;

DATA PROCESSING;

EID: 77953987463     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2010.5470418     Document Type: Conference Paper

References (21)

1. NASA MODIS Web page. http://modis.gsfc.nasa.gov/
2. C. Justice et al., "The Moderate Resolution Imaging Spectroradiometer (MODIS): land Remote Sensing for Global Change Research," IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1313-1323, 1998.
3. A. Huete et al., "Overview of the radiometric and biophysical performance of the MODIS vegetation indices," Remote Sensing of Environment, 83, 195-213, 2002.
4. W. E. Esaias et al., "An overview of MODIS capabilities for ocean science observations," IEEE Trans. Geosci. Remote Sensing, vol.36, pp. 1250-1265, July 1998.
5. Microsoft's Windows Azure Platform. http://www.microsoft.com/azure/ default.mspx
6. Amazon Elastic Compute Cloud (EC2). http://aws.amazon.com/ec2/
7. Google App Engine. http://code.google.com/appengine/
8. Constantinos Evangelinos and Chris N. Hill, "Cloud Computing for Parallel Scientific HPC Applications: Feasbility of Running Coupled Atmosphere-Ocean Climate Models on Amazon's EC2," CCA-08, 2008.
9. P. Watson, P. Lord, F. Gibson, P. Periorellis, and G. Pitsilis, "Cloud Computing for e-Science with CARMEN," Iberian Grid Infrastructure Conference, May 2008.
10. K. Keahey, R. Figueiredo, J. Fortes, T. Freeman, and M. Tsugawa, "Science Clouds: Early Experiences in Cloud Computing for Scientific Applications," Cloud Computing and Applications, 2008.
11. C. Hoffa, G. Mehta, T. Freeman, E. Deelman et al., "On the Use of Cloud Computing for Scientific Workflows", IEEE Fourth International Conference on eScience (eScience 2008), Dec. 2008.
12. E. Deelman, G. Singh, M. Livny, B. Berriman, J. Good. "The Cost of Doing Science on the Cloud: The Montage Example," Proceeding of Super Computing 2008, November 2008.
13. C. Vecchiola, M. Abedini, M. Kirley, X. Chu, and R. Buyya, "Classification of Gene Expression Data on Public Clouds," Technical Report, CLOUDS-TR-2009-2017, Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, Aug. 7, 2009.
14. Z. Hill and M. Humphrey, "A Quantitative Analysis of High Performance Computing with Amazon's EC2 Infrastructure," In Proceedings of the 10th IEEE/ACM International Conference on Grid Computing (Grid 2009). Oct 13-15 2009.
15. Christian Baun, Marcel Kunze, "Performance measurement of a private Cloud in the OpenCirrus Testbed," 4th Workshop on Virtualization in High-Performance Cloud Computing (VHPC 2009). Aug 2009.
16. "Primary Data Search," https://wist.echo.nasa.gov/api/
17. MODIS Swath Reprojection Tool. http://gcmd.nasa.gov/records/MODIS-Swath- Reprojection-Tool.html
18. Msphinx. http://www-loa.univ-lille1.fr/Msphinx/
19. .NET Access Control Service. http://www.microsoft.com/azure/ accesscontrol.mspx
20. "Hadoop MapReduce Framework," http://hadoop.apache.org/ mapreduce/.
21. M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly, "Dryad: Distributed data-parallel programs from sequential building blocks," In EuroSys 2007, March 2007.