1. Analysis of Remote Sensing Quantitative Inversion in Cloud Computing Jing Dong, Yong Xue, Ziqiang Chen, Hui Xu, Yingjie Li Institute of Remote Sensing Applications Chinese Academy of Sciences July 29, 2011

2. Aerosol Optical Depth(AOD) Retrieval Aerosol Optical Depth(AOD) Retrieval High Performance Computing High Performance Computing Institute of Remote Sensing Application, CAS

3. Outline  An Introduction of Cloud Computing  Remote Sensing Quantitative Inversion & Cloud Computing  A Possible Cloud In Remote Sensing Quantitative Inversion  Discussion and Future Work 1 Institute of Remote Sensing Application, CAS

4. Introduction  What is Cloud Computing  What can Cloud Computing do  How does Cloud Computing work 2 Institute of Remote Sensing Application, CAS

5. Introduction 3 SaaS IaaS PaaS DaaS Software as a Service Infrastructure as a Service Platform as a Service Data as a Service Institute of Remote Sensing Application, CAS A large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. ——Foster et al.(2008)

6. Introduction-cloud products Amazon Elastic Compute Cloud (Amazon EC2) of Amazon 、 Windows Azure of Microsoft 、 Google App Engine of Google 、 Blue Cloud of IBM 4 Institute of Remote Sensing Application, CAS

7. Remote Sensing Quantitative Inversion & Cloud Computing  Charge eg. Amazon EC2  Technical Support eg. Google App Engine  Security computing infrastructure maintained and controlled by third parties 5 Institute of Remote Sensing Application, CAS

8. An example-Charge  Application—remote sensing quantitative inversion  Data Resource ： MODIS for one year  Range : over China  total volume: 2.97TB (2008)  per day: 8.4GB 6 Institute of Remote Sensing Application, CAS

9. Charge Standard of Amazon in US dollars-data transfer 7 Institute of Remote Sensing Application, CAS nearly 304.13 US dollars

10. An example-Charge  Data Resource ： MODIS for one year  Range : over China  total volume: 2.97TB  per day: 8.4GB  processing time of geometric correction(GC) 8 Institute of Remote Sensing Application, CAS

11. Charge Standard of Amazon in US dollars 9 The price of on-demand instances Institute of Remote Sensing Application, CAS at least 832.2 US dollars

12. An example-Cost  Data transfer in— nearly 304.13 US dollars for the whole year of 2008  Instance rent for GC— at least 832.2 US dollars for one year data The cost is so high The cost is so high 10 Institute of Remote Sensing Application, CAS

13. An example -Technical Support  Google App Engine Java python  most of the quantitative remote sensing inversion algorithms IDL C++ 11 Institute of Remote Sensing Application, CAS

14. Security  Data Security  Network connection  Emergency breakdown in 2008 and 2011 service closed 12 Institute of Remote Sensing Application, CAS

15. A Possible Cloud 13 Advantage: avoid high cost for data transfer Disadvantage: not suitable for multi-source data comprehensive applications Institute of Remote Sensing Application, CAS

16. Discussions and Future Work  Cloud computing would not necessarily suitable for all scientific applications, especially for geoscience  Standardized metadata management mechanism, more efficient data storage and sharing method, and more reliable security mechanism are needed Institute of Remote Sensing Application, CAS 14

17. Jing Dong Email: dongjing0311@hotmail.com URL: www.tgp.ac.cn www.tgp.ac.cn