1. Spatial Cloud Computing
Chaowei Phil Yang, Co-Director
NASA/GMU Joint Center of Intelligent Spatial Computing for Water/Energy Sciences
Associate Professor, Geography and GeoInformation Science
George Mason Univ., Fairfax, VA,

2. Agenda Concept Examples How to implement Research directions
Why Cloud Computing?
Cloud Computing
Characteristics of Cloud Computing
Spatial Cloud Computing
Examples
GEOSS Clearinghouse
Dust Storm Forecasting & Visualization
How to implement
Research directions

3. Why Cloud Computing? Flooding
We need the right information to the right place and right people almost in real time, when flooding happened.

4. Why Cloud Computing? Flooding Analyses
The Hurricane Katrina response took about one week to integrate and develop relevant response geospatial information. The time is too long for responding.

5. Why Cloud Computing? What if we can
Integrate all geospatial data, information, knowledge, processing in a few minutes
Generate and send the right information in real time to the people including decision makers, first responders, victims
This dream requires a computing platform that
can be ready in a few minutes
can reach out to all people needed
only cost for the amount of computing used
won’t cost to maintain after the emergency response
This is exactly what cloud computing can provide

6. Cloud Computing
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
The Most popular definition is developed by NIST
NIST 2010

7. Cloud Computing
Five essential characteristics, which differentiate cloud computing from grid computing and other distributed computing paradigms:
On-demand self-service. provision computing capabilities as needed automatically.
Broad network access. available over the network and accessed through standard mechanisms.
Resource pooling. computing resources are pooled with location independence
Rapid elasticity. Capabilities can be rapidly and elastically provisioned.
Measured Service. automatically control and optimize resource
NIST 2010

8. Cloud Computing Three service models
Software as a Service (SaaSCloud), such as gmail
Platform as a Service (PaaS), such as MS Azure
Infrastructure as a Service (IaaS), such as Amazon EC2
4. Data as a Service (DaaS)
NIST 2010

9. Geospatial Science Information Workflow
IT Characteristics:
Data Intensity
Computing Intensity
Concurrent Access Intensity, and
Spatiotemporal Intensity
Geospatial sciences has four IT related characteristics. But all of them attribute to the last one, spatiotemporal intensity of the problem, the data resources, the end users, and the computing resources.

10. Spatial Cloud Computing
Refers to the distributed computing paradigm that
Enables the geospatial science discoveries, emergency responses, education, other societal benefits
Is optimized by spatiotemporal principles.
Yang C., Goodchild M., Huang Q., Nebert D., Raskin R., Xu Y., Bambacus M., Fay D., Spatial Cloud Computing: How can geospatial science use and help to shape cloud computing? International Journal on Digital Earth. 4,

11. Agenda Concept Examples How to implement Research Directions
Cloud Computing
Characteristics of CC and SCC
Spatial Cloud Computing
Examples
GEOSS Clearinghouse
Dust Storm Forecasting & Visualization
How to implement
Research Directions

12. Natural Hazards: Dust Storms Forecasting & Visualization
Objectives
Provide timely forecasting of dust storm for public health emergency responses
Provide an intuitive interface for decision makers
Enabling Computing Technologies
Cloud Computing as an advanced cloud computing platform to support simulation and forecasting.
Cloud DB as a data management tool for large volumetric data.
4D/5D Vis Tool to render the data.
Dust storm is a typical problem with the four characteristics. Cloud computing and 5D processing technologies are needed.

13. 5D visualization from 2D to 3D space, 4D (space+time), and 5D (+phenomena, such as dust)

14. Computing Intensity
The computing is very intensive and spatiotemporal related. Lots of researches are needed to enable this. Such as this one on how to leverage the biggest number of CPUs and processes.

15. GEOSS Clearinghouse Objectives
Share Global Earth Observation Data Among 140+ Countries to Address Global Challenges of Natural Hazards and Emergency Responses
Support Global End Users to Discover, Access, and Utilize EO Data
Provide Responses to End Users in Seconds
Advanced Computing Technologies
Cloud Computing (EC2 & Azure) Responds to Spike Massive Concurrent End Users
Cloud DB (SQLAzure) Manages Millions to Billions of Metadata Records
WebGIS & 5D Vis Tools to Visualizes EO Data

16. The discovery, access, and utilization of data can be integrated into one platform with this open simple search. But the problems is on how to get response in time with proper accuracy, knowledge, and large volume of data.

17. This one show if the users are spatiotemporally distributed around the world.

18. Concurrent Intensity
Cloud can help, detailed in the first paper in references.

19. Agenda Concept Examples How to implement Research Directions
Cloud Computing
Characteristics of CC
Spatial Cloud Computing
Examples
GEOSS Clearinghouse
Dust Storm Forecasting & Visualization
How to implement
Research Directions

20. New Hardware Infrastructure

21. Spatial Cloud Computing Architecture

22. Agenda Concept Examples How to implement Research Directions
Cloud Computing
Characteristics of CC
Spatial Cloud Computing
Examples
GEOSS Clearinghouse
Dust Storm Forecasting & Visualization
How to implement
Research Directions

23. Potential Research Directions
Spatiotemporal principle, thinking, and comptuing
Implement important complex geospatial science and applications for best practice
Supporting the SCC characteristics
Security
Citizen and social science issues: Trustworthy, Privacy, Ethical, etc.
Many other (scholar) aspects of geospatial sciences
Many research needs to be done. One if on security, there is no silver bullets on this. Organizational research & development is to standardize the requirements and products so end user can match their security needs to providing with relevant costs.

24. IJDE Special Issue on SCC
One announcement: IJDE SCC special issue

25. Spatial Cloud Computing Special Issue
4th Issue of 5th Volume of International Journal on Digital Earth, (New Journal, SCI Impact Factor 1.453)
Received 25 extended abstract from field leaders around the world
Selected 13 to submit full paper for review
Look for reviewers
Please
state your interests in reviewing the SCC full papers
a one page bio of you focus on cloud computing and geospatial sciences

26. Sponsors and Collaborators

27. References Definition paper
Yang, C., Goodchild M., Huang Q., Nebert D., Raskin R., Xu Y., Bambacus M., Fay D., 2011a, Spatial Cloud Computing: How could geospatial sciences use and help to shape cloud computing, International Journal on Digital Earth.
Review & Overview
Foster, I., Zhao, Y., Raicu, Y., Lu, S., Cloud Computing and Grid Computing 360-Degree Compared, In: Grid Computing Environments Workshop, GCE IEEE, Los Alamitos. 2. Yang, C., Raskin, R., Goodchild, M.F., and Gahegan, M., 2010, Geospatial Cyberinfrastructure: Past, Present and Future, Computers, Environment, and Urban Systems, 34(4):
Spatiotemporal data modeling
M.F. Goodchild, M. Yuan, and T.J. Cova (2007) Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science 21(3): 239–260. (Open Access)
Rey, S. J., and M. V. Janikas STARS: Space-Time Analysis of Regional Systems. Geographical Analysis, 38 (1): 67–86.
Systematic research
Armbrust, M, Fox, A., Griffith R., Joseph A., Katz, R. and etc, Above the Cloud: A Berkeley View of Cloud Computing, Technical Report No. UCB/EECS (Open Access)
Wang S. and Armstrong M., A theoretical approach to the use of cyberinfrastructure in geographical analysis, International Journal of Geographical Information Science 23(2), 169 – 193. (Open Access)
Yang C., Wu H., Li Z., Huang Q., Li J., 2011, Spatial Computing: Utilizing Spatial Principles to Optimize Distributed Computing for Enabling Physical Science Discoveries, Proceedings of National Academy of Sciences, doi: /pnas (Open Access)
Examplar applications
Wang, S., and Liu, Y TeraGrid GIScience Gateway: Bridging Cyberinfrastructure and GIScience. International Journal of Geographical Information Science, 23 (5):
Evangelinos C., Hill C., Cloud Computing for parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2, CCA-08 October 22–23, 2008.