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, 22030-4444
http://cisc.gmu.edu/
http://cpgis.gmu.edu/homepage/
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Agenda
Concept
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Why Cloud Computing?
Cloud Computing
Characteristics of Cloud Computing
Spatial Cloud Computing
Examples
• GEOSS Clearinghouse
• Dust Storm Forecasting & Visualization
How to implement
Research directions
Why Cloud Computing? Flooding
Why Cloud Computing? Flooding Analyses
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
Cloud Computing
Cloud computing is a model for enabling convenient, ondemand 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.
NIST 2010
Cloud Computing
Five essential characteristics, which differentiate cloud computing
from grid computing and other distributed computing paradigms:
o On-demand self-service. provision computing capabilities as
needed automatically.
o Broad network access. available over the network and accessed
through standard mechanisms.
o Resource pooling. computing resources are pooled with
location independence
o Rapid elasticity. Capabilities can be rapidly and elastically
provisioned.
o Measured Service. automatically control and optimize resource
NIST 2010
Cloud Computing
Three service models
1.Software as a Service (SaaSCloud), such as gmail
2.Platform as a Service (PaaS), such as MS Azure
3.Infrastructure as a Service (IaaS), such as Amazon
EC2
4. Data as a Service (DaaS)
NIST 2010
Geospatial Science Information Workflow
IT Characteristics:
Data Intensity
Computing Intensity
Concurrent Access Intensity, and
Spatiotemporal Intensity
Spatial Cloud Computing
Refers to the distributed computing paradigm that
1. Enables the geospatial science discoveries,
emergency responses, education, other societal
benefits
2. Is optimized by spatiotemporal principles.
Yang C., Goodchild M., Huang Q., Nebert D., Raskin R., Xu Y., Bambacus M., Fay D., 2011. Spatial Cloud
Computing: How can geospatial science use and help to shape cloud computing? International Journal on
Digital Earth. 4, 305-329.
Agenda
Concept
• Cloud Computing
• Characteristics of CC and SCC
• Spatial Cloud Computing
Examples
• GEOSS Clearinghouse
• Dust Storm Forecasting & Visualization
How to implement
Research Directions
Natural Hazards: Dust Storms Forecasting & Visualization
 Objectives
1. Provide timely forecasting of dust storm for public health emergency
responses
2. Provide an intuitive interface for decision makers
 Enabling Computing
Technologies
1.
2.
3.
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.
Computing Intensity
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
Concurrent Intensity
Agenda
Concept
• Cloud Computing
• Characteristics of CC
• Spatial Cloud Computing
Examples
• GEOSS Clearinghouse
• Dust Storm Forecasting & Visualization
How to implement
Research Directions
New Hardware Infrastructure
Spatial Cloud Computing Architecture
Agenda
Concept
• Cloud Computing
• Characteristics of CC
• Spatial Cloud Computing
Examples
• GEOSS Clearinghouse
• Dust Storm Forecasting & Visualization
How to implement
Research Directions
Potential Research Directions
1. Spatiotemporal principle, thinking, and
comptuing
2. Implement important complex geospatial
science and applications for best practice
3. Supporting the SCC characteristics
4. Security
5. Citizen and social science issues: Trustworthy,
Privacy, Ethical, etc.
6. Many other (scholar) aspects of geospatial
sciences
IJDE Special Issue on SCC
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 email cyang3@gmu.edu
• state your interests in reviewing the SCC full papers
• a one page bio of you focus on cloud computing and
geospatial sciences
Sponsors and Collaborators
References
Definition paper
1. 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
1. Foster, I., Zhao, Y., Raicu, Y., Lu, S., 2008. Cloud Computing and Grid Computing 360-Degree Compared, In: Grid Computing
Environments Workshop, GCE 2008. 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):264-277.
Spatiotemporal data modeling
1. 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)
2. Rey, S. J., and M. V. Janikas. 2006. STARS: Space-Time Analysis of Regional Systems. Geographical Analysis, 38 (1): 67–86.
Systematic research
1. Armbrust, M, Fox, A., Griffith R., Joseph A., Katz, R. and etc, 2009. Above the Cloud: A Berkeley View of Cloud Computing,
Technical Report No. UCB/EECS-2009-28. (Open Access)
2. Wang S. and Armstrong M., 2009. A theoretical approach to the use of cyberinfrastructure in geographical analysis, International
Journal of Geographical Information Science 23(2), 169 – 193. (Open Access)
3. 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: 10.1073/pnas.0909315108. (Open
Access) http://www.pnas.org/content/early/2011/03/21/0909315108.full.pdf
Examplar applications
1. Wang, S., and Liu, Y. 2009. TeraGrid GIScience Gateway: Bridging Cyberinfrastructure and GIScience. International Journal of
Geographical Information Science, 23 (5): 631-656.
2. Evangelinos C., Hill C., 2008. 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.