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Spatial Cloud ComputingSpatial Cloud ComputingSpatial Cloud ComputingSpatial Cloud Computing

Chaowei Phil Yang, Co-DirectorNASA/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/

Chaowei Phil Yang, Co-DirectorNASA/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/

Agenda

Concept• Why Cloud Computing?• Cloud Computing• Characteristics of Cloud Computing• Spatial Cloud Computing

Examples• GEOSS Clearinghouse• Dust Storm Forecasting & Visualization

How to implementResearch 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

responseThis is exactly what cloud computing can provide

Cloud ComputingCloud 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.

NIST 2010

Cloud ComputingCloud ComputingFive essential characteristics, which differentiate cloud computing from grid computing and other distributed computing paradigms: oOn-demand self-service. provision computing capabilities as needed automatically. oBroad network access. available over the network and accessed through standard mechanisms.oResource pooling. computing resources are pooled with location independenceoRapid elasticity. Capabilities can be rapidly and elastically provisioned.oMeasured Service. automatically control and optimize resource

NIST 2010

Cloud ComputingCloud Computing

Three service models1.Software as a Service (SaaSCloud), such as gmail2.Platform as a Service (PaaS), such as MS Azure3.Infrastructure as a Service (IaaS), such as Amazon EC2

4. Data as a Service (DaaS)

NIST 2010

Geospatial Science Information WorkflowGeospatial Science Information Workflow

IT Characteristics: Data IntensityComputing IntensityConcurrent Access Intensity, and Spatiotemporal Intensity

Spatial Cloud ComputingSpatial 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 implementResearch Directions

Natural Hazards: Dust Storms Forecasting & Visualization

Objectives1.1. Provide timely forecasting of dust storm for public health emergency Provide timely forecasting of dust storm for public health emergency

responses responses 2.2. Provide an intuitive interface for decision makersProvide an intuitive interface for decision makers

Enabling Computing Enabling Computing Technologies Technologies 1.1. Cloud Computing as an Cloud Computing as an

advanced cloud computing advanced cloud computing platform to support simulation platform to support simulation and forecasting.and forecasting.

2.2. Cloud DB as a data management Cloud DB as a data management tool for large volumetric data.tool for large volumetric data.

3.3. 4D/5D Vis Tool to render the 4D/5D Vis Tool to render the data.data.

Computing IntensityComputing Intensity

Advanced Computing TechnologiesAdvanced Computing Technologies• Cloud Computing (EC2 & Azure) Responds to Spike Cloud Computing (EC2 & Azure) Responds to Spike

Massive Concurrent End UsersMassive Concurrent End Users• Cloud DB (SQLAzure) Manages Millions to Billions of Cloud DB (SQLAzure) Manages Millions to Billions of

Metadata RecordsMetadata Records• WebGIS & 5D Vis Tools to Visualizes EO DataWebGIS & 5D Vis Tools to Visualizes EO Data

GEOSS Clearinghouse

ObjectivesObjectives Share Global Earth Observation Data Among 140+ Countries to Address Global Share Global Earth Observation Data Among 140+ Countries to Address Global

Challenges of Natural Hazards and Emergency ResponsesChallenges of Natural Hazards and Emergency Responses Support Global End Users to Discover, Access, and Utilize EO DataSupport Global End Users to Discover, Access, and Utilize EO Data Provide Responses to End Users in SecondsProvide Responses to End Users in Seconds

Concurrent IntensityConcurrent Intensity

Agenda

Concept• Cloud Computing• Characteristics of CC• Spatial Cloud Computing

Examples• GEOSS Clearinghouse• Dust Storm Forecasting & Visualization

How to implementResearch Directions

New Hardware Infrastructure

Spatial Cloud Computing ArchitectureSpatial Cloud Computing Architecture

Agenda

Concept• Cloud Computing• Characteristics of CC• Spatial Cloud Computing

Examples• GEOSS Clearinghouse• Dust Storm Forecasting & Visualization

How to implementResearch Directions

Potential Research DirectionsPotential 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 worldSelected 13 to submit full paper for reviewLook 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

ReferencesDefinition paper1.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 & Overview1.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 research1.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.