Chaowei Yang, Michael Goodchild, Qunying Huang, Doug Nebert,
Robert Raskin, Yan Xu, Myra Bambacus & Daniel Fay (2011)
Spatial cloud computing: how can the geospatial sciences use and
help shape cloud computing?, International Journal of Digital
Earth, 4:4, 305-329, Presenters: Gayathri Gandhamuneni, James Wang
Team URL:
http://www-users.cs.umn.edu/~yumeng/http://www-users.cs.umn.edu/~yumeng/
Slide 3
Topics Motivation Problem Statement & Illustration
Challenges Major Contribution Validation Methodology Proposed
Approach SCC Scenarios Key Concepts Cloud Computing, Spatial Cloud
Computing Assumptions Preserve and Revise
Slide 4
Motivation Constant changes Better recorded through space time
dimensional data Exabytes of data accumulated Increasing at rate of
PB Analysis of information changing Understand, protect &
improve living environment Ex: Predict events like earthquakes,
tsunamis Need of computing infrastructure that can Reduce IT work
Real time applications support Deal with access spikes, Support
massive users
Slide 5
System of System Solutions
Slide 6
Problem Statement Input: Geospatial Sciences (GS) Information
Output: Computing Infrastructure suitable for GS Objective:
Research on challenges in geospatial sciences and use of Spatial
Cloud Computing for solutions. Constraints: SpatioTemporal
Principles & Geospatial env.
Slide 7
Challenges Information Technology challenges for Geospatial
sciences Data Intensity Support of massive data storage, processing
& system expansion Computing Intensity Algorithms and models
based on Earth phenomena are complex Complexity grasp of
spatiotemporal principles Concurrent Access Intensity Lot of end
users trying to access concurrently Spatiotemporal intensity
Geospatial datasets space time dimensions Spatiotemporal
Static/Dynamic
Slide 8
Slide 9
Major Contributions Categorization - Challenges of Geospatial
Sciences in 21 st century Relation of Cloud Computing &
Geospatial Sciences Cloud Computing usage and how spatiotemporal
principles enhance it Examples to show how spatial cloud computing
can solve 4 intensity problems Most Significant Looks ahead to see
possible solutions for intensity problems
Slide 10
Cloud Computing Advanced Distributed Computing Provides
computing as a service Pay-as-you-go model Model: Convenient,
on-demand network access Access to shared pool of computing
resources Ex: networks, servers, storage, applications and services
Resources can be provisioned and released fast Minimal management
effort Service provider interaction
Slide 11
Characteristics of Cloud Computing Cloud Computing difference
to other distributed approaches On-Demand Self Service As needed
automatically Broad Network Access Different types of network
terminals Resource Pooling Consolidation of diff. types of
Computing resources Rapid Elasticity Rapidly & elastically
provisioning, allocating & releasing resources Measured Service
Supports pay-as-you-go approach
Slide 12
Advantages of Cloud Computing Rapid Deployment
Dependability/Redundancy Flexibility/Scalability Levelled Playing
Field Security Identity Management & Access Control What are
the advantages of Cloud Computing?
Slide 13
Services for Cloud Computing Cloud Computing is provided
through 4 services Infrastructure as a Service (IaaS) Platform as a
Service (PaaS) Software as a Service (SaaS) Data as a Service
(DaaS) Geospatial Sciences
Slide 14
Uses of Cloud Services Earth Observation (EO) Data Access:
Fast, secure access & utilization of EO data Storage &
Processing needs - DaaS Parameter Extraction: Complex geospatial
processes Reformatting & Reprojecting PaaS can be used
Knowledge & Decision Support: Used by domain experts, managers
or public SaaS provides good support Social Impact & Feedback:
SaaS such as Facebook & email can be best utilized
Slide 15
Spatial Cloud Computing (SC2) Cloud Computing Paradigm Driven
by geospatial sciences Optimized by Spatiotemporal principles
Geospatial Science Problems Intensive Spatiotemporal constraints
& Principles Best if spatiotemporal rules for geospatial
domains used
Slide 16
GeoSpatial Principles Physical phenomena are Continuous
Heterogeneous in space, time, and space-time scales;
Semi-independent across localized geographic domains and can be
divided and conquered Geospatial science and application problems
include the spatiotemporal locations of Data Storage
Computing/processing resources Physical phenomena Users
Spatiotemporal phenomena that are closer are more related (Tobler
first law of geography)
Slide 17
Spatial Cloud Computing Framework
Slide 18
Validation Methodology Four scenarios given for 4 intensity
problems in order to validate their work Case study to show that
SCC might solve the four problems of geospatial sciences
Slide 19
SCC: Data Intensity Scenario
Slide 20
SCC: Computing Intensity Scenario
Slide 21
SCC: Concurrent Access Intensity Scenario
Slide 22
SCC: Spatiotemporal Intensity Scenario Real-time traffic
network - Metropolitan area like DC, Static Routing 90k nodes, 200k
links, 90k*90k origin & destination requests Several Optimized
routes for one OD request pair 1 GB Dynamic Real Time Routing
Routing condition Changes for each min. and each link & node
Daily - Volume increases by about (2460) 1TB Weekly (24607) 10TB
Yearly - (2460365)- 1PB
Slide 23
Assumptions Methods and principles of geospatial sciences that
can drive and shape computing technology would remain unchanged
Unreliable assumption Both the development in technology &
geospatial sciences itself might cause changes to occur Validation
done with examples of particular scenario Can cloud computing be
used always Overhead cost of cloud computing might be > Cost
without cloud computing
Slide 24
Application Areas Spatiotemporal principle mining &
extracting Important digital earth & complex geospatial science
and applications Supporting the SCC characteristics Security
Citizen and Social Science
Slide 25
Present & Future Present:
Slide 26
Present & Future Present: Google Maps: Encouraged Web
developers Other Companies: GISCloud.com, SpatialStream.com Web
based solutions for GIS functions Spatial Analysis & Data
management ESRIs ArcGIS Online ArcGIS.com Future: Security Personal
& Sensitive data Boundaries Mostly on internet Wary about
location of data and services Source:
http://www.linkedin.com/groups?gid=1839124http://www.linkedin.com/groups?gid=1839124
Slide 27
Exercises/Questions to Check What are the problems faced by
geospatial data? What are geospatial principles? What does system
of systems solution include? What is Cloud Computing? Different
services of Cloud Computing? How is Cloud Computing different from
others? What is Spatial Cloud Computing? What scenarios Spatial
Cloud Computing can be used in context of geospatial sciences?
Slide 28
Preserve & Revise Revise Whole paper - Recent advancements
in cloud computing More practical examples of SC2 scenarios
Security issues faced and any possible solutions Preserve Different
types of intensities Cloud Computing & SC2 key concepts
Relationship between both
Slide 29
References [1] Chaowei Yang, Michael Goodchild, Qunying Huang,
Doug Nebert, Robert Raskin, Yan Xu, Myra Bambacus & Daniel Fay
(2011) Spatial cloud computing: how can the geospatial sciences use
and help shape cloud computing?, International Journal of Digital
Earth, 4:4, 305- 329, doi: 10.1080/17538947.2011.587547 [2] Buyya,
R., Pandey, S., and Vecchiola, S., 2009. Cloudbus toolkit for
market-oriented cloud computing. Cloud Computing, Lecture Notes in
Computer Science, 5931 (2009), 24_44. doi:
10.1007/978-3-642-10665-1_4. [3] Olson, A.J., 2010. Data as a
service: Are we in the clouds? Journal of Map & Geography
Libraries, 6 (1), 76_78. [4] Mell, P. and Grance, T., 2009. The
NIST definition of cloud computing Ver. 15. [online]. NIST.gov.
Available from: http://csrc.nist.gov/groups/SNS/cloud-computing/
[5] Yang, C., et al., 2011a. WebGIS performance issues and
solutions. In: S. Li, S. Dragicevic, and B. Veenendaal, eds.
Advances in web-based GIS, mapping services and applications.
London: Taylor & Francis Group, ISBN 978-0-415-80483-7. [6]
Yang C., et al., 2011b. Using spatial principles to optimize
distributed computing for enabling physical science discoveries.
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doi: 10.1073/pnas.0909315108.