Spatial Cloud Computing

Spatial Cloud Computing: How can the geospatial sciences use and help shape cloud computing?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 WangTeam URL: http://www-users.cs.umn.edu/~yumeng/TopicsMotivationProblem Statement & IllustrationChallengesMajor ContributionValidation MethodologyProposed Approach SCC ScenariosKey ConceptsCloud Computing, Spatial Cloud ComputingAssumptionsPreserve and Revise MotivationConstant changes Better recorded through space time dimensional dataExabytes of data accumulatedIncreasing at rate of PBAnalysis of information changingUnderstand, protect & improve living environmentEx: Predict events like earthquakes, tsunamisNeed of computing infrastructure that canReduce IT workReal time applications supportDeal with access spikes, Support massive users

System of System Solutions

Large amounts of geo spatial data and lack of computing infrastructurePractical approaches to solve regional, local and global issues.4 Problem StatementInput: 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.

ChallengesInformation Technology challenges for Geospatial sciencesData IntensitySupport of massive data storage, processing & system expansionComputing IntensityAlgorithms and models based on Earth phenomena are complexComplexity grasp of spatiotemporal principlesConcurrent Access IntensityLot of end users trying to access concurrently Spatiotemporal intensityGeospatial datasets space time dimensionsSpatiotemporal Static/Dynamic

Major ContributionsCategorization - Challenges of Geospatial Sciences in 21st centuryRelation of Cloud Computing & Geospatial SciencesCloud Computing usage and how spatiotemporal principles enhance itExamples to show how spatial cloud computing can solve 4 intensity problems Most SignificantLooks ahead to see possible solutions for intensity problems Cloud ComputingAdvanced Distributed ComputingProvides 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 servicesResources can be provisioned and released fastMinimal management effort Service provider interaction

Characteristics of Cloud ComputingCloud Computing difference to other distributed approachesOn-Demand Self ServiceAs needed automaticallyBroad Network AccessDifferent types of network terminalsResource PoolingConsolidation of diff. types of Computing resourcesRapid ElasticityRapidly & elastically provisioning, allocating & releasing resourcesMeasured ServiceSupports pay-as-you-go approachAdvantages of Cloud ComputingRapid DeploymentDependability/Redundancy Flexibility/ScalabilityLevelled Playing FieldSecurityIdentity Management & Access ControlWhat are the advantages of Cloud Computing?Rapid Deployment Nothing to buy, setup and implement Lot of time savedCost effective solutions Dependable and reliable best of class solutions based on industry best practicesNeeds change Flexibly can increase or decrease resourcesSize of organization not an issueSecurity Focus a lot on securityIdentity Management Advanced solutions for access control and identity management that ensure users and uses of data are legitimate11 Services for Cloud ComputingCloud 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

IaaS Most popular Delivers computer infrastructure including physical machines, networks, storagePaaS Higher level than IaaS Platform service for software developers to develop applications Azure, Google App EngineSaaS Most used type Provides various capabilities of sophisticated applications that are provided through web applications Gmail ArcGIS implementation on cloud Example of SaaSDaaS Least defined - facilitate data discoverability, accessibility and utilizability on the fly to support science on demand12 Uses of Cloud ServicesEarth Observation (EO) Data Access:Fast, secure access & utilization of EO data Storage & Processing needs - DaaS

Parameter Extraction:Complex geospatial processes Reformatting & ReprojectingPaaS can be used

Knowledge & Decision Support:Used by domain experts, managers or publicSaaS provides good support

Social Impact & Feedback:SaaS such as Facebook & email can be best utilizedCloud services could be used to support the elements in geospatial sciences according to their respective characteristics.Parameter Extraction Extracting parameters from EO data

13Spatial Cloud Computing (SC2)Cloud Computing ParadigmDriven by geospatial sciencesOptimized by Spatiotemporal principles

Geospatial Science ProblemsIntensive Spatiotemporal constraints & PrinciplesBest if spatiotemporal rules for geospatial domains usedAdds geography to Cloud Computing paradigm14 GeoSpatial PrinciplesPhysical phenomena are Continuous Heterogeneous in space, time, and space-time scales;Semi-independent across localized geographic domains and can be divided and conqueredGeospatial science and application problems include the spatiotemporal locations of Data StorageComputing/processing resources Physical phenomenaUsersSpatiotemporal phenomena that are closer are more related (Tobler first law of geography)Spatial Cloud Computing Framework

Validation Methodology Four scenarios given for 4 intensity problems in order to validate their workCase study to show that SCC might solve the four problems of geospatial sciences

SCC: Data Intensity Scenario

SCC: Computing Intensity Scenario SCC: Concurrent Access Intensity Scenario

SCC: Spatiotemporal Intensity ScenarioReal-time traffic network - Metropolitan area like DC,Static Routing 90k nodes, 200k links, 90k*90k origin & destination requestsSeveral Optimized routes for one OD request pair 1 GBDynamic Real Time RoutingRouting condition Changes for each min. and each link & nodeDaily - Volume increases by about (2460) 1TB Weekly (24607) 10TB Yearly - (2460365)- 1PBAssumptionsMethods and principles of geospatial sciences that can drive and shape computing technology would remain unchangedUnreliable assumptionBoth the development in technology & geospatial sciences itself might cause changes to occurValidation done with examples of particular scenarioCan cloud computing be used alwaysOverhead cost of cloud computing might be > Cost without cloud computing Application AreasSpatiotemporal principle mining & extractingImportant digital earth & complex geospatial science and applicationsSupporting the SCC characteristicsSecurityCitizen and Social SciencePage 18 22 in pdf23Present & FuturePresent:

Present & FuturePresent:Google Maps: Encouraged Web developersOther Companies: GISCloud.com, SpatialStream.comWeb based solutions for GIS functionsSpatial Analysis & Data managementESRIs ArcGIS Online ArcGIS.comFuture:Security Personal & Sensitive dataBoundaries Mostly on internetWary about location of data and servicesSource: http://www.linkedin.com/groups?gid=1839124

Google Maps - use of the web to provide map-based access to information Open free access Web developers include mapping in their appl.s

25 Exercises/Questions to CheckWhat 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?

Preserve & ReviseReviseWhole paper - Recent advancements in cloud computingMore practical examples of SC2 scenariosSecurity issues faced and any possible solutions

PreserveDifferent types of intensities Cloud Computing & SC2 key conceptsRelationship between both

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. Proceedings of National Academy of Sciences, 106 (14), 5498_5503. doi: 10.1073/pnas.0909315108.THANK YOU