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, , Presenters: Gayathri Gandhamuneni, James Wang Team URL:

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

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

System of System Solutions

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.

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

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

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

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

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?

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

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 & can be best utilized

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

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)

Spatial Cloud Computing Framework

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

SCC: Data Intensity Scenario

SCC: Computing Intensity Scenario

SCC: Concurrent Access Intensity Scenario

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 - ( )- 1PB

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

Application Areas Spatiotemporal principle mining & extracting Important digital earth & complex geospatial science and applications Supporting the SCC characteristics Security Citizen and Social Science

Present & Future Present:

Present & Future Present: Google Maps: Encouraged Web developers Other Companies: GISCloud.com, SpatialStream.com Web based solutions for GIS functions Spatial Analysis & Data management ESRI’s ArcGIS Online – ArcGIS.com Future: Security – Personal & Sensitive data Boundaries Mostly on internet Wary about location of data and services Source:

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?

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

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, , doi: / [2] Buyya, R., Pandey, S., and Vecchiola, S., Cloudbus toolkit for market-oriented cloud computing. Cloud Computing, Lecture Notes in Computer Science, 5931 (2009), 24_44. doi: / _4. [3] Olson, A.J., Data as a service: Are we in the clouds? Journal of Map & Geography Libraries, 6 (1), 76_78. [4] Mell, P. and Grance, T., The NIST definition of cloud computing Ver. 15. [online]. NIST.gov. Available from: [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 [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: /pnas

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