1 / 29

Spatial Cloud Computing: How can the geospatial sciences use and help shape cloud computing?

Spatial Cloud Computing: How can the geospatial sciences use and help shape cloud computing?.

kirk
Download Presentation

Spatial Cloud Computing: How can the geospatial sciences use and help shape cloud computing?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 Wang Team URL: http://www-users.cs.umn.edu/~yumeng/

  2. 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

  3. 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

  4. System of System Solutions

  5. Problem Statement • Input: Geospatial Sciences (GS) Information • Output: Computing Infrastructure suitablefor GS • Objective: Research on challenges in geospatial sciences and use of Spatial Cloud Computing for solutions. • Constraints: SpatioTemporal Principles & Geospatial env.

  6. 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

  7. Major Contributions • Categorization - Challenges of Geospatial Sciences in 21st 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

  8. 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

  9. 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

  10. 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?

  11. 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

  12. 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

  13. 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

  14. GeoSpatialPrinciples • 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)

  15. Spatial Cloud Computing Framework

  16. 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

  17. SCC: Data Intensity Scenario

  18. SCC: Computing Intensity Scenario

  19. SCC: Concurrent Access Intensity Scenario

  20. 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

  21. 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

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

  23. Present & Future • Present:

  24. 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: http://www.linkedin.com/groups?gid=1839124

  25. 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?

  26. 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

  27. 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.

  28. THANK YOU

More Related