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GeoApps in the AWS Cloud - Jinesh Varia
- 2. “GeoApps” Geo-Mapping and Visualization Location Based Services (LBS) Analyzing Geo-data Location based Mobile Apps Geospatial Applications Geo-Social Apps Emergency response Maps Geographic Information Systems Map-based Mashups and overlays
- 3. “GeoApps” Geo-Mapping and Visualization Location Based Services (LBS) Analyzing Geo-data Location based Mobile Apps Geospatial Applications Geo-Social Apps Emergency response Maps Geographic Information Systems Map-based Mashups and overlays Powered by the Cloud
- 4. The AWS Cloud Tools to access services Cross Service features High-level building blocks Low-level building blocks
- 5. Platform that provides foundation to build innovation solutions on top Platform that provides abstraction to hide underlying layers (hardware and software) Platform that is self-service The Cloud as a Platform for Platforms
- 6. The Cloud as a Platform for Geo
- 7. GeoApps Need Cloud Geo-compute and Analysis Massively App Server & Service Scalable Database Orientation Services
- 8. GeoApps Need Cloud Geo-compute and Analysis Elasticity Massively App Server & Service Scalable Database Orientation Services
- 9. 1 Job, 1 Machine, 500 hours 500 Hours $1500 To rack and stack on-premise 1 Job, 500 Machines, 1 hour One Hour $26 For 3 years (reserved 100% utilized) 0
- 10. SwissTopo - The Swiss Federal Office of Topography
- 11. SwissTopo - The Swiss Federal Office of Topography GIS App: http://map.geo.admin.ch/
- 12. SwissTopo - The Swiss Federal Office of Topography 30,000 unique visitors per day 8 TB per month 250,000,000 preproduced map tiles 1,300 delivered map tiles per second 40 GIS applications 40 Web map services
- 13. High Performance WMS Service in the cloud
- 14. Topographical data + Hydrological data + Cloud computing = good dams What is the best location for the dam for optimum results? What is the best height for the dam? It would take 35 days of non-stop computation
- 15. Digital Elevation Model Whether it makes Elevation Elevation economic sense ? Distance Distance
- 16. “Cloud HD” High Performance Cloud computing with Amazon EC2 Cluster Compute Instance Family 2 * Xeon 5570 (“Intel Nehalem”) 23 GB RAM 10 gbps Ethernet 1690 TB local disk HVM-based virtualization $1.60/hour
- 17. Amazon EC2 Instances Start End AWS Master Node Notify From IDE Command line Input output Web Console dataset results Input Data Input S3 Output Get Results bucket S3 bucket Amazon S3
- 18. GeoApps Need Cloud Geo-compute and Analysis Elasticity Massively App Server & Service Scalable Database Orientation Services
- 19. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Massively App Server & Service Scalable Database Orientation Services
- 20. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Massively App Server & Service Scalable Database Orientation Services
- 22. Recovery.gov First government-wide system to move to the cloud Savings of over $750,000 in current budget cycle “Cloud computing strikes me as a perfect tool to help achieve greater transparency and accountability. Moving to the cloud allows us to provide better service at lower costs. I hope this development will inspire other government entities to accelerate their own efforts. The American taxpayers would be the winners.’’ - Earl E. Devaney, the Board’s Chairman.
- 24. Recovery.gov
- 25. ArcGIS Autoscaling Architecture in the Cloud More Info: (Whitepaper) http://bit.ly/aws-esri-arcgis-paper
- 28. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Massively App Server & Service Scalable Database Orientation Services
- 29. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Massively App Server & Service Scalable Database Orientation Services
- 30. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively App Server & Service Scalable Database Orientation Services
- 31. World Wide Storage and Delivery Infrastructure Amazon S3 Amazon CloudFront • Elastic • Low Cost No Distributed Commitment Highly Content Redundant Delivery Data Store 5 Regions 18 Edge Locations US West (N. California) Ashburn, Dallas, Los Angeles, Miami, US East (N. Virgina) Newark, Palo Alto, Seattle, St. Louis, EU West (Ireland) Amsterdam, Dublin, Frankfurt, APAC (Singapore) London, Hong Kong, Singapore, APAC (Japan) Tokyo
- 32. GeoData Library
- 33. Share your Geo datasets
- 36. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively App Server & Service Scalable Database Orientation Services
- 37. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Scalable Database Orientation Services
- 38. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Services
- 40. Time to provision a server in an enterprise 350,000 Minutes (7-8 Months) $1000 To rack and stack on-premise Time to provision a server in the cloud <5 Minutes $260 For 3 years (reserved 100% utilized)
- 41. Natural Disasters and Crisis
- 42. Haitian Earthquake ESRI was able to provision ArcGIS server on Amazon EC2 in less than 2 hours
- 44. www.Gulfofmexicoresponsemap.com was built using ArcServer Standard version 9.3.1 leveraging the Flex API which utilizes ArcGIS Online, Microsoft Bing, and Response Content • Response content is refreshed twice a day • Solution was deployed in Amazon
- 45. Japan Earthquake + Tsunami
- 46. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Services
- 47. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Geo-Collaboration Services
- 48. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibility Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Geo-Collaboration EasyServices
- 52. “Customers can immediately access and integrate most current geospatial data into business analyses, location-based marketing programs and risk management calculations” Geosk Platform and Geosk Library will be powered by WeoGeo, which is powered by Amazon Web Services
- 53. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibilty Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Geo-Collaboration EasyServices
- 54. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibilty Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Geo-Collaboration EasyServices Innovation Open
- 55. GIS Amazon Machine Images
- 57. GeoApps Need Cloud Geo-compute and Analysis Elasticity Visualization and Geo-Mapping Flexibilty Geo-Data Storage and Delivery Low Cost Massively AgilityServer &Crisis Mapping App during Service Self Scalable Service Database Orientation Geo-Collaboration EasyServices Innovation Open
- 58. The Cloud is an ideal platform for Geo
- 59. KEY BENEFITS TO RUNNING IN THE AWS CLOUD Lowers Cost Increases Agility Eliminates Capital Reduce Time to Market Investment Removes constraints Reduces Operational Costs Removes the “Heavy Foundation for Lifting” 21st Century Leverages Scalability, Reliability and Security Architectures
Editor's Notes
- Jinesh
- Swisstopo currently serves with its Amazon EC2 instances up to 30,000 unique visitors per day. This equates to data transfer of 8 TB per month; up to 1,300 delivered map tiles per second; 250,000,000 preproduced map tiles in Amazon Simple Storage Service (Amazon S3); and 40 GIS applications with 40 Web map services for Swiss Federal offices, among other customers. Swisstopo estimates it is now possible for the FSDI team to launch a new server within hours, instead of the weeks or months it took before using AWS.
- The first diagram depicts a setup to do high performance OGC WMS setup for a lot of source image files, usually uncompressed GeoTiff files. This setup puts source GeoTiffs on S3 allowing n-number of servers to be started without having to script loading of large EBS volumes with data for the whole area. Each zone group acts like a stripe of the overall coverage area. This happens automatically because the front-end index cluster only refers requests for certain geographic areas to certain zones, making them cache just those files to local EC2 volume.
- This is an example what data can do today.An Analyst firm PSR took topographical data from NASA of area of 1o latitude x 1o longitude of an river in India (Sub river of Ganga) and analyzed the data with hydrological information (river flow) to determine the best site for constructions of hydro plants.Hydro inventory studies determine the best sites for the construction of hydro plants and their size in terms of installed capacity, dam height, reservoir area and storage capacity. These studies rely on topographical and hydrological information (hydropower just the conversion of potential energy into kinetic energy that moves the turbines and finally electric energy produced by the generator which is coupled to the turbine). Thus, the key components for hydropower are the artificially produced head by the damming of the river and the river streamflow (hydrology). NASA and the Japanese Government launched in 2009 a public database with an approximated topographical data for the entire Earth, based on satellite infrared measurements. One selects the area of interest (river basin plans where hydro projects are being planned) and downloads the data from NASA’s servers. Each file contains topographical data that covers an area of 1o latitude x 1o longitude (see below and notice “scarf” running above the red arrow, indicating the river pathway). The first task then becomes to massively process this data, which is also known as Digital Elevation Model (DEM) in order to: Infer the river path (Geographical Information Systems are used in this task)Determine the river elevation profile (from headwaters to mouth)Calculate cross sections profiles at various pointsUse all previous information to estimate the investment costs to build the hydro plant, deciding its best construction layout For instance, a river with an extension of 500km for which one investigates where to develop hydro plants based on a discretization of one kilometer and in each case how high the dam should be based on 20 elevations and choosing from 5 different engineering layouts would require 500 x 20 x 5 = 50,000 alternatives. Our model currently estimates construction costs in roughly one minute per alternative. Thus, if using a single machine, the total processing time to carry out the previous tasks would require 35 days of non-stop computation. As each computation may be carried out independently, the procedure may be processed in parallel using the Amazon Web Services - AWS. After pre-processing is made (steps (i)-(iv) above), the best construction alternative in each point is known. It remains to be determined where to build the plants (i.e. which of the those 500 sites are the “right ones”? and in each case “what is the best height for the dam?”). The problem becomes (mathematically) interesting because the combinations of possible ways to explore the hydro potential with the plants is exponential. The next figure shows two alternatives, one with three larger plants (left) and another with four smaller ones (right). To answer these questions, we formulate a Mixed Integer Programming model (MIP) that uses all pre-processed information regarding the construction costs and alternatives and determines the optimum solution, that is, that maximizes the economic benefit of developing the projects. This benefit is measured by a cash flow analysis that considers revenues related to the sales of electricity produced in the hydro plants on one side and the investment and operation costs on another. Socio-environmental factors are also considered in the analysis as economically interesting projects may not be feasible due to constraints.
- they provide a visual representation of the US with the distribution of stimulus funding in the form of contracts, loans and grants. If you drill down into the top level map, there is a detailed map that allows you to see the funding and programs right down to an individual township. For each contract, grant or load you can see the dollars that were spent and the jobs that were created. This tracks the distribution of $$ that were made available as part of President Obama’s stimulus package – called the Recovery Act.
- they provide a visual representation of the US with the distribution of stimulus funding in the form of contracts, loans and grants. If you drill down into the top level map, there is a detailed map that allows you to see the funding and programs right down to an individual township. For each contract, grant or load you can see the dollars that were spent and the jobs that were created. This tracks the distribution of $$ that were made available as part of President Obama’s stimulus package – called the Recovery Act.
- We deploy GeoIQ through Amazon cloud services and are moving the entire GeoCommons to AWS. In about a month we will be launching several major NGO and US Federal Government geo-sites all on AWS.
- The 2nd diagram, shows the first diagram as the wms service on the left and in the middle shows yet another Elastic Beanstalk app that is the Tile Making app. The Tile Making app is driven by messages that are recursively created in SQS to buildup certain parts of world into TMS type databases. From my earlier comments. This system takes advantage of the "auto"features of AWS, but more importantly to run something like this without incurring licensing fees, all of the component parts WANT to be FOSS. One US State at an aerial image resolution of 50cm/pixel can have on the order of 10 million tiles, just for the base layer. This system allows SpatialCloud to generate the full TMS "stack" for such an area in hours, not days. SpatialCloud has used diagram setups and other similiar tools, to process hundreds of millions of image tiles to S3. The SpatialCloud USA dataset alone is 200 million. The reason you want to pre-generate the TMS on S3 is because one server instance of WMS ( for an average state ) would require about 2TB of EBS volume to store uncompressed source gtifs and for it to be fast you would need to multiple instances and configure caching etc.Its less expensive to just run many servers once and pre-generate the files to S3. Once on S3 you can run other FOSS4G tools such as MapProxy to run WMS using the TMS'ed image content. Handling larger raster datasets for image data, for elevation data etc is a perfect use-case for on-demand nature of AWS services.
- Jinesh
- Haiti Earthquake occurred on Jan 12thFirst satellite images were available Jan 13thNeed to get viewer up to facilitate emergency response efforts
- ESRI built application to allow for translation of up to the minute satellite images of the affected areaRelief workers were able to quickly identify evacuation routes and get aid to the hardest hit areas
- BP Gulf Oil Spill ResponseIn response to the Deepwater Horizon incident, The Joint Incident Command needed to have a publicly facing interactive websiteNeed to rapidly deploy a site that would support the public’s need to have a view into the responseTimeframe was extremely tight, need to go in roughly a week from idea to global deploymentAmazon cloud was the most cost effective solution
- Mapufacture was actually acquired by FortiusOne in August of 2008 - we just haven't shutdown the service. It has been entirely incorporated into the fuller featured GeoCommons (http://geocommons.com). GeoCommons, and Mapufacture before it, aims to provide collaboration and access to data, visualization and analytics. On the public web with GeoCommons we allow anyone to upload and share open data. Our customers then license the platform, GeoIQ, to combine their business data with open data. For example, we work with real estate companies to compare their appraisals and listings with demographic information, school ratings, and even social media. We also actively work in Crisis response - as a founding member of CrisisCommons, and original member of OpenStreetMap, to use GeoCommons as a way to gather data that is useful to responders and citizens in dealing with a disaster.