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Corporation For National Research Initiatives The MAGIC Project Final Demonstration and Briefing Ira Richer 29 July 1999.

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Presentation on theme: "Corporation For National Research Initiatives The MAGIC Project Final Demonstration and Briefing Ira Richer 29 July 1999."— Presentation transcript:

1 Corporation For National Research Initiatives The MAGIC Project Final Demonstration and Briefing Ira Richer 29 July 1999

2 Corporation For National Research Initiatives MAGIC Scenario

3 Corporation For National Research Initiatives Datasets  Globe –San Francisco Bay area  Menlo Park –Ft. Irwin –Camp Pendleton (low resolution)  Camp Pendleton (high resolution) –Laguna Beach –Yosemite –Colorado –Lawrence, KS –Ohio –Washington, DC –Ft. Benning

4 Corporation For National Research Initiatives Background  The MAGIC scenario is based on applications such as mission planning and crisis management  These applications need to: –locate, process, and display real-time and/or archived datasets on-the-fly –fuse multiple types of data (e.g., weather, terrain) –be able to transfer large volumes of data for interactive visualization –support a range of user capabilities (e.g., command center, mobile user)  MAGIC focuses on a terrain visualization application that uses geo-referenced datasets

5 Corporation For National Research Initiatives Objectives  To develop an information system that provides application users with fast, interactive access to very large volumes of remote data distributed over a wide geographic area  To demonstrate the above capability in the context of a 3-D terrain visualization application

6 Corporation For National Research Initiatives MAGIC Participants  Corp. for National Research Initiatives  USGS/EROS Data Center  Lawrence Berkeley National Laboratory  Sprint  SRI International  University of Kansas

7 Corporation For National Research Initiatives Distinguishing Characteristics  Distributed information system, designed from the outset to work over a network –adaptation to network conditions and to system load  No limit on size of viewable datasets  No restrictions on location of datasets  Fusion of multiple datasets  Near real-time visualization of terrain data

8 Corporation For National Research Initiatives MAGIC Components  High-speed internetwork  Distributed parallel storage system (DPSS)  Software for terrain dataset preparation  3-D interactive terrain visualization application: TerraVision

9 Corporation For National Research Initiatives DPSS Generic Configuration High-speed internetwork Repository Processor User Repository

10 Corporation For National Research Initiatives 71 72 73 74 70 43 31 87 88 44 45 46 32 33 34 35 36 86 25 24 System Operation D1D2 73 33 35 45 SERVER 2 Server 1 D1D2 71 43 34 SERVER 1 Internetwork User 71433334357345 Tiles located on DPSS server/disk S1 D1 S1 D2 S1 D1 S2 D1 S2 D2 S2 D2 S2 D1 Tiles on path of travel Tiles retrieved and transmitted

11 Corporation For National Research Initiatives Today’s Demonstration and Briefing  3-D, interactive visualization of large volumes of remote data  Platform scalability  On-demand data preparation and 3-D flyover  Fusion of multiple types of data from multiple sources  DPSS fault tolerance

12 Corporation For National Research Initiatives Today’s Configuration DARPAISI-E LBNL ESnet GPN NREN ATDnet Abilene EDC NASA Goddard KU DPSS

13 Corporation For National Research Initiatives Dataset Characteristics

14 Corporation For National Research Initiatives Data Flow (1) DARPAISI-E LBNL GPN NREN ATDnet DPSS Abilene EDC DPSS NASA Goddard KU DPSS ESnet

15 Corporation For National Research Initiatives Dataset Characteristics

16 Corporation For National Research Initiatives Platform Scalability  SGI Octane (~$50k) –OC-3, IRIX, DPSS –60 frame/sec, ~80Mb/s  Dual-processor PC with graphics accel. (~$8k) –Fast Ethernet, Windows NT, HTTP –30 frame/sec, ~0.5Mb/s (Internet-limited)  Dual-processor PC (~$4k) –OC-3, Linux, DPSS –10 frame/sec, ~10Mb/s  Laptop (~$3k) –Fast Ethernet, Windows 98, HTTP –~4 frame/sec, ~0.5Mb/s (Internet-limited)

17 Corporation For National Research Initiatives TerraVision II  Composite datasets: data from multiple sources, at different resolutions  VRML overlays: wind vectors, buildings, roads; viewable with standard browser –Wind-vector dataset hosted at NAVO  Co-registered maps for navigation  Pre-defined flight paths for operations support, testing

18 Corporation For National Research Initiatives DPSS Cache Architecture

19 Corporation For National Research Initiatives DPSS Costs (Jan. ‘99)

20 Corporation For National Research Initiatives Monitoring and measurement  Real-time traffic monitor  DPSS system monitor  NetLogger –Logs events at critical points of distributed applications –Provides complete view of system operation –Visualization tools allow for real-time monitoring  Agent-based management of measurement tools

21 Corporation For National Research Initiatives NetLogger Instrumentation

22 Corporation For National Research Initiatives DPSS Capabilities  Load balancing –Dataset must be replicated –Tile requests sent to server that will provide the best performance, based on current server load and network conditions  Automatic TCP tuning –Window size determined by throughput and delay information  Fault tolerance –Automatic detection of failed server, switchover to alternate servers, and reconnection after recovery

23 Corporation For National Research Initiatives Data Flow (2) DARPAISI-E LBNL ESnet GPN NREN ATDnet DPSS Abilene EDC DPSS NASA Goddard KU DPSS

24 Corporation For National Research Initiatives Technology Transfer and “Spinoffs”  DPSS –Earth Systems Grid (climate modeling) –Kaiser Permanente project –Combustion Corridor (diesel modeling) –Matisse (MEMS/Supernet)  NetLogger –Being used by many network and grid projects

25 Corporation For National Research Initiatives Technology Transfer and “Spinoffs” (concluded)  USGS archive –Web-based access to digital earth-science data –Gateway to the Earth: Ohio Pilot (automatic dissemination of Landsat imagery)  TerraVision –BADD (Dissemination of VRML terrain datasets to warfighter) –Digital Earth (fast indexing of geo-data, multimodal capabilities, and education applications) –Netscape plug-in


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