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DRAGON Dynamic Resource Allocation via GMPLS Optical Networks

DRAGON Dynamic Resource Allocation via GMPLS Optical Networks. Jerry Sobieski University of Maryland (UMD) Mid-Atlantic Crossroads (MAX). Tom Lehman University of Southern California Information Sciences Institute (USC/ISI). National Science Foundation. Bijan Jabbari

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DRAGON Dynamic Resource Allocation via GMPLS Optical Networks

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  1. DRAGONDynamic Resource Allocation via GMPLS Optical Networks Jerry Sobieski University of Maryland (UMD) Mid-Atlantic Crossroads (MAX) Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National Science Foundation Bijan Jabbari George Mason University (GMU)

  2. DRAGON Team Members • University of Maryland (UMD) Mid-Atlantic CrossRoads (MAX) • University of Southern California Information Sciences Institute (USC/ISI) • George Mason University (GMU) • Movaz Networks • MIT Haystack Observatory • NASA Goddard Space Flight Center (GSFC) • US Naval Observatory • National Center for Supercomputing Applications (NCSA) Alliance Center for Collaboration, Education, Science, and Software (ACCESS)

  3. DRAGON Objectives • Experiment with next generation regional optical network architectures, features, capabilities • Experiment with eScience applications • What network features and capabilities are needed to support eScience applications? • What features do eScience applications need to include, to best utilize next generation networks? • Build collaborations between network community and eScience communities • to utilize next generation networks to enable advanced science in those domains

  4. DRAGON Activities • Instantiation of an Experimental Regional Optical Network in Washington D.C. region • “Hybrid” Packet Switched and Circuit Switched Infrastructure • All optical core • Protocol agnostic (HDTV, ethernet, sonet, fibreChannel, any optical signal) • Dynamic provisioning of end-to-end paths • Inter-Domain • Authentication, Authorization, Accounting • Scheduling • Integrate eScience applications • eVLBI • High Definition format collaboration and remote steering/display of visualization resources

  5. End to End GMPLS TransportWhat is missing?

  6. DRAGON Architecture Components • Network Aware Resource Broker (NARB) • Inter-domain routing for GMPLS TE Capabilities • IGP/EGP Listener • Path Computation • AAA • Scheduling (and monitoring/enforcement) • Application Request Processing • Virtual Label Switched Router (VLSR) • Proxy for non-GMPLS capable network segments • Application Specific Topology Descriptions Language (ASTDL) • Language for application requests to network • All Optical End-to-End Routing

  7. VLSR Abstraction

  8. Application Specific Topology Description Language - ASTDL

  9. Heterogeneous Network TechnologiesComplex End to End Paths

  10. DRAGON NetworkOptical Transport layer - Year 3 All Optical Core Dynamic Provisioning of “Application Topologies”

  11. DRAGON Network – Example Topology

  12. Commercial PartnerMovaz Networks • MEMS-based switching fabric • 400 x 400 wavelength switching, scalable to 1000s x 1000s • 9.23"x7.47"x3.28" in size • Integrated multiplexing and demultiplexing, eliminating the cost and challenge of complex fiber management • Dynamic power equalization (<1 dB uniformity), eliminating the need for expensive external equalizers • Ingress and egress fiber channel monitoring outputs to provide sub-microsecond monitoring of channel performance using the OPM • Switch times < 5ms

  13. eVLBI Experiment Configuration - Goals • electronic-Very Long Baseline Interferometry (e-VLBI) • MIT Haystack • NASA GSFC (GGAO) • USNO • Radio Telescopes reachable via other Infrastructures • eVLBI Experiment Configuration

  14. Uncompressed HDTV-over-IPCurrent Method

  15. Low latency High Definition CollaborationDRAGON Enabled • End-to-end native SMPTE 292M transport • Media devices are directly integrated into the DRAGON environment via proxy hosts • Register the media device (camera, display, …) • Sink and source signaling protocols • Provide Authentication, authorization and accounting.

  16. Low Latency Visual Area Networking • Directly share output of visualization systems across high performance networks. • DRAGON allows minimum latency paths.

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