DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern...

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

Transcript of DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern...

Page 1: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

DRAGON

Dynamic Resource Allocation via GMPLS

Optical Networks

Tom LehmanUniversity of Southern CaliforniaInformation Sciences Institute (USC/ISI)

National Science Foundation

Jerry SobieskiUniversity of Maryland (UMD)Mid-Atlantic Crossroads (MAX)

Bijan JabbariGeorge Mason University (GMU)

Page 2: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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)

Page 3: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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 communitieso to utilize next generation networks to

enable advanced science in those domains

Page 4: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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

Page 5: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

End to End GMPLS TransportWhat is missing?

Page 6: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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

Page 7: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

VLSR Abstraction

Page 8: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

Application Specific Topology Description Language - ASTDL

Page 9: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

Heterogeneous Network TechnologiesComplex End to End Paths

Page 10: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

DRAGON NetworkOptical Transport layer - Year 3

All Optical Core

Dynamic Provisioning of “Application Topologies”

Page 11: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

DRAGON Network – Example Topology

Page 12: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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

Page 13: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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

CLPK

GWU

ARLG

NASAGSFC

MAXUMD

BossNetUSNO

Radio Telescopeat GGAO

CorrelatorCorrelator

Radio Telescopesreachable via Abilene

Radio Telescopeat Haystack

Radio Telescopesreachable via NLR

MITHaystack

NLR

Abilene

ECK

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Uncompressed HDTV-over-IPCurrent Method

Page 15: DRAGON Dynamic Resource Allocation via GMPLS Optical Networks Tom Lehman University of Southern California Information Sciences Institute (USC/ISI) National.

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.

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Low Latency Visual Area Networking

• Directly share output of visualization systems across high performance networks.• DRAGON allows minimum latency paths.