nonlinearity estimation for efficient resource allocation in elastic ...€¦ · resource...
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High Performance Networks Group
RuiWang,Sarvesh Bidkar,RezaNejabati andDimitraSimeonidouHighPerformanceNetworksGroup,UniversityofBristol
Nonlinearity Estimation for Efficient Resource Allocation in Elastic Optical
Networks
High Performance Networks Group
Outline
• Motivation
• Currentsolutionofnonlinearityanalysis
• Proposedhybridnonlinearitymodel
• Nonlinearityawareresourceallocationalgorithm
• Results
• Conclusion
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High Performance Networks Group
Motivation
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• Opticallinkintroducepenaltieswhichaffectconnectionsquality.
ROADM
Optical Link
ROADM
Transponder Transponder
Capacity BERReachDistance
Linear Impairments:AttenuationCDPMDPDLCrosstalk
Nonlinear Impairments:SPMXPMFWMSBSSRS
Easy to compensateDifficult to mitigate
High Performance Networks Group
ExistingMethodtoUseNonlinearityInformation
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Pros: Easy to implement/calculate. Does not require complex model or expensive nonlinear impairments monitoring techniques.
Cons: Sacrifice network performance when the network not in worst case.
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Channel Index
SNR
Channel SNR based on worst case
16 QAM SNR Requirement
Channel SNR based on real nonlinearity
Worst-case or known as reference margin (RM) method
High Performance Networks Group
ExistingMethodtoUseNonlinearityInformation
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Pros: More accurate information, improve network performance if traffic matrix pre-known.
Cons: Computational complex model, expensive monitoring hardware. May block the future requests when traffic matrix unknown.
Accurate nonlinearity information
16 QAM SNR Requirement 16 QAM SNR Requirement
§ Existing channel§ New channel
High Performance Networks Group
EffectsofTwoNonlinearityAnalysisMethods
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Worst-case/reference margin method has much better performance in terms of blocking ratio.This is due to inter-channel blocking problem.
High Performance Networks Group
HybridNonlinearityModel
• Hybridnonlinearitymodel– Step-wisemarginbasedonassignedspectrumindexandlinkoccupancy
condition.
– 5loadingstatesofcontinuouschanneloccupancywithinanopticallinkas20%,40%,60%,80%or100%occupiedassumedinourwork.
– Nonlinearityarecalculatedbasedonabove5loadingstatesinadvanced.
– Nointer-channelblockingforaddingnewlightpath whenthelinkremainswithinsameloadingstate.
Thus𝑃"#$ = 𝑃&'( ∑ 𝛽+,-.�+,-,.
𝛽+,-. :nonlinearcoefficientoflink𝑙,frequencyindex𝑚 andloadingstate𝑛
𝑃&':lauch powerofonefrequencyslot
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High Performance Networks Group
HybridNonlinearityModelPerformance
• SequentialloadedEON:• Upto130more100Grequestsusingcongestion-awarerouting• Upto100moremixedtrafficrequestsincongestion-awarerouting• Stillbetterperformanceundertwotrafficmodelusingshortestpath
routing.
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High Performance Networks Group
HybridNonlinearityModelPerformance
• Usinghybridnonlinearitymodeltendstoutilizemorehighmodulationformatsthanconservativereferencemargin(RM)method.
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High Performance Networks Group
NARAAlgorithmFlowchart
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Newrequest
FindKleastcongestedpaths
yes
Yes
No
Calculateend-to-endSNRbasedonHNmodelfori-thpath
Costcalculation
Anypathleft?
SetcostINFINITY
yes
Choosethepathwithminimalcost
Assignproperspectrumandmodulationformatbasedonfirstfit
Assignmentsuccessful?
MinimalcostisINFINITY?
TemporallyacceptservicesNo
No
yes Verificationandreconfiguration
Blockingexistingservices?
DoesReplanningblockotherservices?
Service
con
figurationalgorithm
Serviceconfigurationalgorithm
Reconfigurationfail
Blockrequest
Reconfigurationtemporallysuccess?
Permanentlyacceptrequest
Updateresourcespool&loadingstates
No
No
No
yes
yesyes
High Performance Networks Group
AssumptionandNetworkModel
• Transparentdual-polarizationopticalsystemusingcoherentdetectionwithoutinlinecompensation.
• RectangleNyquistspectrumshapeandnoguardband.• Nonlinearityaccumulatesincoherentlyalongspans.• EqualtransmissionPSDamongdifferentchannels.• PowerlossiscompletelycompensatedbyEDFA.• Bandwidthvariableandmodulationformatadaptable
transceiversbeingdeployed.• ThetrafficrequestsincludeFECoverhead.
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High Performance Networks Group
SimulationEnvironment
• 12.5GHzgridopticalsystemdeployedSMF.• NSFNETtopologywith80km/span• 100Gbps requestsandmixedline-ratetrafficrequests(10
Gbps – 400Gbps ).• Pre-FECBERthreshold:4×107( .• Interval time of traffic requests: Poisson distribution• Service holding time: exponentially distributed
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High Performance Networks Group
NARAPerformanceResults
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NARA algorithm achieves between 5% to 15% higher service acceptance ratiothan the benchmark method.
Benchmark: shortest path routing, first fit spectrum allocation and reference margin method for nonlinearity estimation.
High Performance Networks Group
NARAPerformanceResults
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NARA experiences 5-10% less blocking compared to benchmark for 100Gbps traffic request and approximate 5% improvement for mixed line-raterequests.
Service average holding time to be 8000 time units
High Performance Networks Group
NARAPerformanceResults
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NARA is able to achieve:4% to 7% more network spectrum utilization for 100 Gbps requests.Approximate 6% more network spectrum utilization for mixed requests.
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Average Holding Time (units)
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Averageutilization,NARAAverageutilization,Benchmark
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Mixedrequests
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Spectrum utilization after 10000 service demands.
High Performance Networks Group
NARAPerformanceResults
• Atleast4%improvementforallscenarios.
• Smallserviceholdingtime:moreadvantagesforlargetrafficrequests(100Gbps and400Gbps),11%- 13%higher
• Largeserviceholdingtime:moreadvantagesforsmalltrafficrequests(10Gbps and40Gbps),7%- 9%.
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Average holding time10 G requests 40 G requests100 G requests 400 G requests
Lesscongestednetwork
Morecongestednetwork
Mixed traffic request:
High Performance Networks Group
Conclusion
• Hybridnonlinearitymodelissimpleandaccurate.
• TheNARAalgorithmusinghybridnonlinearitymodelsignificantlyimprovesnetworkserviceacceptanceratio.
• NARAusinghybridnonlinearitymodelachieveshigherspectralefficiencyandhighernetworkutilization.
• NARAfavoursdifferenttraffictypesdependingonnetworkcongestionstatus.
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Thankyou.Anyquestion?
High Performance Networks Group
LARASolutionShowcase
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Service N SNR based on HN
Service N SNR based on HN
Link 2 NLI estimation based on HN Link 1 NLI estimation based
on HN
Service N+1,...
ROADM A
ROADM C
ROADM B
Service N (blocked)
After deploying services N+1,...
ROADM A
ROADM C
ROADM B
Service N(16QAM)
Before deploying future services
Service N NLI estimation based on HN
Service N NLI estimation based on HN
Service N+1,...
ROADM A
ROADM C
ROADM B
Reconfigure service N
Service N (QPSK)
End-to-end SNR: 10.5 dB
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Fig. a Fig. b
Fig. c
High Performance Networks Group
Contribution
• Wedevelopastep-wiseload-awarenonlinearitymodel.• Moreaccuratethantheworst-case/referencemarginscenario.
• Computational
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