Nonlinearity Estimation for Efficient
Resource Allocation in Elastic Optical
Networks
RuiWang,Sarvesh Bidkar,RezaNejabati andDimitraSimeonidou
HighPerformanceNetworksGroup,UniversityofBristol
High Performance Networks Group
Outline
• Motivation
• Currentsolutionofnonlinearityanalysis
• Proposedhybridnonlinearitymodel
• Nonlinearityawareresourceallocation
algorithm
• Results
• Conclusion
High Performance Networks Group
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Motivation
• Opticallinkintroducepenaltieswhichaffectconnections
quality.
ROADM
Capacity
Reach
Distance
ROADM
BER
Transponder
Transponder
Optical Link
Easy to compensate
Linear Impairments: Nonlinear Impairments:
Attenuation
CD
PMD
PDL
Crosstalk
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SPM
XPM
FWM
SBS
SRS
Difficult to
mitigate
3
ExistingMethodtoUseNonlinearityInformation
Worst-case or known as reference margin (RM) method
16.2
Cons: Sacrifice network
performance when the
network not in worst case.
16
15.8
Channel SNR
based on real
nonlinearity
15.6
15.4
SNR
Pros: Easy to
implement/calculate.
Does not require
complex model or
expensive nonlinear
impairments monitoring
techniques.
16 QAM SNR
Requirement
15.2
15
Channel SNR based on
worst case
14.8
14.6
14.4
0
10
20
30
40
50
60
70
80
90
100
Channel Index
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ExistingMethodtoUseNonlinearityInformation
Accurate nonlinearity information
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.
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§
Existing channel
New channel
16QAM
QAMSNR
SNR
16
Requirement
Requirement
5
EffectsofTwoNonlinearityAnalysisMethods
Worst-case/reference margin method has much better performance in terms of
blocking ratio.
This is due to inter-channel blocking problem.
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HybridNonlinearityModel
• Hybridnonlinearitymodel
– Step-wisemarginbasedonassignedspectrumindexandlinkoccupancy
condition.
– 5loadingstatesofcontinuouschanneloccupancywithinanopticallinkas
20%,40%,60%,80%or100%occupiedassumedinourwork.
– Nonlinearityarecalculatedbasedonabove5loadingstatesinadvanced.
– Nointer-channelblockingforaddingnewlightpath whenthelinkremains
withinsameloadingstate.
( ∑
.
Thus𝑃"#$ = 𝑃&'
𝛽
+,-,. +,. :nonlinearcoefficientoflink𝑙,frequencyindex𝑚 andloadingstate𝑛
𝛽+,-
𝑃&' :lauch powerofonefrequencyslot
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HybridNonlinearityModelPerformance
• SequentialloadedEON:
• Upto130more100Grequestsusingcongestion-awarerouting
• Upto100moremixedtrafficrequestsincongestion-awarerouting
• Stillbetterperformanceundertwotrafficmodelusingshortestpath
routing.
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HybridNonlinearityModelPerformance
• Usinghybridnonlinearitymodeltendstoutilizemorehigh
modulationformatsthanconservativereferencemargin
(RM)method.
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NARAAlgorithmFlowchart
Newrequest
Yes
No
Temporallyacceptservices
No
Blockingexistingservices?
yes
yes
Calculateend-to-endSNRbasedon
yes
HNmodelfori-thpath
Assignproperspectrumand
modulationformatbasedonfirstfit
No
Assignmentsuccessful?
Serviceconfigurationalgorithm
No
Reconfiguration
temporallysuccess?
yes
yes
yes
DoesReplanningblock
otherservices?
No
Costcalculation
SetcostINFINITY
Choosethepathwithminimalcost
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No
Reconfigurationfail
Permanentlyacceptrequest
Blockrequest
Updateresourcespool&
loadingstates
Verificationandreconfiguration
Serviceconfigurationalgorithm
FindKleastcongestedpaths
Anypathleft?
MinimalcostisINFINITY?
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AssumptionandNetworkModel
• Transparentdual-polarizationopticalsystemusingcoherent
detectionwithoutinlinecompensation.
• RectangleNyquistspectrumshapeandnoguardband.
• Nonlinearityaccumulatesincoherentlyalongspans.
• EqualtransmissionPSDamongdifferentchannels.
• PowerlossiscompletelycompensatedbyEDFA.
• Bandwidthvariableandmodulationformatadaptable
transceiversbeingdeployed.
• ThetrafficrequestsincludeFECoverhead.
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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
1
1520
2
12
880
3520
2160
1440
4
880
1040
2640
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1120
1840
3
11
2960
6
7
1040
1200
8
9
1280
2480
1520
1040
560
560
640
14
13
400
10
12
NARAPerformanceResults
Benchmark:
shortest path
routing, first fit
spectrum allocation
and reference
margin method for
nonlinearity
estimation.
NARA algorithm achieves between 5% to 15% higher service acceptance ratio
than the benchmark method.
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NARAPerformanceResults
Service average holding time to be 8000 time units
NARA experiences 5-10% less blocking compared to benchmark for 100
Gbps traffic request and approximate 5% improvement for mixed line-rate
requests.
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NARAPerformanceResults
0,9
0,85
0,8
0,75
0,7
0,65
0,6
0,55
0,5
Averageutilization,
NARA
Averageutilization,
Benchmark
2000 3000 4000 5000 6000 7000 8000
Average Holding Time (units)
0,95
Network Spectrum Utilization
Network Spectrum Utilization
0,95
100Gbpsrequests
Mixedrequests
0,9
0,85
0,8
0,75
0,7
0,65
Averageutilization,
NARA
Averageutilization,
Benchmark
0,6
0,55
0,5
2000 3000 4000 5000 6000 7000 8000
Average Holding Time (units)
Spectrum utilization after 10000 service demands.
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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NARAPerformanceResults
Lesscongested
network
Mixed traffic request:
•
•
Atleast4%improvementfor
allscenarios.
Smallserviceholdingtime:
moreadvantagesforlarge
trafficrequests(100Gbps and
400Gbps),11%- 13%higher
Largeserviceholdingtime:
moreadvantagesforsmall
trafficrequests(10Gbps and
40Gbps),7%- 9%.
14,00
Improvement in number of
accepted requests (%)
•
Morecongested
network
12,00
10,00
8,00
6,00
4,00
2,00
0,00
2000
3000
5000
6000
7000
Average holding time
10 G requests
100 G requests
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4000
8000
40 G requests
400 G requests
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Conclusion
• Hybridnonlinearitymodelissimpleandaccurate.
• TheNARAalgorithmusinghybridnonlinearitymodel
significantlyimprovesnetworkserviceacceptanceratio.
• NARAusinghybridnonlinearitymodelachieveshigher
spectralefficiencyandhighernetworkutilization.
• NARAfavoursdifferenttraffictypesdependingonnetwork
congestionstatus.
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Thankyou.
Anyquestion?
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LARASolutionShowcase
Before deploying future services
After deploying services N+1,...
Fig. a
Fig. b
ROADM C
ROADM C
Service N
(blocked)
Service N(16QAM)
ROADM A
18 spans
ROADM B
Service N+1,...
18 spans
ROADM A
Service N SNR
based on HN
Service N NLI estimation based
on HN
ROADM B
Service N SNR
based on HN
Service N NLI estimation
based on HN
Reconfigure service N
Fig. c
End-to-end SNR: 10.5 dB
Service N (QPSK)
Link 2 NLI estimation based
on HN
Link 1 NLI estimation based
on HN
ROADM C
Service N+1,...
ROADM A
High Performance Networks Group
ROADM B
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Contribution
• Wedevelopastep-wiseload-awarenonlinearitymodel.
• Moreaccuratethantheworst-case/referencemargin
scenario.
• Computational
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