Machine Learning Assisted QoT Estimation and Planning with … · 2019. 5. 23. · 100 32 DP-QPSK...

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Machine Learning Assisted QoT Estimation

and Planning with Low Margins

Kostas Christodoulopoulos1, I. Sartzetakis2, P. Soumplis2, and E. Varvarigos2

1Nokia Bell Labs, Stuttgart, Germany

2 School of Electrical and Computer Engineering, National Technical University of Athens,

Greece

2019

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Outline

• Motivation

• QoT estimation and margins

• Accurate QoT estimation using ML

– Accuracy evaluation

• Provisioning with accurate QoT estimation

– Incremental multiperiod planning

– Case study

2019

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Motivation

Optical networks are planned to be operated statically

• Provision lightpaths, by estimating QoT at EOL (10 years)

• Ageing, increased interference, inaccuracies – EOL Margins

High margins lead to overprovisioning / high CAPEX &OPEX

• Other overprovisioning factors: inefficient failure handling,

accounting for future traffic demands

Static network operation & overprovisioning will not work as

traffic becomes more volatile – 5G, Edge Cloud

Call for increased efficiency, lower overprovisioning, reduced

margins

2019

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Evolution of margins over time

• Define acceptable performance after accounting for fast penalties (~1 dB), operator’s margin (~1 dB), uncertainties (~2 dB), unallocated (transponder-reach mismatch)

• Traditional approach: target to be acceptable at EoL

• Reduced margins: target to be acceptable at intermediate periods (while we also reduce uncertainties)

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QoT estimation

• QoT estimation is used by a planning or online algorithm

• QoT estimator (Qtool): a Physical Layer Model (PLM)

• Modelling inaccuracy

– Inputs (databases)

• Physical layer parameters: spans, fibers, amplifier parameters, etc.

– Uncertainty: measuring errors, outdated measurements

• Connections parameters: route, spectrum, baudrate, modulation, etc.

– Output: lightpaths’ QoT (SNR, BER, etc.)

• Design margin: account for inaccuracies (model and input parameters)

• System margin: account for QoT deterioration until EOL (equipment

ageing, increased interference, fiber cut reparation, etc.)

Provisioning algorithm

(placement of equipment and configurations, routing

and wavelength assignment, etc.)

Design margin

- PLM model inaccuracy - Physical layer

parameters uncertainty

System marginto reach EOL- Interference

- Ageing

Physical layer

parameters

Established connections

QtoolPhysical Layer Model

(PLM)

Demands

2019

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QoT estimation with Machine Learning

• Assume established connections / brownfield deployment

• Use monitoring and machine learning (ML)

– Understand actual network conditions

• Reduce design margin: improve accuracy of physical layer parameters

• Reduce system margins: no need to target EOL

• Monitoring data

– Power monitors

– Rx (e.g. dispersion, SNR, BER), focus on SNR (used by developed models)

but a Rx gives aggregated information →account for routes & spectrum

• Lightpaths sharing common links share information

• Lightpaths relative spectrum position includes information

• Target per link and wavelength/interference QoT parameters

Lightpaths crossing the same

link share information

2019

Physical layer

parameters

ML train


(PLM)

Design margin



System marginsto reach EOL- Interference

- Ageing


Accuracy improved

Reduced(no need to

target EOL)Reduced

Monitored data

Monitoring

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1st method: Machine Learning - Physical Layer Model (ML-PLM)

• Physical Layer Model (PLM)

– Inputs

• Connection parameters 𝑃

routes, spectrum, TRx configuration (baudrate, modulation, etc.)

• Physical layer parameters 𝑏

spans, fiber attenuation, dispersion, nonlinear coefficients, amplifiers

parameters, ROADM parameters

– Output: lightpaths’ QoT (SNR) estimates 𝑄 𝑏, 𝑃

• Parameters 𝑏 not accurately known, yield QoT estimation error

• Train PLM using monitoring 𝑌 𝑃 → ML-PLM

2019

ML train


(PLM)

Physical layer

parameters


𝑄 𝑏, 𝑃

𝑏

𝑃

Monitored data

Monitoring

𝑌 𝑃

[1] E. Seve, J. Pesic, C. Delezoide, S. Bigo, and Y. Pointurier, “Learning Process for Reducing Uncertainties on Network Parameters and Design Margins”, JOCN 2018

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1st method: Machine Learning - Physical Layer Model (ML-PLM)

• ML training

– Initialize physical layer parameters b0

datasheets or (outdated) measurements

– Fit (iteratively) parameters 𝑏𝑖 to min the error 𝑌 𝑃 − 𝑄 𝑏𝑖 , 𝑃

• Fitting algo depends on the PLM model, and if we know ∂Q/∂bj

• If Q is nonlinear to some bj→ nonlinear fitting

– Obtain fitted physical layer parameters 𝑏∗

• For a new connection, 𝑤 (𝑃′ = {𝑃U 𝑤}), use learned parameters 𝑏∗

to estimate 𝑄(𝑏∗, 𝑃′), when deciding how to establish it

• Implementation: Qtool = GN model, fitting = nonlinear regression (Levenberg-Marquardt nonlinear least squares algorithm)

2019

ML train


(PLM)

Physical layer

parameters


𝑄 𝑏, 𝑃

𝑏

𝑃

Monitored data

Monitoring

𝑌 𝑃

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2nd method: Machine Learning Model (ML-M)

• Without a Qtool

• ML-M: Machine Learning Model, functioning as Qtool

– Input

• Features 𝑋 = 𝑓 𝑃 , 𝑋 : matrix with one row per lightpath

For a lightpath its row - features represent QoT-related parameters

• ML Model coefficients 𝛩

depend on the particular ML model (linear regression, NN, SVM, etc.)

– Output: lightpaths’ QoT estimates෩𝑌(𝑋, 𝛩)

• Use monitoring 𝑌(𝑃) to train the model and obtain 𝛩∗ that yield low estimation error

ML train

ML-MQoT estimator

ML-M coefficients


Features extraction 𝑋 = 𝑓 𝑃

𝑋 ෩𝑌(𝑋, 𝛩)

𝛩

𝑃

2019

Monitored data

Monitoring

𝑌 𝑃

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• Choosing appropriate features

– Literature: end-to-end features (e.g. path length, #hops, #EDFAs)

• Cannot cope with network heterogeneity

→ Per link features

• Features matrix with link features: X=[B A S W]|P|x(1+3|L|)

Bias+3 sets of link features for the 3 major impairment classes

– A: ASE, S: SCI, X: XCI

e.g. Sp,i=PSDp3 (power spectral density) of lightpath p if it uses link i, else=0

SCI noise contribution depends (linearly) on lightpath’s PSD3

ML train

ML-MQoT estimator

ML-M coefficients



𝛩

BBias

Ap,l =1 Sp,l = 𝑃𝑆𝐷𝑝

3

𝑊𝑝, 𝑙 =

𝑝′𝑃𝑆𝐷𝑝 ∙ 𝑃𝑆𝐷𝑝′

2 ∙

{𝑎𝑠𝑖ℎ𝑛 𝑑𝑝,𝑝′ + 𝐵𝑝′/2 ∙ 𝐵𝑝 -

𝑎𝑠𝑖ℎ𝑛 𝑑𝑝,𝑝′ − 𝐵𝑝′/2∙ 𝐵𝑝 }

2019


𝑃Monitored

data

Monitoring

𝑌 𝑃

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• Features designed so that the impairment noise contribution

depends (close to) linearly on them

• 𝑋𝑝,𝑗 the jth impairment/link feature of lightpath 𝑝, the noise contribution

of impairment on that link is approximated well with 𝑛𝑗 𝑋𝑝,𝑗 , 𝛩 = 𝑋𝑝,𝑗 ∙ 𝛩𝑗

• Noise additivity assumption

– The total noise of lightpath 𝑝 is σ𝑗 𝑛𝑗(𝑋𝑝,𝑗 , 𝛩)

• ML-Model: linear regression ෩𝑌 𝑋, 𝛩 = 𝑋 ∙ 𝛩, gradient decent

• Also tried NN and SVM, and obtained similar results

• NN, SVM would account better for nonlinear dependency of

features and other more complicated features

2019

ML train

ML-M QoT estimator

ML-M coefficients



𝛩

BBias

Ap,l =1 Sp,l = 𝑃𝑆𝐷𝑝

3

𝑊𝑝, 𝑙 =

𝑝′𝑃𝑆𝐷𝑝 ∙ 𝑃𝑆𝐷𝑝′

2 ∙

{𝑎𝑠𝑖ℎ𝑛 𝑑𝑝,𝑝′ + 𝐵𝑝′/2 ∙ 𝐵𝑝 -

𝑎𝑠𝑖ℎ𝑛 𝑑𝑝,𝑝′ − 𝐵𝑝′/2∙ 𝐵𝑝 }


𝑃Monitored

data

Monitoring

𝑌 𝑃

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ML QoT Estimation – accuracy evaluation

• Ground truth (create monitoring data and obtain estimation error): GN model

• 12 nodes Deutsche Telecom topology

• Physical layer parameters

– Span parameters (length, fiber coefficients): ±0%, 10%, 20% around default values

– Actual parameters assumed unknown → uncertainty: 0%, 10% and 20%

• Traffic

– 4 traffic loads 100, 200, 300, 400 connections, 80% training, 20% testing

– Uniform source-destination, uniform baudrate: 32, 43, 56 Gbaud

– 500 instances per load

• ML-PLM, ML-M

2019

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Mean Square Error

• Both ML-PLM and ML-M achieve excellent MSE

• ML-PLM is better (note: the ground truth and the trained PLM are the same)

• ML-PLM’s error is higher for higher uncertainty

– Starts from default / average parameters and learns

• ML-M’s accuracy is not sensitive to uncertainty since it does not assume any default parameters

2019

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

100 200 300 400

MSE

SN

R (in

dB)

Number of lightpaths

ML-PLM (0% uncertainty)



ML-M (0% uncertainty)



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Maximum Overestimation

• Similar findings for max overestimation

• Design margin = max overestimation– SNRover= SNRest - SNRreal , for threshold SNRthr , it is safe if SNRreal> SNRthr➔ SNRest - SNRover > SNRthr

• ML-PLM design margin: 0.05 dB, ML-M design margin: 0.2 dB for 200 lightpaths

2019

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

100 200 300 400

Max

ove

rest

imat

ion

SNR

(in d

B)

Number of lightpaths







(untrained) PLM max overestimation 0dB for 0% uncertainty0.9dB for 10% uncertainty2dB for 20% uncertainty

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Quantifying savings of accurate QoT estimation

• Multi-period/incremental planning (period=several months to years)

1. Traditional: provision with high margins to reach end-of-life (EOL) and account for inaccuracies

• System margin: equipment ageing, interference increases, maintenance operations

• Design margin: QoT estimation model inaccuracy

2. With reduced margins / accurate QoT estimation

• New connections: provision them with enough margins to reach next (or some targeted) period

• Established connections: check their QoT and reconfigure or add regenerators to reach next (or targeted) period

2019

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Incremental planning algorithm

• Input at the start of period τi

– Traffic described by the remaining and new demands

– TRx installed at previous periods / established lightpaths (up to τi)

– Equipment e.g. capabilities of Flex- (or fixed-) rate TRx

• Interface with QoT estimator

• Objective

– Serve traffic

• Cater for remaining lightpaths that run out of margins, serve new demands

– Minimize added cost

• Algorithm: heuristic, examines previous and new connections 1-by-1 [1][2]

[1] P. Soumplis, K. Christodoulopoulos, M. Quagliotti, A. Pagano, E. Varvarigos, "Network Planning with Actual Margins“, Journal of Lightwave Technology (JLT), 2017

[2] P. Soumplis, K. Christodoulopoulos, M. Quagliotti, A. Pagano, E. Varvarigos, "Multi-Period Planning With Actual Physical and Traffic Conditions", Journal of Optical Communications & Networking (JOCN), 2018





New demands

QtoolPhysical Layer Model (PLM)

Available equipment

2019

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Incremental Planning and ML-PLM





Design margin



System marginsto reach EOL- Interference

- Ageing

Physical layer

parameters

Initial demands



and wavelength assignment, etc.)Established

connections

Provisionconnections

ML train

Monitoring


Design margin



system marginto reach EOL- Interference

- Ageing

Physical layer

parameters

Accuracy improved

Reduced(to reach next

period)Reduced

Provisionconnections

Reduced(to reach next

period)

Greenfield Brownfield / Incremental planning

Available equipment

New demands

2019

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Case study – Topology, traffic, TRx

• DT network topology

• 11 periods, 1 period ≈ 1 year, incremental planning every 1 year

• Initial traffic: 200 initial connections, uniform src-dst, uniform [100-200] Gbps

• Traffic increases by 20% per period

• 2 types of TRx: TRx1 available at period 0 (τ0), TRx2 available at period 5 (τ5)

– TRx1: 32 Gbaud, DP-QPSK to DP-16QAM, SNRthr=0.01dB, cost= 1, at period 0 (τ0)

– TRx2: 64 Gbaud, DP-QPSK to DP-32QAM, SNRthr=0.01dB, cost= 1, at period 5 (τ5)

• Cost reduction 10% per period

Data

Rate

(Gbps)

Baud

Rate

(Gbaud)

Mod

Format

BOL ageing

& BOL

interf. &

High design

EOL ageing &

EOL interf. &

Low design

EOL ageing &

EOL interf. &

High design

100 32 DP-QPSK 4720 3600 2280

150 32 DP-8QAM 2080 1600 1280

200 32 DP-16QAM 1040 800 560

Data

Rate

(Gbps)

Baud

Rate

(Gbaud)

Mod Format

BOL ageing &

BOL interf. &

High design

EOL ageing &

EOL interf. &

Low design

EOL ageing &

EOL interf. &

High design

200 64 DP-QPSK 4160 2800 2240

300 64 DP-8QAM 2720 1840 1440

400 64 DP-16QAM 1840 1280 960

500 64 DP-16QAM 1280 880 640

2019

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Case study – Physical layer evolution & margins

• Initialize with heterogeneous spans and uncertainty

– Attenuation, dispersion, nonlinear coefficients

uniformly around default values ±10%

– Unknown to QoT estimator, requires ~1 dB margin

• Ageing: increase per period according to table

• 10 instances (load & physical layer), average results

• Planning with high margins

– EOL system margin (EOL ageing & full load interference), BOL design margin (2 dB)

• Planning with reduced margins - ML-M (or ML-PLM) and incremental planning algorithm

• Initial period: design = 2 dB, system = 2 periods ageing & current interference

• Each period, train ML-M and obtain new design (=1dB+0.2dB+training max overest.), system = 2

periods ageing & current interference

2019

Physical layer parameters evolutionIncrease per

period

syst

em m

argi

n

Age

ing

Transponder margin (dB) 0.05

Attenuation (dB/km) 0.0015EDFA noise figure (dB) 0.1OXC loss (dB) 0.3

Interference According to load

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Case study - basic comparison

• The reduction of the system margin postpones the purchase of equipment

• The reduction of the design margin (ML – learning) avoids the purchase, after the first period

• ~20% savings at the end of 10 periods

– Could be even higher if we optimize the power

2019

0

0.05

0.1

0.15

0.2

0.25

0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9 10

savi

ngs

TRx

Co

st

Period

EOL planning

Reduced margins planning

savings

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6 7 8 9 10

Des

ign

mar

gin

(d

B)

Periods

used design margin(1dB+0.2dB+training max overest)

max estimation error (afterestablishing) +1dB

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Conclusions

• Traditionally lightpaths are provisioned using a QoT estimator (PLM) and EOL margins

• Developed 2 ML QoT estimators (with a PLM and without)

– Use monitoring data, understand physical conditions and ageing, reduce system margins

– Very good accuracy, design margin reduced to 0.2 dB with few 100s lightpaths

• Quantified savings of accurate QoT estimation

– Integrated ML-M with incremental planning algorithm

– Multiperiod planning case study

~20% savings with accurate QoT estimation/planning with reduced margins as opposed to EOL margins

2019

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2019

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QoT estimation – state of the art

Internal

[1] I. Sartzetakis, K. Christodoulopoulos, C. Tsekrekos, D. Syvridis, E. Varvarigos, "Quality of transmission estimation in WDM and elastic optical networks accounting for space–spectrum dependencies", JOCN 2016

[2] C. Rottondi, L. Barletta, A. Giusti, M. Tornatore, “Machine learning method for quality of transmission prediction of unestalished lightpaths”, JOCN 2018

[3] E. Seve et. al., “Learning Process for Reducing Uncertainties on Network Parameters and Design Margins,” JOCN, 2018.

[4] M. Bouda, et. al. “Accurate Prediction of Quality of Transmission Based on a Dynamically Configurable Optical Impairment Model”, Journal of Optical Communications and Networking, 2018. (Fujitsu)

[5] P. Samadi et. al., “Quality of Transmission Prediction with Machine Learning for Dynamic Operation of Optical WDM Networks,” ECOC 2017. (Bergman)

[6] G Choudhury, et. al., ”Two use cases of machine learning for SDN-Enabled IP/Optical Networks: traffic matrix prediction and optical path perfomrnace prediction”, JOCN 2018

Paper Qtool ML method Features Simulations /Experiments Comment

[1] without Qtool RegressionKriging (linear correlation)

Interference aware links(1/SNR per link, different links according to adjacent lightpaths)

Simulations, GN model as ‘ground truth’ Homogeneous’ spans

[2] without Qtool ClassificationK-nearest neighbors, Random Forest

#hops, path length, longest link, modulation format, network traffic volume, …

Simulations, GN model with worst case interference as ‘ground truth’ Worst case interference‘Homogeneous’ spans

[3] with Qtool (inhouse, GN model) RegressionGradient decent

Input parameters of Qtool (power at nodes, XXX) Simulations, GN model and inhouse Qtool as ‘ground truth’, worst case interference

Worst case interreference

[4] with Qtool (inhouse close to GN) Calculate the derivatives, similar to gradient decent

length-dependent loss and nonlinearintensity (NLI) noise based on the GN model [15], computing in each fiber span the SPM-like and XPM-like noise contributions due to nonlinear effects in fiber based on frequency spacing between optical signals, their signalpower levels, and the fiber nonlinear coefficient [2]. In this work we used only a single-mode fiber (SMF).

Experiments, 6 nodes !

[5] With Qtool (GN model) RegressionMaximum likehood / extended Kalman filter

Not clearly described, for sure SNR and wavelength Experiments (6 nodes, VOAs to emulate different link lengths), and simulations (to evaluate benefits)

Account for non-homogeneous amplification

[6] Without Qtool Ridge regressionLASSO regressionLASSO with quadratic featuresMultilayer perceptronGaussian process regressionGradient boosted regression treesRandom forest regression trees

26 input features for each wavelength or data sample.These features include data rate, fiber type, frequency, length of path, margin, measured fiber loss, measurement date, number of amplifiers in the path, number of passthrough ROADMs, optical return loss (ORL), end-of-path optical signal-to-noise ratio (OSNR), and polarization mode dispersion (PMD).We estimate the OSNR of each fiber section based on launch power, amplifier noise, and measuredspan loss.We then combine these fiber section estimates to estimate the end-to-end path OSNR. In cases where regeneration is needed, we treat the sections between regeneration points as separate wavelengths.

Experiments

2019

Machine Learning Assisted QoT Estimation and Planning with … · 2019. 5. 23. · 100 32 DP-QPSK...

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