turning elastic metro optical networks into reality...elastic transponders (trx) reconfigurable set...

1 © Nokia 2018

Turning elastic metro optical networks into reality

Séminaire Telecom ParisTech 2019

• Patricia Layec, team

• 11-01-2019

Public

2 © Nokia 2018

1. Introduction

2. Today’s elastic optical networks

3. Towards true elastic networks

4. What needs to be replaced

5. What needs to be added

6. Conclusions

Public

Agenda

3 © Nokia 2018 Public

Introduction

Market segments

Picture from description of work of H2020 METRO-HAUL project

Access ~50km

Metro network ~100-500km

Core network ~500-10000km

~10Gb/s ~10-100Gb/s ~100-400Gb/s

4 © Nokia 2018

• Metro segment is growing 2x faster than core segment

• Key drivers for this growth

Metro video traffic will increase by 720% by 2017

Metro cloud and DC traffic will increase by 440% by 2017

- DC traffic includes DC-to-end-user & DC-to-DC (i.e. DCI) traffic

- Growth rate of DCI is 100% per year since 2012

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Introduction

Traffic is growing fast

[Bell Labs Consulting, “Metro network traffic growth: an architecture impact study,” Dec. 2013]

[A. Chen (Alibaba), W3B4, OFC 2016]

0

20

40

60

80

100

120

Tra

ffic

(T

b/s

)

metro backbone

2012 2013 2014 2015 2016 2017

5.6x

3.2x

560% INCREASE IN TOTAL METRO TRAFFIC

METRO TRAFFIC GROWS ALMOST

2X FASTER

5 © Nokia 2018

• Service providers are deploying video caching and cloud computing platforms within metro networks

- Better performance

- Better user experience

more traffic terminated in metro and not going to the core networks

Public

Introduction

Traffic distribution is changing

[Bell Labs Consulting, “Metro network traffic growth: an architecture impact study,” Dec. 2013]

Total IP traffic

IP traffic terminates in

metro network

57%

2012

Total IP traffic

IP traffic terminates in metro network

75%

2017

6 © Nokia 2018

• Traffic patterns are becoming meshed and variable

3-fold heterogeneity:

- connection lengths (and impairments),

- bandwidth requests on demand (capacity market)

- Smaller duration of connections (from months to minutes)

• Reach << 1000km

- Less DSP constraints

• Metro networks are also influenced by over-the-top (vs. telco)

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Introduction

Mismatch between dynamics of new services and optical transport

Most innovative solutions should appear in the metro networks

Metro Core

Meshed & variable traffic

patterns

7 © Nokia 2018

• Traffic patterns are becoming meshed and variable

3-fold heterogeneity:

- connection lengths (and impairments),

- bandwidth requests on demand (capacity market)

- Smaller duration of connections (from months to minutes)

• Reach << 1000km

- Less DSP constraints

• Metro networks are also influenced by over-the-top (vs. telco)

Public

Introduction

Mismatch between dynamics of new services and optical transport

Metro Core

Meshed & variable traffic

patterns

Most innovative solutions should appear in the metro networks

Try and learn Continuously adjust Always zero margins

Working with just-enough performance

Design and optimize Set and forget (for 15years) Guaranteed (large) margins Seeking top performance (bit-rate/distance)

OTTs Telcos

8 © Nokia 2018

• Elastic optical networks introduced in 2008 [M. Jinno et al, Th3F6, ECOC’08]

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Today’s elastic optical networks

Finisar

From BPSK to 16QAM 40G, 100G, 200G

Cisco

Multirate linecard BPSK, QPSK, 16QAM

50G, 100G, 200G

But in 1 touch can be upgraded to 200G.

Nokia

QPSK, 16QAM 100G, 200G

Infinera

QPSK N x 100G

superchannel

Reconfigurable transponder but in set & forget configuration

9 © Nokia 2018

Universal transponder: 1 does it all !

• Single hardware: single development cost, fast price erosion

• Similar technology for all rates: jointly optimized link design, limited XPM degradation

good side effects on engineering margin and planning tools

• Limit spares: single hardware device whatever the selected data rate [A. Morea et al, Th10F5, ECOC’10]

• Tunable devices: support easily traffic increase and network upgrades [O. Rival et al, P5.12, ECOC’12]

no need to uninstall and replace low rate devices with higher ones

Confidential

Today’s elastic optical networks

Set & forget configuration

Sp

ares

TS

P R

1

TS

P R

2

TS

P R

2

TS

P R

m

TS

P

TS

P

TSP

100Gbps 200Gbps

400Gbps

10 © Nokia 2018

Most popular usage is trade-off between capacity and optical reach

Fine granularity of flexrate technologies

• Set partitioning [J. Renaudier et al, We1C5, ECOC’12]

• Time hybrid QAM [X. Zhou et al, IEEE Com Mag., 2013]

• Probabilistic shaping [F. Buchali, et al., arXiv:1509.08836, 2015]

• FEC with puncturing

Today’s elastic optical networks

700 km 6000 km

+25% +50%

Fixed modulation, fixed power Adapted modulation, optimized power

[D.J. Ives et al., PNC, 29 (3), 2015]

t

Polar. X

8QAM

QPSK 8QAM QPSK

QPSK 8 QAM

Polar. Y

QPSK

8QAM

X Y

11 © Nokia 2018

PDM-QPSK optical spectrum

• Current networks mostly use a 50GHz fixed-grid

• Elastic networks enable flexgrid thanks to:

- Flexible channel spacing

- Tunable symbol rate of the transponder

=> Irregular grid with variable bandwidth of channels

- Techno-economics show a 20% spectrum savings with uniform traffic [A. Morea et al., OFC’11, JWA62]

Public

Today’s elastic optical networks

Spectrum

savings

60Gb/s in 100G l

75Gb/s in 100G l

50Gb/s in 100G l

50GHz

50 ps/div

FPGA electrical output

[A. Dupas et al., OFC’15, M3A2] Commercial WSS and ROADM have now very narrow granularity

12 © Nokia 2018 Public

Towards true elastic networks

ELASTIC TRANSPONDERS (TRx)

Reconfigurable

Set & forget configuration [Y. Zhou, et al. “1.4 Tb Real-Time Alien Superchannel Transport Demonstration Over 410 km Installed Fiber Link Using Software Reconfigurable DP-16 QAM/QPSK,” in JLT 2015]

Measured switching time (field trial with commercial hardware)

QPSK 16QAM : 40 sec 16QAM QPSK : 20 sec

TSP

100Gbps 200Gbps

400Gbps

13 © Nokia 2018 Public

Towards true elastic networks

ELASTIC TRANSPONDERS (TRx)

Reconfigurable

Set & forget configuration

SELF-EVERYTHING ELASTIC OPTICAL NETWORKS

Fast reconfigurable

Automated

Easy to use & configure

TSP

100Gbps 200Gbps

400Gbps

14 © Nokia 2018

• Easy to use and configure

- To trigger intents rather than specific hardware configuration => Tell what you need, not how to do

- e.g. low latency, 200G bandwidth, secure end-to-end connection, …

• Boulder project at Open Networking Foundation (ONF) is the first that really started working on the specifications and design of intent-based

- Open-source projects are developing intent-based networking

Public

Towards true elastic networks

NFV / Applications

Intent-based mediation

SDN controller

Data plane

vQOT vRSA vBoD …

Open day light

15 © Nokia 2018 Public

Towards true elastic networks

Perfect future network

M. Weldon et al., “The Future X Network: A Bell Labs Perspective,” ISBN 978-1498759267, CRC Press, 2015.

16 © Nokia 2018

• Highly scalable, tunable network fabric including:

- Disaggregated SDN network – to decouple the HW and SW that different lifecycle

- Adaptive impairment-aware optimization – to leverage monitoring and machine learning to optimize and automate the network

- Elastic optical metro transport – to allow specific innovation into the metro segment (i.e. low cost, low energy) while today innovation is driven by the core segment (i.e. performance)

- Ultra-scalable routers

• Network OS that fully automates and exposes the tunable network fabric

Confidential

Towards true elastic networks

Perfect future network

17 © Nokia 2018

KEY BENEFITS

• Assumptions:

- 10x traffic increase in 5 years

- Perfect future network

• TCO savings:

- OPEX & CAPEX for Optical (15%)

- OPEX & CAPEX for IP (43%)

And…

- Automation & optimization (42%)

Confidential

Towards true elastic networks

M. Weldon et al., “The Future X Network: A Bell Labs Perspective,” ISBN 978-1498759267, CRC Press, 2015.

IP/Optical cost/Gb

10x traffic

-71%

SDN automation / optimization (42%)

High scalable IP (43%)

High rate WDM (15%)

18 © Nokia 2018

• Data plane is already in good way

- CDC-F ROADMs

- Flexible elastic transponder (data rate, spectrum, tunable laser, etc.)

Commercial products (planned to be) deployed on field

Hardware foundations are ready for perfect future network

• Optical management tools

- Technology-specific

- Backward-compatible to previous generation of hardware

- SDN should become a carrier-grade system with resilient and secure functionalities

• Manual intervention

- Cabling between router ports and photonic nodes

To reduce delays of service creation (from weeks to days)

• Network programmability and applications come from upper layers

- Already happening in our day-to-day life

- dynamic bandwidth consumption,

- network as a service

Public

What needs to be replaced

Bottom-up

Top-down

19 © Nokia 2018

• Hitless data rate change for bandwidth on demand on optical transport

• State-of-the-art:

- 2017: commercial Field Trial, 20-35s switching time [JLT 2017, Y. Zhou et al., Vol 35, n°3]

- 2016: 450 µs switching time between 10 different bit rates [OFC’2016, A. Dupas et al., Th3I.1]

• Experimental proof-of-concept on 100Gbps real-time transmitter:

- Zero loss of data during data rate reconfiguration

- Flex symbol rate transponder (10G, 20G, …, 100G)

- a novel hitless protocol that buffers and synchronizes commands with data flows after all framing processing => No additional latency on data flows and service hitless switching.

=> Max switching time ~12µs.

Public

What needs to be added

Hitless data rate change (speed)

A. Dupas et al., “Ultra-fast Hitless 100Gbit/s Real-Time Bandwidth Variable Transmitter with SDN optical control,” Proc. OFC, 2018

To

fib

er

link

SDN Controller

Local controller

OTUflex-like

from 10 to

100Gbps

Hitless

coding

Tunable High

speed

interfaces

Hitless flexrate transmitter

Synchronization at the bit level

10ns/div

FPGA Clock For 160 bits

Command from SDN controller

Switching

10ns/div

20 © Nokia 2018

• Why?

- When crossing optical nodes, filter cascade reduces the available bandwidth => less tolerant to detuning

• Key goal: A self-optimizing transponder leveraging monitoring at the receiver side (no need for calibration)

- To reduce filter penalties by several dBs (worst case)

- To reduce the large filter design margin provisioned

• We propose an algorithm to safely cancel Tx/Rx lasers and filters misalignments

- Tx and Rx lasers are never perfectly aligned (+/-1.5GHz), and are not aligned with the optical filter cascade either (+/- 3GHz)

- Exact misalignments cannot be predicted

- Closed-loop automated algorithm that correct the effect of Tx/Rx frequency fluctuations using analytical metric

- No need for extra equipment (e.g. OSA)

Public

What needs to be added

Monitoring & closed-loop systems: filter mitigation

[O. Bertran Pardo et al., OFC’15]

Tx frequency correction

Tx Rx Detuned filter cascade (WSS)

Control plane

[C. Delezoide et al., OFC’19]

21 © Nokia 2018

• Analytical metric [C. Delezoide et al., OFC’19]

- Step #1: Filter effects are mitigated by the equalizer, hence we need to compute the power spectral density (PSD) after the Analog-to-Digital Converters (ADCs)

- Step #2: Metric based on the center of mass :

where Fs is the ADC sampling frequency and f is the baseband frequency

- Step #3: ensure the Tx laser is aligned with the Rx one. This permits an optimal monitoring of the signal-filter detuning

- Step #4: minimize the metric |M1|, hence filter penalties

• Experimental measurements

- Tx laser frequency known at +/- 1.5GHz and filter slot center at +/-2.5GHz

Public

What needs to be added

Monitoring & closed-loop systems: filter mitigation

22 © Nokia 2018 Public

What needs to be added

Monitoring & machine learning: architectures

Low latency High Cognition

Vendor A

Rx adaptation

Vendor A

Cloud

Program devices

Monitor

SDN controller

Learning & decision making

Vendor A

Program learning

Monitor

SDN controller

Cloud

Rx learning & adaptation

Learning

23 © Nokia 2018

Monitoring & machine learning: examples

What needs to be added

Public

Low latency High Cognition

QoT prediction “A learning living network”

[Oda et al., JLT’17] Vendor A

Program learning

Monitor

SDN controller

Cloud

Rx learning & adaptation

Learning

Vendor A

Cloud

Program devices

Monitor

SDN controller

Learning & decision making

ML at DSP level [Zibar et al., ECOC’15]

24 © Nokia 2018

QoT prediction with learning

Public

• Network margins (as defined in Augé et al., OFC’13):

- Unallocated margins – where transmission reach exceeds the transmission distance

- Design margin – unwanted, worst case QoT prediction (imperfect model + input uncertainties)

- System margins – time-dependent, fixed margins (e.g. polarization effects, ageing, network load)

• Many recent works on QoT prediction with learning

- Oda et al., JLT, vol. 35, no. 8, 2017

- E. Seve et al., OFC’17

- S. Yan et al., ECOC’17 PDP

- M. Bouda et al., JOCN vol.10 (1), 2018

- …

[Seve et al., OFC’17]

25 © Nokia 2018

• E. Seve et al., OFC’17

- QoT model, evaluation of power uncertainty

- Simulations with statistical error on measured power P

Examples

Public

• S. Oda et al., JLT 2017

- Experimental testbed with 6 ROADMs, 88 channels at 100Gbps with 50GHz spacing

- Monitoring BER

No learning

#Demands

SN

R e

rro

r (d

B)

0

-1

-2

1

2

3

QoT prediction with learning

Prediction instead of (worst-case) forecast

26 © Nokia 2018

Monitoring & machine learning: examples

What needs to be added

Public

Proactive fiber break detection

[Boitier et al., ECOC’17]

Low latency High Cognition

QoT prediction “A learning living network”

[Oda et al., JLT’17] Vendor A

Program learning

Monitor

SDN controller

Cloud

Rx learning & adaptation

Learning

ML at DSP level [Zibar et al., ECOC’15]

27 © Nokia 2018

[F. Boitier et al., ECOC’17]

Proactive Fiber Break Detection

Public

• To proactively detect fiber break to use cost-effective optical restoration

- Be as reliable (as 1+1 protection) and low cost (as in shared restoration)

- Use the transmission fiber as a sensor

- Unleash coherent receiver for monitoring

• We develop a real-time proof-of-concept with monitoring and learning

- We compute State-of-Polarizations (SOP) based on existing FIR filters

- We classify events at the DSP level to lower false alarms and re-route traffic in case of a risky event before the cut occurs

28 © Nokia 2018

[F. Boitier et al., ECOC’17]

Proactive Fiber Break Detection

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• Training phase

- Control & management plane

- 16548 events divided into training and test, with 10-fold cross-validation

- 4 different SOP events generated, usage of polarization scrambler for robustness

- We evaluate accuracy with naïve Bayesian classifier for various number of parameters

• Real-time measurements

- Small number of variables required for event classification

=> Easy to embed in real-time boards

- Reduce false alarms and avoid unnecessary re-routing of traffic

Real-time receiver board

Step #1

Calculate SOP

Step #2

Calculate rotation

speed

Step #3

Compare to

threshold

Step #4

Save SOP

during few sec

t

SOP

Alarm

Yes

Event

recognition

!

Embedded CPU in receiver board

29 © Nokia 2018

Real-time proof-of-concept

Proactive Fiber Break Detection

Elastic Tx

Node C Node B

Emulated Rx

Elastic Rx

SDN Controller

Node D

Node A Working path

Restoration path

30 © Nokia 2018 Public

Real-time proof-of-concept

Proactive Fiber Break Detection

31 © Nokia 2018

• Metro is the meeting point between telcos and OTTs with less stringent requirements in long reach / high capacity performance than core networks

Good playground for disruptive research

• Need to change perspective from “set & forget” to “easy to use & (re)-configure”

- Good news is that data plane is already deploying products with high level of flexibility

- Good news: many talks at OFC & ECOC on automation, online optimization

- Need for faster reconfiguration

• Unified control may be more challenging

• Machine learning is a natural evolution to

- Optical monitoring

- Traffic patterns observation

Predicting (deterministic models) instead of forecasting (uncertainty)

Public

Conclusions

Work partly supported by H2020 European project METRO-HAUL

33 © Nokia 2018

• Superchannel optimization example

Public

What needs to be added

Plug and play, monitor and optimize

F. Cugini, et al., “Towards Plug-and-Play Software-defined EONs: Field Trial of Self-Adaptation Carrier Spacing,” in Proc ECOC, 2015

Installation with QoT prediction

subcarrier packing Sequential shift

Monitor BER

Monitor BER => FEC limit reached

WSS reconfiguration

Increase #iterations of FEC decoder

SDN control + supervisory channel

34 © Nokia 2018

• Optical white box [M. De Lenheer, et al., Th1A.7, OFC’16], Architecture on Demand [N. Amaya et al, ICTON’11]

more flexibility to use hardware, open possibilities by software

• Chassis, hence backplane disappears but this has a cost:

- loss of flexibility with manual cabling between hardware elements

- Insertion loss of about 3.5dB due to a fiber switch matrix

• Such losses may be well-accepted in metro networks where the need for high performance is not always mandatory.

• But, back-2-back performance are key in metro networks to increase network capacity [D. Boertjes, Workshop flexible optical networks,ECOC’16]

Public

What needs to be added

Open architecture