seminar at institut mines telecom may 3 “brighter networks: a · 9 © 2018 nokia nokia internal...

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Seminar at Institut Mines TelecomMay 3rd 2018

Sébastien Bigo

“Brighter networks: a glimpse into the future

of optical networks”S. Bigo, Seminar IMT, Paris - All right reserved

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A vision of the future

S. Bigo, Seminar IMT, Paris - All right reserved

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The future of networks is all about time


Timeto transport

The capacitychallenge

Time to queue

The latencychallenge

Time to reconfigure The automation

challenge

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Agenda


The automation challenge

Space, the newfrontier

Q&A

The latency challenge


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Spectrum[Hz]

Space

Spectral Efficiency [b/s/Hz]

≤

Bandwidth (B) beyond erbium limit

Approach non-linear Shannon Limit (SNR)

Expand fiber-space (M)

Smart Network Fabric : Capacity

C ~ B x M x log2(SNR)


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High Baud Rates

High Order Modulation

10011010101

10100011101

10100111101

00101011010

10010100111

01001011001

Advanced Coding

Nyquist Filtering

Smart Network Fabric: Solving for SNR with PCS

Probabilistic Constellation

Shaping

The final piece of the super coherent puzzle


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Building a new langage, easier to decipherBased on existing symbols


https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwjr_8OK47HaAhXJXhQKHYNDDBsQjRx6BAgAEAU&url=https://www.opticiensremplacants.com/difficulte-a-trouver-opticien-remplacant/&psig=AOvVaw34nNw9lRmONVmtrwg6zLAW&ust=1523520712822216

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Discrete QAM formats perform well only at specific distances

Distance

Capacity

Shannon Limit approaching

optimal capacity at any distance

25%Shaping gain

PCS

PCS

PCS

Smart Network Fabric: Solving for SNR with PCS


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Smart Network Fabric: Solving for B

-50

-45

-40

-35

1480 1510 1540 1570 1600 1630 1660

Wavelength (nm)

Po

we

r (d

Bm

)

EDFA

SOA

100nm

C-Band L-BandS-Band

250 channels, each carrying >400 Gb/s, making the first 100-nm continuous band transmission of 115 Tb/s over 100 km (reported at ECOC 2017)

375

400

425

450

475

500

1500 1520 1540 1560 1580 1600

Ch

. n

et

bit r

ate

(G

b/s

)

Wavelength (nm)

C band L bandS band

Total throughput: 115.9 Tb/s115.9Tbit/s


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Smart Network Fabric: Solving for M

Single fiber, more cores Single fiber, more modes


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Smart Network Fabric: 1Pb/s System in 2024 ?

~ 1Tb/s

Each cell = Single mode with maximum spectralefficiency (PCS inside)

6 to

40

sp

atia

l p

ath

s

50 (C) to 100 cells (C + L)

Spectrum(more wavelengths)

Space(more modes, more fibers)X100

Shannon (more PCS)

~ 1Pb/s


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Agenda



Space,the new frontier

Q&A



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MICROSFT cloud networkS. Bigo, Seminar IMT, Paris - All right reserved

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© Nokia 2016

Telecommunicationproviders

Prescribers in optics

Followers in IP/Ethernet

Over the topcontent providers

Prescribers in IP/Ethernet

Followers in optics

Two types of players competing to define and rule the 2025 global cloudIP + Optics

Te

lco

s

OT

Ts

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Two types of players, two competing visionsCreating the future with the best of both

Try and learnContinuously adjustAlways zero marginsWorking with just-enough performance

Design and optimizeSet and forget

Guaranteed (large) marginsSeeking top performance (bit-rate/distance)

Detect and mitigateAutomatic troubleshoot

Seek for maximum quietness(Zzzzz…. $$$$)


Optics today(Telco centric)

Optics tomorrow

Quotes from Najam Ahmed, Facebook, OFC 2016

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Push down demands to the networkProvision and monitorAll programmable, very dynamic network If equipment fails, remove from network,Perform troubleshooting later

Plan resources well in advance (e.g. 15y ahead)

secure them before establishing servicethen allocate demands.

Optics today(Telco centric)

Optics tomorrow

Quotes from Najam Ahmed, Facebook, OFC 2016S. Bigo, Seminar IMT, Paris - All right reserved

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Connection-based approachReserved, even if partially filledGuaranteed performance and jitter-free

All-packet approachFully flexible, can be shared

Bandwidth and latency depend on load

Optics today(OTT centric)

Optics tomorrow


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a network to iterate, not architecta network which fails fast instead of a fail-proof onea network which pushes down demands to the network, not planned 15y aheada network still with guaranteed delivery, but only when needed

Optics tomorrow


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Living networks, accounting for timeBrighter optical networks

..

Flexible TRX

SDNControl

New demand

Resourceallocation

New hardware?

Predict

Measure

Demands (t)Learn from the pastPredict the future

Hardware resources (t)Learn from the pastPredict the future

f(t)+

-

From set-and-forget to try-and-learn

Margin

predictor


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Marginless live network planning with machine learning

Network margins (as rephrased by Augé et al., OFC’13):

Unallocated margins – when the achievable distance 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

Sève et al., OFC’17

Yan et al., ECOC’17 PDP

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

…

[Sève et al., OFC’17]S. Bigo, Seminar IMT, Paris - All right reserved

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• Key goal

Fibers type for rent (especially dark fibers) are sometimes either unknown or mistaken for another type, hence require worst-case design

• Key disruption

Automatically detect fiber type without traffic interruption by monitoring and correlating the accumulated chromatic dispersions extracted from coherent receivers of established lightpaths.


Automated fiber type identification for cost reduction without traffic interruptionGoal and disruption

Reduction of network cost thanks to a better knowledge of type of fiber

Number of regenerators: 5Fiber 1 (SMF): high perf.

Fiber 2 (LEAF): low perf.

Unknown LEAF

Worst case scenario :

Number of demands: 0 With monitoring

Number of regenerators: 3

?

??

Number of demands: 1Number of demands: 2Number of demands: 3Number of demands: 4

R$$$$$R$$$

Unknown fiber type

SDN platform

vFiber

Type vCost Paris

London

Lyon

Strasbourg

Amsterdam

Bonn

Hamburg

Zurich

Brussels

Number of regenerators: 4

R$$$$

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Reduce overprovisioning (margins) on new lightpaths by reducing the uncertainties of the network characteristics thanks to

(1) monitoring

(2) learn from previously established lightpaths

E. Sève et al., OFC’17

Quality of transmission model,

Application to power uncertainties

Example – Containing design marginsMarginless live network planning with machine learning

No learning

# Lightpaths

SN

R e

rro

r (d

B)

0

-1

-2

1

2

3


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Track mechanical stress thanks to real-time State of Polarization monitoring

SOP Monitor embedded in coherent receiver


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Use fiber as a sensor to avoid outage while providing root cause analysis of incidents

Track mechanical stress thanks to real-time State of Polarization monitoring

Embedded in a Coherent Receiver

Brings enhanced security and resilience to the fiber infrastructure by - detecting fiber break - classifing event with machine learning


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When cloud contaminates transportDistributed (edge) cloud networking

New stringent requirements, new traffic patterns

Beyond industry 4.0, on-line gaming, VR/AR, and 5G (cloudRAN)

- Standard packet-based networks fail because of latency

fluctuations (jitter)

- Circuit-based networks are not economically viable because too rigid/costly for the volatility of DC-like traffic

Need for a single convergent platform

- to host most time-sensitive apps, where lower and higher bounds should match (deterministic)

- reactive to the traffic pattern of a DC (dynamic)

In today’s DC(measured)

What we needfor time sensitive

applications

Fre

qu

en

cy

Fre

qu

en

cy

Latency (µs)

Latency (µs)Dynamic Deterministic Networks


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Making the best of two opposing strategiesContaining latency in 5G networks

packet

switching

circuit

switching

Bell Labs proposal:

Optical Ethernet (OE) between edge DCs

Cloud Burst optical slot switching (CBOSS) inside DCs

Natively designed for best effort traffic

Effortless on-the-flyutilization of resources

Increasing latency with increasing load

Decreasing throughput with increasing load

Statistical multiplexing of data

Natively designed for business traffic

In-advance reservation of resources

Deterministic, guaranteedlatency

Constant, guaranteed throughput

Secure separation of data flows


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Bell Labs approach : get a time, why wait in line ? Distributed Dynamic Networking becoming real

Eliminate latency fluctuations by

- introducing (electrical or optical) time slots of fixed duration as data containers

- reserving slots for time-sensitive traffic, opportunistic queueing for best effort traffic.

- Automated orchestration from one domainto the next for strict end-to-end latencycontrol and L1 slicing

* across access fronthaul

* backhaul across DC and metro.

Change the user’s waiting experience as Disneyland did it with Fastpass


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High capacity optical satellite communicationSpace, the new frontier for coherent systems?

Optical feeder link

Optical link

Internet Ground station

Optical link

Clients

Countries with poor Internet connection diversity

Landlocked countries

Risk of disconnection:

SevereModerateLowRobust

RF link

Key goal (What we are trying to achieve):

Bring internet services to populations in countries in isolated or landlocked world regions and enhance reliability of existing internet services

Key disruptions (How we are thinking differently):

Leverage Nokia’s leadership in coherent transmissions to develop:

space-proof hybrid RF/optical satellite repeaters, with 10X greater Gbit/s/Watt

the corresponding optical ground stations, integrated with terrestrial networks


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seminar at institut mines telecom may 3 “brighter networks: a · 9 © 2018 nokia nokia internal...

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