Brighter networks: a glimpse into the future of optical networks

Transcription

1 Seminar at Institut Mines Telecom May 3 rd 2018 Brighter networks: a glimpse into the future of optical networks Sébastien Bigo 1

2 A vision of the future 2

3 The future of networks is all about time Time to transport The capacity Time to reconfigure The automation 3 Time to queue The latency

4 Agenda The capacity The automation The latency Space, the new frontier Q&A 4

5 Smart Network Fabric : Capacity Bandwidth (B) beyond erbium limit Spectrum [Hz] Approach non-linear Shannon Limit (SNR) C ~ B x M x log 2 (SNR) Spectral Efficiency [b/s/hz] Space Expand fiberspace (M) 5

6 Smart Network Fabric: Solving for SNR with PCS The final piece of the super coherent puzzle High Order Modulation Advanced Coding Probabilistic Constellation Shaping Nyquist Filtering High Baud Rates 6

7 Building a new langage, easier to decipher Based on existing symbols 7

8 Smart Network Fabric: Solving for SNR with PCS PCS optimal capacity at any distance Capacity Discrete QAM formats perform well only at specific distances PCS PCS 25% Shaping gain Shannon Limit approaching 8 Distance

9 Power (dbm) Ch. net bit rate (Gb/s) Smart Network Fabric: Solving for B S-Band C-Band L-Band EDFA 100nm SOA Wavelength (nm) S band C band Total 115.9Tbit/s throughput: Tb/s L band Wavelength (nm) 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) 9

10 Smart Network Fabric: Solving for M Single fiber, more cores Single fiber, more modes 10

11 6 to 40 spatial paths Smart Network Fabric: 1Pb/s System in 2024? Shannon (more PCS) Spectrum (more wavelengths) 50 (C) to 100 cells (C + L) Each cell = Single mode with maximum spectral efficiency (PCS inside) X100 Space (more modes, more fibers) ~ 1Tb/s ~ 1Pb/s 11

12 Agenda The capacity The automation The latency Space, the new frontier Q&A 12

13 MICROSFT cloud network 13

14 Two types of players competing to define and rule the 2025 global cloud IP + Optics Telecommunication providers Prescribers in optics Followers in IP/Ethernet Over the top content providers Prescribers in IP/Ethernet Followers in optics Telcos OTTs 14 Nokia 2016

15 Two types of players, two competing visions Creating the future with the best of both Optics today (Telco centric) Design and optimize Set and forget Guaranteed (large) margins Seeking top performance (bit-rate/distance) Optics tomorrow Try and learn Continuously adjust Always zero margins Working with just-enough performance Seek for maximum quietness (Zzzzz. $$$$) Detect and mitigate Automatic troubleshoot 15 Quotes from Najam Ahmed, Facebook, OFC 2016

16 Two types of players, two competing visions Creating the future with the best of both Optics today (Telco centric) Optics tomorrow Plan resources well in advance (e.g. 15y ahead) secure them before establishing service then allocate demands. Push down demands to the network Provision and monitor All programmable, very dynamic network If equipment fails, remove from network, Perform troubleshooting later 16 Quotes from Najam Ahmed, Facebook, OFC 2016

17 Two types of players, two competing visions Creating the future with the best of both Optics today (OTT centric) Optics tomorrow All-packet approach Fully flexible, can be shared Bandwidth and latency depend on load Connection-based approach Reserved, even if partially filled Guaranteed performance and jitter-free 17

18 Two types of players, two competing visions Creating the future with the best of both Optics tomorrow a network to iterate, not architect a network which fails fast instead of a fail-proof one a network which pushes down demands to the network, not planned 15y ahead a network still with guaranteed delivery, but only when needed 18

19 Brighter optical networks Living networks, accounting for time Demands (t) Learn from the past Predict the future New hardware? f(t) Margin predictor Predict Measure + - Resource allocation SDN Control Flexible TRX Hardware resources (t) Learn from the past Predict the future New demand.. From set-and-forget to try-and-learn 19

20 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), [Sève et al., OFC 17]

21 Automated fiber type identification for cost reduction without traffic interruption Goal and disruption 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. Number of demands: Fiber 1 (SMF): high perf. Fiber 2 (LEAF): low perf. Unknown fiber type Worst case scenario : Unknown SDN platform LEAF With monitoring Number of regenerators: 53 4 London vfiber Type vcost Paris Reduction of network cost thanks to a better knowledge of type of fiber? Lyon R$$$$$ Amsterdam? Brussels? Hamburg Bonn Strasbourg Zurich 21

22 SNR error (db) Marginless live network planning with machine learning Example Containing design margins 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 No learning # Lightpaths 22

23 Track mechanical stress thanks to real-time State of Polarization monitoring SOP Monitor embedded in coherent receiver 23

24 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 Use fiber as a sensor to avoid outage while providing root cause analysis of incidents 24

25 Agenda The capacity The automation The latency Space, the new frontier Q&A 25

26 Frequency Frequency Distributed (edge) cloud networking When cloud contaminates transport 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) Latency (µs) What we need for time sensitive applications Dynamic Deterministic Networks Latency (µs) 26

27 Containing latency in 5G networks Making the best of two opposing strategies Natively designed for best effort traffic Effortless on-the-fly utilization of resources Increasing latency with increasing load Decreasing throughput with increasing load Statistical multiplexing of data packet switching circuit switching Bell Labs proposal: Optical Ethernet (OE) between edge DCs Cloud Burst optical slot switching (CBOSS) inside DCs Natively designed for business traffic In-advance reservation of resources Deterministic, guaranteed latency Constant, guaranteed throughput Secure separation of data flows 27

28 Your worst waiting experience?. 28

29 Distributed Dynamic Networking becoming real Bell Labs approach : get a time, why wait in line? 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 domain to the next for strict end-to-end latency control and L1 slicing * across access fronthaul * backhaul across DC and metro. Change the user s waiting experience as Disneyland did it with Fastpass 29

30 Agenda The capacity The automation The latency Space, the new frontier Q&A 30

31 Space, the new frontier for coherent systems? High capacity optical satellite communication 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 Countries with poor Internet connection diversity Landlocked countries Risk of disconnection: Severe Moderate Low Robust 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 Optical feeder link Internet Ground station RF link Clients Optical link Optical link 31

32 Agenda The capacity The automation The latency Space, the new frontier Q&A 32

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