How to use Machine Learning for Testing and Implementing Optical Networks

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1 How to use Machine Learning for Testing and Implementing Optical Networks Jesse Simsarian, Steve Plote, Marina Thottan, and Peter J. Winzer October 4, Nokia

2 Outline 1. Introduction Machine learning applications and algorithms Network operating system Network abstractions 2. Sensors OSNR Fiber polarization sensing 3. Algorithms QoT estimation optimization for margin reduction 4. Actions Error-aware rerouting - SLA to set error tolerance 5. Challenges 2 Nokia

3 Introduction Ubiquitous cloud services will drive bandwidth and dynamism Computing resources instantiated in minutes Comparable flexibility presently not possible in IP/Optical networks Today: Largely quasi-statically re-configurable components Flexgrid ROADMs, flexible bitrate transponders, tunable lasers, FlexEthernet, Dynamic networking requires sophisticated Network Operating System (OS) Manage and allocate resources for optimal network capacity, security and reliability Need to be able to physically sense and control the optical network Advanced machine learning algorithms on top of the Network OS Dynamic Networking = Sensors + Abstraction + Algorithms + Actuators Communications industry efforts: Optical Internetworking Forum (OIF): SDN for transport networks Open Daylight controller (ODL) Open Network OS (ONOS): Open-source NetOS from ON.Lab, Linux Foundation 3 Nokia

4 Foundations of Machine Learning Applications Machine learning applications sensors + abstraction + algorithms + actuators IP/Optical grooming algorithms Optical margin reduction Optical network defragmentation Programmable network operating system (netos) abstraction Flexible, tunable network Data Center 1 actuators sensors Data Center 2 Polarization Sensor OSNR Sensor 4 Nokia

5 Algorithms Machine Learning Artificial Neural Networks I 1 W 1 Numerical Optimization I 2 I 3 W 2 W 3 W N x > T? O O I N T x I 1 W 11 Σ > O 1 MarI/O: Artificial Neural Network W p1 I 2 Inputs W 12 Σ > O 2 Outputs W p2 I p T. Mitchell, Machine Learning, McGraw Hill (1997) A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. W 1q W pq Σ > O q 5 Nokia

6 Dynamic and Flexible Networks Data Center 2 Data Center 3 Data Center 1 Flex Ethernet client interface IP Layer Flexgrid ROADMs Optical Layer Disturbance /Impairment Bandwidth variable transponders and channels 6 Nokia

[b/s/hz] 15 10 400G 250G 200G 5 PS E 100G 2S 2 (3 rd gen.

7 WDM Transmission Capacity and Flexible Optical Transponders Get the Most Capacity out of Your WDM System Bell Labs 1 Tb/s Field Trial Spectral efficiency [b/s/hz] G 250G 200G 5 PS E 100G 2S 2 (3 rd gen. coherent, 2016) 100 1,000 10,000 Transmission distance [km] C-band capacity [Tb/s] 7 Nokia

8 NetOS: The Network Brain Application needs capacity latency security reliability Network resources coordinate schedule control Network state The network brain is a cognitive global network control plane 8 Nokia

9 NetUNIX NetOS Functions Dynamic intents Intent Framework Network Topology Performance Data Network Resource Manager Network Events Network Config. Continuous testing framework Prototype NetUNIX system designed and demonstrated 9 Nokia

10 Data Models, Network Abstraction Advanced algorithms on an vendor-independent, abstract network representation What information is needed? How many data points are needed? Joint IP/Optical network grooming and optimization - IP/Optical network topology is needed Optical margin reduction - optical network OSNR data is needed The Internet Engineering Task Force (IETF) YANG data modeling language to model configuration of network elements OpenROADM (AT&T, Ciena, Fujitsu, Nokia, SK Telecom ) Defines interoperability specifications for ROADMs, transponders, pluggable optics Includes YANG data models for devices, network, services OpenConfig (Google, AT&T, Microsoft, BT, Facebook ) YANG data models for optical transport: terminal optics, wavelength router, optical amplifier OpenFlow 1.4 (Open Networking Foundation) Common southbound interface definition for multi-vendor network element control NetGraph - mathematically-rigorous, expresses multilayer network complexity Does not replace other efforts, adds explicit layering and mathematical rigor to network model 10 Nokia

NetGraph Primitives Underlying Mathematical Model ) primitives Primitives link Link (e.g. fiber) 1 2 3 Adapter adapter layer Layer 1 layer Layer 22 connect Connect paths Path Bind + s t s t link Link s t bind S.

11 NetGraph Primitives Underlying Mathematical Model ) primitives Primitives link Link (e.g. fiber) Adapter adapter layer Layer 1 layer Layer 22 connect Connect paths Path Bind + s t s t link Link s t bind S. Fortune, Equivalence and generalization in a layered network model, J. Comput. Syst. Sci., vol. 81, no, 8, pp , Nokia

12 Implementation: Adaptation to Network Elements Resource Mgr NetGraph Mgr Intent Mgr Network- level granularity Device Mgr Event bus Link Mgr Host Mgr Flow Mgr Device- level granularity NetGraph abstract network representation maps to network resources Device/Link/Host/Flow Providers Device Drivers Device- specific adaptors OpenFlow REST/GRPC Protocols Populate network elements into NetUNIX SAM - SOAP NokiaNE Proxy Telnet /SSH TCP REST Gather statistics Control network elements OF Switches Optical 1830PSSs Transport WiFi APs PXCs 12 Nokia

13 Outline 1. Introduction Machine learning Network operating system Network abstractions 2. Sensors OSNR Fiber polarization sensing 3. Algorithms QoT estimation optimization for margin reduction 4. Actions Error-aware rerouting - SLA to set error tolerance 5. Challenges 13 Nokia

OSNR Sensing with an Artificial Neural Network Single photodiode eye diagram machine learning analysis: J. Thrane, J. Wass, M. Piels, J. C. M. Diniz, R. Jones, and D.

14 OSNR Sensing with an Artificial Neural Network Single photodiode eye diagram machine learning analysis: J. Thrane, J. Wass, M. Piels, J. C. M. Diniz, R. Jones, and D. Zibar, Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals, Journal of Lightwave Technol., vol. 35, no. 4, Target OSNR Function of eye diagram variance and weights Measurable Input Unknown transformation Output Artificial neural network used to estimate OSNR from eye diagram 14 Nokia

15 Fiber as a Sensor Optical fiber network becomes a massively-distributed motion detector 15 Nokia

16 OSC for Polarization Sensing Span Monitoring OSC SFP Amplifier Cards OF OF OA OSC SFP Fiber Direction 1 PBS V H D 1 D 2 Processing + - PD TIA PBS Fiber Direction 2 OF OA OSC SFP OF + - OSC data channel Polarization monitor signal TIA PD 1 mm OSC OA OSC OA OSC Rx 16 Nokia

Experiment CW Laser 7 cm Hammer Test Fiber Adaptif A3300 Pol. Synth. Pol. Analyzer PBS D 1 D 2 Scope Ch. 1 Ch. 2 3 Results for Comparison Trig. PR 1 A, full = sin 5 PR 2 2 2 ( 0.

17 Experiment CW Laser 7 cm Hammer Test Fiber Adaptif A3300 Pol. Synth. Pol. Analyzer PBS D 1 D 2 Scope Ch. 1 Ch. 2 3 Results for Comparison Trig. PR 1 A, full = sin 5 PR ( 0. ΔS1 + ΔS2 + S3 ) SRanalyzer 2 Δ 1 A, 2 Arm = 2sin 0. 5 Δ ( P P ) 1 2 SRanalyzer ( P + P ) 1 2 Full analyzer measurement 2-Arm analyzer calculation PR 1 S, 2 Arm = 2sin 0. 5 Δ ( V V ) 1 2 SRscope ( V + V ) Arm scope measurement 17 Nokia

18 Results Summary Figure of Merit 58 Fiber Impacts max( PR F = max( PR 2 Arm A, full ) ) F F A,2 Arm S,2 Arm = 0.62 ± 0.22 = 0.58 ± % > 0.25 J. E. Simsarian and P. J. Winzer, Shake Before Break: Per-Span Fiber Sensing with In-Line Polarization Monitoring, in Proc. OFC 2017, paper M2E.6, Nokia

19 Coherent Transponder Polarization Sensing Path Monitoring Machine Learning Event Classification controller Event classification: Y = f(x) F. Boitier, V. Lemaire, J. Pesic, L. Chavarría, P. Layec, S. Bigo, E. Dutisseuil, Proactive fiber damage detection in real-time coherent receiver, ECOC Nokia

20 Outline 1. Introduction Machine learning Network operating system Network abstractions 2. Sensors OSNR, BER, NGMI Fiber polarization sensing 3. Algorithms QoT estimation optimization for margin reduction 4. Actions Error-aware rerouting - SLA to set error tolerance 5. Challenges 20 Nokia

21 Why and When do We Need to Learn from Experience (As opposed to knowing what we are doing ) Good models of the underlying physics are generally best Subsystem: Chromatic dispersion compensation in a coherent DSP System: Predicting physical-layer performance through adequate models Network: Rule-based (x-layer) routing and wavelength assignment But: How good / comprehensive is the respective model? Hybrid approach: physical model + parameter tuning with machine learning - A db in system planning accuracy is as valuable as a db in coding gain! Q factor FEC threshold Margin Predicted performance Q factor FEC threshold Margin Predicted performance Q factor Margin FEC threshold Actual performance Actual performance Actual performance Baseline planning tool Improved planning tool Supervised learning (TRX feedback) Wavelength Wavelength Wavelength 21 Nokia

22 Optical System Margin Reduction Learning process to reduce uncertainties Lifecycle Learning Algorithm SDN data base Re-training E. Seve, J. Pesic, C. Delezoide and Y. Pointurier, Learning process for reducing uncertainties on network parameters and design margins, in Proc. OFC 2017, paper W4F Nokia

23 Outline 1. Introduction Machine learning Network operating system Network abstractions 2. Sensors OSNR, BER, NGMI Fiber polarization sensing 3. Algorithms QoT estimation optimization for margin reduction 4. Actions Error-aware rerouting - SLA to set error tolerance 5. Challenges 23 Nokia

24 SDN Testbed at Bell Labs IP Transformation Center (IPTC) NetUNIX Executive Briefing Center Optical Transport Service Routers AirFrame POD AirFrame Rack Photonic Crossconnect (PXC) TOR Switches & Servers 24 Nokia Fiber Connectivity Error-aware networking Optical Transport Optical switching inside data center Service Routers PXC MEMS switch SDN Switches

25 Error-Aware Rerouting Host 1 Switch 2 Host 2 Host 3 Switch 1 Switch 3 Host 4 Transport 2 SDN Switch Transport 1 Transport 3 Optical Transport Host 1 Switch 2 Host 2 Host 3 Switch 1 Switch 3 Host 4 Transport 2 Transport 1 Transport 3 25 Nokia Regular intent Error-aware intent

Challenges and Discussion Points for Network Operators Classification of Modulation format Stability and reliability Event classification probability How much data is enough as a basis for machine

(abstraction) Machine learning algorithms local to network element for sensor data Reliability and operational implications Human operator confirmation of network operations Business structures - IP

26 Challenges and Discussion Points for Network Operators Classification of Modulation format Stability and reliability Event classification probability How much data is enough as a basis for machine learning? What data is needed at which layer of the network? (abstraction) Machine learning algorithms local to network element for sensor data Reliability and operational implications Human operator confirmation of network operations Business structures - IP and optical businesses What are operational barriers to more efficient and dynamic network operation? Outages and accountability applications on top of NetOS *J. Thrane, J. Wass, M. Piels, J. C. M. Diniz, R. Jones, and D. Zibar, Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals, Journal of Lightwave Technol., vol. 35, no. 4, Nokia

27 Conclusions Cloud services as drivers of bandwidth and network dynamism Dynamic networks = Sensors + Abstraction + Algorithms + Actuators Machine learning algorithms to identify network events, reduce optical system margins, and optimize the network Built on programmable NetOS and flexible network infrastructure Different machine learning methods ranging from black box artificial neural networks, to event classification, to hybrid methods using a physical model of the system and machine learning NetUNIX prototype NetOS Improve sensing of the physical layer to detect impairment or possible disruption Abstract representations of the network layers with NetGraph Applications Impairment aware IP/optical re-routing Optical channel monitoring 27 Nokia

28 28 Nokia 2016

Brighter networks: a glimpse into the future of optical networks

Seminar at Institut Mines Telecom May 3 rd 2018 Brighter networks: a glimpse into the future of optical networks Sébastien Bigo 1 A vision of the future 2 The future of networks is all about time Time