Operationalizing SDN: From Automated Control to AI

Operationalizing SDN
Ulrich Kohn
April 2018
From automated control to artificial intelligence

© 2018 ADVA Optical Networking. All rights reserved.22
Self-/automated-/machine-learning can unleash phenomenal possibilities
The power of learning
Human DNA: 1 Gbyte of data
Learning
Human brain: >1 Pbyte of data

Software-defined cognitive networks:
machine learning from comprehensive network
data for self-organizing, self-optimizing
management
Software-defined networking: automated,
multi-layer control with open interfaces for
better resource utilization
Legacy NMS: manual processes with tight
control and potential for higher resource
utilization
Short-term improvements – long-term objectives
Intelligently managing and operating networks

Intent-based service activation
Real-time, intent-based
provisioning of secure connections
Multi-layer network optimization
and self-healing for highest
availability
Seamless integration with open,
standardized interfaces
Open network automation -
multi-partner demonstration
NETCONF RESTCONF/ TAPIOPENFLOW
Secure photonic layer
Secure CE layer
Secure vSwitch layer
Intent-driven API
Network orchestrator
Source: Intent-Based In-flight Service Encryption in
Multi-Layer Transport Networks, OFC 2017

Security is key
ZTP is flexible and can be adapted to operational needs and stakeholder scenarios
Zero touch provisioning in action
Configuration steps Yang models
1. Device connects to bootstrap server and authenticates itself and
the bootstrap server
Zero touch information data model; procedure: get-
bootstrapping data
2. Device uploads the boot-image and configuration scripts and
verifies integrity and authenticity
Ownership certificate, ownership voucher
3. Device installs and executes boot image and scripts Procedure: report-progress; device: “bootstrap
complete”
NB: There is also a need for securely connecting with NMS systems and there might be several boot and configuration servers …
Service provider (owner)
Bootstrapping server

Intelligence emerges from complexity and scale
Intelligence
substituting human
capabilities
Insight
extending human
capabilities
Explain
affirming human
capabilities
• Machine learning: prediction
• Deep learning: automated machines
• AI: behaves and reasons
• Data analysis: individual problem
• Data analytics: general problem
• Big data: beyond traditional data
• Statistics: quantification
• Data mining: pattern identification

AI business value expectations (suppliers)
Optimization of
network resources
Fraud detection
and security
Personalization
Predicting
congestion
ChatbotsSituation-aware
assuranceCall center
Natural language
machine control
Source: AI – the time is now; TM Forum, Dec 2017

Understanding what it needs
Low hanging network intelligence fruits

Major SDO and open communities start AI / autonomous networking initiatives
Standard bodies, open source and industrial alliances

Use case: predictive maintenance
Year-months Days-hours Minutes
Physical
degradation
modes
Equipment
dynamic modes
Failure
modes

Predictive maintenance
Time
Bit errors
Performance measures of many optical
pluggables combining several customer networks
2. Maintenance window scheduled
3. Pluggable managed replacement
- Bit errors drop back to acceptable levels
- No unexpected traffic outage thanks to
predictive maintenance
1. Optical pluggable reporting errors;
- Bit errors are on the rise
- The shape of the curve is an early
indicator that the HW will fail (pattern
detection through ML).

Machines learn from networks for initiating early action -> higher availability
ADVA network analytics
Input Inhouse Storage Predictive Modelling Dashboard Distribute
JSON
CSV
Warnings
Suspicious data
Customer
A
Customer
B
Customer
C

Goals
• Predicting maximum
capacity on new path
• Suggesting best route
• Maximize margins
Parameters
• Launch power
• tilt, amplification
• Composite power levels
Which method? Analytically calculating or self-learning algorithms
Use case: network optimization
Performance ???

Network optimization – problem statement
Calculation is extremely complex and time consuming
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It somehow has to start with Maxwell,
leading to wave propagation in fibers…
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erEE 
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Next, consider dispersion, polarization
and nonlinear effects …
… and you get a nonlinear
propagation equation …
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 … which cannot be solved analytically anymore and
requires time-consuming numerical calculations.
Simplified considerations,
like OSNR only …
Npf log10dBm58OSNR inges0,1nm 
… or calculating bit
errors directly …
            
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… do not consider span-length variations, amplifier types, different modulation formats and FEC types, etc. in a simple way and need severe approximations, again
leading to the necessity to calculate each channel and path individually …
Fiber Length
Power[dB]
Gon/off
With B-FRA
Without B-FRA
Separate network into OTDAs
with any-to-any optical connectivity
Pre-analysis
Power and
Dispersion
Engineering
Performance
Analysis
Topology
Link parameters
(traffic demand and routing)
Determine the ReferencePath as the
longestpath with N spans an OTDAhas to
supportwithout electrical regeneration
Define the targetspan launchpower from
N and accumulated launch-power limit
Design CD to hit per-spantarget range
based on reference-pathresidual-CD criteria
Design power by placing amplifiers per link
to attain the node ingress and egress
power targets
Any-to-any path-performanceanalysis
based on signal-integrity parameters
Iteration:
- Optimize span launch power
- Alter amplifiertypes
- Add regeneration
- Add amplificationsites
- Alter designtarget
1E-8
1E-7
1E-6
1E-5
1E-4
1E-3
1E-2
1E-1
1E+0
1 4 7 10 13 16 19 22
BER
Es/N0 [dB]
BPSK
QPSK
9QPR
8QAM
16QAM
Even the NLT concept …
OSNR Requirement incl.
Penalties
10 20 300
16
Number of Spans
24
20
28
32
OSNR[dB]
12
OSNR Degradation
System
Limit
… requires a workflow that is
pretty complex – despite the
many approximations …
Span Count
4 8 16120 20
LaunchPower[dBm]
-1
3
7
11
10 Gb/s, 100 GHz, G.652
10 Gb/s, 50 GHz, G.652
10/40 Gb/s, 50 GHz, G.655
4 dBm
1 dBm
5 10
13

Complexity of numerical
computation requires:
• Sophisticated skills
• Precise fiber, component and
signal characteristics
Real-time estimation better
than near real-time
calculation, however
• Different skills required
• Outcome unclear
Self-learning model estimates/predicts channel performance
Use case: network optimization
{Number of links, span loss,
number of channels, power
levels, modulation format, …}
Real
performance
Training
data
Fiber characteristics
Numerical calculation
Neural network
Test data ML estimation

Neural network trained
with real data as well as
analytically modelled data
Performance estimation
useful for real-world
operations
Further work: path
selection, channel
optimization (power,
modulation index)
Reasonable results with moderately sized neural network and test data set
Encouraging results from initial work
Predicted margin vs. expected margin

Service activation over standardized MEF Presto
network resource provisioning API
Talk to us and visit us on the exhibition floor!
Towards automation and AI
With ZTP towards autonomous networking
Predictive maintenance
Intelligent planning
Further ideas …
… and even more great ideas

Further reading
• D. Rafique, et al., “TSDN-enabled network assurance: A cognitive fault
detection architecture,” in 43rd European Conference on Optical
Communication (ECOC), 2017.
• D. Rafique, et al., “Cognitive assurance architecture for optical network fault
management,” IEEE Journal of Lightwave Technology, vol. 36, no. 7, pp.
1443–1450, Apr 2018.
• D. Rafique, et al., “Analytics-driven fault discovery and diagnosis for
cognitive root cause analysis,” in Optical Fiber Communication Conference.
Optical Society of America, 2018.
• etc.
A few publications:
https://blog.advaoptical.com/en/

Thank you
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Operationalizing SDN: From Automated Control to AI

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