This document discusses using network analytics and machine learning to automate critical infrastructures. It outlines a path towards network automation using simplified service ordering, streamlined operations, and intuitive interfaces. Analytics can provide insights to optimize network performance through continuous data collection, analysis using AI models, and automated orchestration. Open challenges include high data requirements, multi-vendor data sharing, and integrating ML solutions into network operations. Case studies demonstrate ML-based optical network monitoring and a data-sovereign telemetry broker. Overall, network analytics and AI will enhance optical network control and automation.

1. Analytics for automating critical infrastructures
Achim Autenrieth, Adtran
OFC 2023, 8 Mar 2023
Symposium on “beyond the hype of network analytics: use cases,
feasibility, and barriers.”
ADVA is now part of Adtran

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The path towards network automation
Simplified point-and-click
service ordering
Speed-up Service
Monetization
Streamline
Network Operations
Simplify
Support
Automated, fully
interconnected, virtualized
network infrastructure
Intuitive customer-facing
interfaces that simplify service
management and monitoring
Intent-based
operation
Streaming telemetry &
AI-based analytics
AI-based
network
automation
SDN
control
✓

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AI/ML for network automation and optimization
Programmable
Network
Insights
Orchestration
Open APIs
Streaming
Telemetry
Closed Loop Automation
Data collection (Telemetry)
Analysis
(Real-time & offline)
Refine & Adapt
automatically
Predictive
Maintenance
Turning data into actionable insights to optimize network and service performance
Network Optimization & Insights
Turning data into actionable insights to optimize
network and service performance
• Focus on end-to-end optimization
• Combines real-time data collection, AI-driven analysis
and orchestration to enable proactive optimization
• Cloud-based control covering on-premise, access and
packet/optical metro/core networks
Streaming telemetry
• Continuous data collection via streaming telemetry
• Data-centric analysis of network conditions
• gRPC / gNMI and NETCONF/YANG protocols
Network analytics / ML use cases
• ML-based transmission performance optimization
• Traffic and failure prediction
• Predictive maintenance
• ML-based network optimization

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Network analytics pipeline
Data Storage
and Analysis
Time series
database
Visualization
Data Collection
Data Buffering
Stream
processing
Message broker
• gRPC/gNMI
• NETCONF
• SNMP
• YANG Push
Telemetry
Network Elements
ML-assisted
Solutions
Ticketing
Alarming
SDN Control
Orchestration
Scalable common data collection framework
• Telemetry retrieval
• Efficient telemetry collectors, brokers, and Time-Series Databases (TSDB)
• Computation requirements for data analysis
• Cross-interface and cross-terminal telemetry data sharing

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AI-driven fiber networking – the whole picture
Kafka
(streaming
telemetry/events)
RESTCONF
(prov and monitoring with
service level APIs)
Cloud-based network
analytics applications
AI-Driven Orchestration, Management and Optimization
Network and service
control and orchestration
Network Optimization & Insights
NETCONF/YANG control
gRPC telemetry
OSS / BSS APIs
(integration on op. /
business support systems)
uCPE
Edge cloud
Cloud-managed
Mesh Wi-Fi
Business Ethernet
Fiber Access
Ethernet/WDM with
network synchronization
DCI
Mobile
X-Haul and Wholesale
Metro / Core
WDM
Access and Aggregation Optical Networking
Subscriber
Networks / Solutions
Open, disaggregated access, aggregation and transport
Cloud
data center
Core
data center
Metro
data center
Open Line System (OLS)

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Open questions & challenges
Can we extract real-time
data for ML models?
Can we work with the
data we already have?
Can this be orchestrated on an
SDN-based network?
Can we address relevant use-
cases to our ML+data setup?
Can we share data with data
privacy and sovereignity?
How do we get the data
to the orchestrator ?
UC1
UC2

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• Machine Learning solution for OTDR traces
• Reflective event detection in Passive Optical Networks (PON)
• Web-UI for visualization of PON characterization and monitoring
UC1 ML-Based PON Characterization
Research highlights
e d e e
n e
e
e
n
e
onne o o e e
on o
e
o b ne
Maximilian Brügge et al., “Live Demonstration of ML-Based PON
e on nd on o ng ( Z.7)”, OFC 2023 [M3Z.7]
2023
M3Z.7

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• Modular telemetry broker for extensive collection and sharing of data
• Flexible Optical Terminal in a partially disaggregated optical network
• Optical performance measurement (SNR, Q-Factor, BER) with second granularity
Research highlights
UC2 Data-Sovereign Telemetry Broker
`
CA
ROADM 3
ROADM 1 ROADM 2
OLS
Vendor B
λ2
Terminal
Vendor
C
10G/100G
aggregation
10G/100G
aggregation
λ2
Terminal
Vendor
A
agent
agent
CB CC
Data Sovereign
Marketplace
Network Health
Monitoring Tool
IDS Connector
VNF Provider Y
Impairment
Validation Tool
IDS Connector
VNF Provider Y
Data Marketplace Connector
Data MarketplaceConnector
Mapping Data
Model
DSC
Agent Wrapper
Consumer
DSC
Data
Owner
Config File
Agent Wrapper Provider DSC Consumer DSC
Agent Wrapper Provider DSC Consumer DSC
Collect Device Info
Create Resources
Artifact Request
Call / Telemetry
Device Info
TelemetryData Res. Serialize Telemetry
Data
Artifact Response
Payload: Serialized Data
Telemetry
Update
Telemetry
Req.
TelemetryReq.
2023
M3Z.3
Haydar Qarawlus et al., “Demonstration of Data-Sovereign Telemetry Broker
o en nd gg eg ed e o k ”, OFC 2023 [M3Z.7]

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• SDN, streaming telemetry, and network analytics provides you insights to
improve and optimize network operations
• Privacy-preserving ML techniques and inter-operator model sharing
support carrier grade network automation
• AI/ML will gradually enhance / augment optical network control and automation
Open research challenges & next steps
• High data acquisition and processing requirements
• Alignment on multi-vendor ML-AI data exchange formats
• Open dataset access and machine-learning marketplace integration
• Transport network simulation and digital twin
• Network optimization, energy efficiency and sustainability
• Integration in vendor solutions and interface to OSS / BSS
Conclusion and outlook

10. Thank you
This work has been performed in the framework of the
CELTIC-NEXT project AI-NET-PROTECT (Project ID
C2019/3-4), and it is partly funded by the German Federal
Ministry of Education and Research (FKZ16KIS1279K).
Acknowledgements:
Maximilian Brügge, Jasper Müller, Sai Kireet Patri, Sander Jansen, Jim Zou,
Stephanie Althoff, Klaus-Tycho Förster, Haydar Qarawlus, Steffen Biehs,
Behnam Shariati, Jose-Juan Pedreno-Manresa, Ayoub Bouchedoub, Hendrik
Haße, Pooyan Safari, Johannes Karl Fischer