At OFC, Sai Kireet Patri demonstrated a single solution capable of fiber monitoring, predictive maintenance of optical hardware and security information management. Leveraging machine learning, the system integrates ADVA’s comprehensive ALM fiber monitoring solution and its robust FSP 3000 ConnectGuard™ Optical security technology.

1. DeepALM: Holistic optical network monitoring
based on machine learning
7 March 2022
OFC 2022 Demo Zone (M3Z.11)
Sai Kireet Patri, José-Juan Pedreno-Manresa, Joo Cho, Khouloud Abdelli, Carsten Tropschug, Jim Zou, Piotr Rydlichowski

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AGENDA
Motivation
1.
Faults and Monitoring Solutions
2.
DeepALM
3.
Demo Setup
4.
Use Cases and Demo
5.
Conclusions
6.

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Protect communication infrastructure against failures, attacks and natural disasters
Network Resilience is more important than ever
Motivation
Optical JC&S*:
Phase-sensitive OTDR for
infrastructure monitoring
*Joint communicaton & sensing
*koreaherald.com
*nature.com
*Getty Images via bbc.com

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Combined Network Resilience Monitoring is a complex task
• Digital and physical
security is a key
component in networks
of future
• Passive fiber sensors
deployed extensively for
investment protection
• Monitoring large number
of devices for security
incidents can be
challenging
Demand for access to fiber is ever-growing
Motivation
5G Networks Enterprises
Critical Infrastructure
Protection

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Aim: Deploy solutions which reduce overall fault analysis and repair time
Different faults
Faults and Monitoring Solutions
Higher Probability of occurence
More time to
localize issue
Less time to
localize issue
Lower Probability of occurence
*datastrait.com
*emersonenvironcare.com.au
*Wikipedia.org
*Michael Madon, medium.com
*deciso.com
*X Liu et. al

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Aim: Convert collected telemetry and maintenance information into holistic, actionable intelligence
Different monitoring solutions
Faults and Monitoring Solutions
Fiber faults and location can be detected via OTDR trace
Passive, remotely monitored door sensor
states can be detected via OTDR trace
Encryption status of each encryption card can be
monitored to find any issues
Predictive maintenance of deployed lasers can reduce lead
times
Encryption status of each encryption card can be
monitored to find any issues
Fiber faults and location can be detected via OTDR trace
Passive, remotely monitored door sensor
states can be detected via OTDR trace
Predictive maintenance of deployed lasers can reduce lead
times
Encryption status of each encryption card can be
monitored to find any issues

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ML driven data analytics can reduce fault analysis and detection times
• Fiber Monitoring
• Detecting and localizing
fiber cuts via ML-enabled
OTDR monitoring
• Intrusion Detection
• Door sensor state detection
using ML
• Security Info Management
• Encryption Status
monitoring
• Predictive Maintenance
• Laser time to fail prediction
of vendor based aging data
Hetergeneous devices monitored
DeepALM Overview
Holistic
Network
Monitoring
Fiber
Monitoring
Intrusion
Detection
Security
Information
Management
Predictive
Maintenance

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Fiber Monitoring and Intrusion Detection
Security Information Management
Hardware Components
DeepALM Demo Setup
attn
ALM Fiber Spool 20 km Door Sensor
Extra patch cord w. open
connector
FSP3000 R7
5TCE10G-AES
FSP3000 R7
5TCE10G-AES
Alice Bob

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Python Scripts on cloud server enable ETL pipelines
Software Components
DeepALM Demo Setup
ALM
Alice
Bob
OTDR Trace
via SSH
Cloud Server
ML Fiber
Cut
ML Door
Sensor
InfluxDB
Data
Lake
Grafana-based Web
Dashboard
Encryption Status
via SNMP Polling
Encryption Status
via SNMP Polling
ETL Pipeline 1
ETL Pipeline 2

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Fiber Cut Monitoring
Use Case 1
ALM
OTDR monitoring
script generates
signal level at
each distance
point
Pre-processing
functions create
inputs for ML
model
Trained GRU Model [*]
InfluxDB
Grafana
* K. Abdelli, et.al , "Gated Recurrent Unit based Autoencoder for Optical
Link Fault Diagnosis in Passive Optical Networks," ECOC, 2021

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ML model used provides higher fault detection accuracy as compared to conventional methods
Sneak Peak at the ML model
GRU based Autoencoder
* K. Abdelli, et.al , "Gated Recurrent Unit based Autoencoder for
Optical Link Fault Diagnosis in Passive Optical Networks," ECOC,
2021

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Door Sensor Monitoring
Use Case 2
ALM
OTDR monitoring
script generates
signal level at
each distance
point
Pre-processing
functions create
input at fixed
locations for ML
model
InfluxDB
Grafana
Trained GRU Model [*]

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Security Information Management
Use Case 3
SNMP Polling for
Encryption
Module
FSP3000 R7
5TCE10G-AES
Bob
InfluxDB
Grafana
• We simulate a security incident by changing the authentication
password @ Bob
• By forcing key-exchanges, the encryption module will show errors
• These errors are visible on the Grafana dashboard

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DeepALM takes us one step closer to convert data into actionable intelligence
• Proof-of-Concept and data flow
for real-time heterogeneous
device monitoring
• Solution is flexible for adding
more devices and creating
rules/alerts for monitoring
• With larger devices and
datasets, ML enabled data
analytics can provide deeper
insights into faults and
intrusions
• Grafana based front-end
dashboard provide friendly GUI
for data visualization
Conclusions
Holistic
Network
Monitoring
Fiber
Monitoring
Intrusion
Detection
Security
Information
Predictive
Maintenance

15. Thank you
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