Danish Rafique’s OFC 2019 presentation explores the AI application space and its architectural integration into today’s end-to-end network management stack.

1. Danish Rafique, senior manager, ML and AI
Challenges and solutions
Artificial intelligence in IoT-to-core network
operations and management

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Agenda
Challenges, enablers and goals
Case study: fault management
1
2
3
4
Network management applications
Food for thought5
Architecture and AI hierarchy

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Challenges | Value | Goal
Network complexity
Operational black boxes
Application/service diversity
Dynamic network behavior
Availability requirements
Extent of data sources
Planning and optimization
Capex reduction (design)
Simplify operations
Services
Network optimization

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Application space – Where to start?
NN: Neural Networks, K-NN: k Nearest Neighbors, DT: Decision Trees, SVM: Support Vector Machines,
RL: Reinforcement Learning, BN: Bayesian Networks, NLP: Natural Language Processing, DNN: Deep Neural Networks

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Network segments reuse – Not really!
ML-driven predictive maintenance: “Similar” challenges for various
network segments (failure detection, RCA, remedy, etc.)
PacketCore
While ML engines developed for one network domain don't simply scale to
other network segments, ML pipeline helps
Sources Monitors IoT
Relevance
packet
Relevance
core
Infrastructure
Locations,
holders, etc.
Medium Low
Performance
indicators
Latency, QoE,
jitter, etc.
Medium Low
Profile
Settings, usage,
etc.
Low Low
Sensors
Mobility, mag.
field, etc.
Low Low
Device
Type, MAC,
address, etc.
Medium Medium
L-1 flap
BGP error
Memory
Packet
buffer full
Gain fault
Low
power
Laser
drift
Temp.
high

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Holistic network management – AI hotspots
Edge DC
controller
Cloud orchestrator Transport orchestrator
Edge SDN
controller
Mid DC
controller
Packet SDN
controller
Core SDN
controller
Hyperscale
DC controller
Global orchestrator
AIhierarchy
Transport networkPacket networkEdge networkIoT
Hyperscale
DC
Edge DC
VNF host
Mid DC
Gateway
Macro cell
Micro/Pico/Femto cells
Hybrid
AI
Hybrid
AI
Hybrid
AI
Controller
AI
Orchestrator
AI
Platform
AI
Hybrid
AI
Local
AI

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Case study: fault detection
o Optimized asset availability
o Improve customer satisfaction
o Reduce unnecessary replacements
o Lower service loss
Networkassetreliability
Time
Normal
operation
ML-driven
detection
Rule-based
detection
Reactive
mode
Future (field data)
Current day (field data)

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Without the IoT-to-core theory of unified networking, we will keep reinventing the wheel
Food for thought
Data access, storage, processing (tradeoffs)
Explainable models (must-have)
Real-time applicability (diverse req.)
End-to-end models (orchestration)
Big data security (show stopper)
Silos (disaggregation)
Software stack (standardization required)
http://blog.advaoptical.com/en/About/danish-rafique.aspx
• D. Rafique, et al., “Machine Learning for Network Automation”
Tutorial Invited JOCN, 2018. (Open access)
• D. Rafique, et al., “TSDN-enabled network assurance: A cognitive
fault detection architecture,” ECOC Upgraded, 2017.
• D. Rafique, et al., “Analytics-driven fault discovery and diagnosis for
cognitive root cause analysis,” OFC, 2018.
• and several others …
https://www.datacenterknowledge.com/industry-
perspectives/streaming-telemetry-unleashing-big-datas-
power-network-management

9. Thank you
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drafique@advaoptical.com