This document discusses closed loop automation for network functions virtualization (NFV). It begins by outlining the goal of achieving fully closed loop automation through techniques like machine learning, where the network can detect and fix issues without human intervention. It then discusses the current state of play, including tools that allow operators to confirm automated changes. The document outlines the key aspects of closed loop systems, including continuous monitoring, anomaly detection, corrective action recommendation by a policy engine, and status monitoring after repair. It discusses approaches to closed loop automation by projects like ONAP and ETSI OSM. Finally, it explores how machine learning can help make closed loop systems more predictive, intelligent, and dynamic over time as more data is collected.

1. Panel IV
Closed Loop Automation for NFV

2. Our speakers
Srilakshmi Valisammagari
Senior Technologist and Strategist
Verizon Enterprise Solutions
Jim Fagan
Director, Global Platforms
Telstra
David Hughes
VP, IP Engineering
PCCW Global

3. Outline
• Closed-loop automation is the end goal for NFV.
• Telcos are on a journey from manual processes to writing scripts
(Python), building models (YANG, TOSCA), introducing model-driven
programmability, analytics and finally AI.
• With machine learning the network can detect or predict faults, know
how to fix it (e.g. re-route traffic, spin up a new instance) and execute
it without any human intervention.
• However, in the early days it may be risky and many CSPs will want
tools that will let them confirm changes are handled correctly.
• This panel will discuss the state of play for closed loop automation in
NFV and what needs to be done to make it a reality.

4. Control Loop Automation
• Open loop systems capture telemetry and diagnostics information
from the underlying infrastructure (syslog, SNMP, fault and
performance events), conduct analysis, and provide reports or alarms
to the operations team.
• Closed-loop systems continuously monitor the system for problems
(fault, performance, security, etc.) and compute a set of signatures
based on detected anomalies.
• A policy engine then interprets these signatures and recommends
corrective actions to repair the system.
• Once the system has been repaired the status is monitored to check
the problem has been fixed.
• The goal is to minimise downtime

5. ONAP Closed Loop Automation

6. ETSI OSM view of NFV automation
• Design and development (onboarding) – full automation still some
way off
• Instance lifecycle – what can be changed in-life and what is
immutable?
• In-life
• Performance – auto scaling
• Resilience – fault tolerant load sharing
• Service monitoring, assurance and reporting
• All this automation is policy driven

7. ML for Closed-Loop Automation
• Rather than performing reactive analytics, ML can help predict
anomalies in advance based on time-varying signals.
• Instead of applying hard-wired policies or rules to determine next
best action, ML can make the recommendation more intelligent
based on data, context, patterns and outcomes.
• As more data is collected over time, ML can learn from successes and
failures and adjust models to reduce errors. ML can make the system
more dynamic and proactive.
• Opportunities for ML: Self-Optimizing Networks; cybersecurity &
threat analytics; fault management; customer experience
management; traffic optimization.