1. Sana Tariq Sr. Architect Service Orchestration March 26th, 2018
Telecom Service Providers PoC Learnings from ONAP Policy Engine with AI/ML Predictive Inputs
Sana Tariq
Sr. Architect Service Orchestration
March 26th, 2018

2. Agenda Service Orchestration Vision Motivation for PoC1 2
Motivation for PoC 3
Intelligent Closed Loop Architecture
Beyond PoC: Academic Research 4 5
Next Steps & Conclusions
Questions 6

3. Orchestration Desired State Roadmap
5G IoT customers defined services through customized user portals
AI driven/managed operations, capacity and applications
AI driven/managed services (advanced)
Automated Assurance
Applications Onboarding
PNFs
Building NFV Cloud
Q2 2018
2016
2017
2018
2019
2020
2021
2022
IoT and OTT Services/ User-defined Services/International customers
Increased Maturity OSS/BSS interlock, evolution of customer Portals, inventory compliance
Increased Maturity of Catalogs/Templates/Blueprints to deliver software defined services
H12018

4. PoC Background & Motivation
Why we need AI/ML?
Amazon use AI/ML to achieve huge cost & performance optimization on clouds, need to investigate similar strategies for NFV Cloud
In the absence of AI/ML Policy Engine will take reactive decisions based on every false spike/surge without knowledge of patterns
Three types of Policy Closed Loop Actions
Issue/problem/anomaly --- healing
Congestion/over utilized resources – scaling
To improve the QoS/Efficiency – optimization
If we connect analytics engine to Policy Control directly to take closed loop decisions, we will be taking wrong decisions. We will be responding and reacting to every spike, change or anomaly in the cloud and network without looking at recurrent pattern or identifying the root cause for it.
Policy + AI use cases for Telecom Service Providers
5G IoT services data changing complex patterns could be best managed by AI to get predictive inputs take appropriate actions through Policy- Business Intelligence teams working on some AI with-in TELUS
Traffic Classification (flow identification, QoE) – HOMA already doing it
Capacity management: Resource Orchestration can place workloads optimally across different Vim zones, Pro-actively calculate risks
Operations: Anomaly detection, fault detection, root cause analysis
IoT/5G devices can increase traffic patterns un-expectedly across various cloud regions

5. What will AI Driven Policy Deliver?
Improved Customer Experience
Cloud Optimization & Efficiency
Energy optimization
Capacity optimization
Various Cloud configurations
Closer to the edge
Service Healing
Service optimization
Differentiated QoS
Secure Cloud
Network Optimization & Efficiency
Proactive/predictive threat identification
Closed loop decisions to attacks mitigation
Traffic optimization DC SDN controller
QoS based routing

6. Closed-loop orchestration

7. ONAP AI Driven Policy PoC
What we did so far
Static Policies
Static thresholds
Focus on VNFs and Services scaling and healing
What we want to do this year ….
Dynamic policies
Dynamic thresholds
Assisted with AI/ML
Focus on Cloud Optimization
Capacity
Energy
Cost
Performance
Humans Static closed loop Dynamic policies assisted with AI/ML
Today without AI, we are aided by sophisticated software products that package data for human consumption: in graphs, the "trails of network blood," and postcard suggestions of actions. Then we allocate to humans the final decision on what to do. Then often humans take, or at least authorize, the required action.

8. ONAP Intelligent closed-loop Architecture

9. ONAP Intelligent closed-loop

10. AI Policy driven workload placement in Cloud
Cloud infrastructure could also be designed, configured/optimized for
Energy
Throughput
Location
Performance
The idea of QoS and differentiated treatment of networking flows has been well studied/applied in the field of networking
There are different types of services supported by traditional Service providers relating to different types of workflows that can be placed in cloud based on Policy
Location based
Real-time, low latency
High throughput/performance
Policy Engine to include intelligent rules for first-time placemen, moving/switching workloads based on traffic surge/optimization requirements

11. Beyond PoC: UBC Research & Patents
Areas of Study
How to include QoS colors/tags for various services
VNFDs (specific to VNF type)
NSDs( specific to service type, generating workload flows)
How to develop Policy Rules
Matrix to achieve multiple/over-lapping goals i.e., Cloud Optimization & better customer QoS
Study cost/performance optimization through execution of policy rules
How to design/configure Cloud in multiple flavors
Edge Cloud (configuration parameters)
Cloud optimized for
Energy
Efficiency
Capacity
Cost
AI/ML could be applied to Policy Rules and policies (or thresholds) updated dynamically based on defined success criteria achieved e.g., Better QoS, Better cloud performance/capacity optimization

12. Conclusion & open questions?
ONAP DCAE evolution will support dynamic policy rules?
What would be the most ideal approach to define Policy rules?
We want to extend APIs to Policy module to internal application owners from various domains to develop/manage their own policies
Will vendors develop additional tools to supplement the functions of DCAE to help reach desired state?
What are the risks associated with AI/ML driven closed-loop automation?
When will service providers be ready for AI driven Orchestration

13. Questions