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

Agenda Service Orchestration Vision Motivation for PoC 1 2 Motivation for PoC 3 Intelligent Closed Loop Architecture Beyond PoC: Academic Research 4 5 Next Steps & Conclusions Questions 6

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

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

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

Closed-loop orchestration

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.

ONAP Intelligent closed-loop Architecture https://wiki.onap.org/display/DW/Policy+GUI

ONAP Intelligent closed-loop

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

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

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

Questions