AI Driven Orchestration, Challenges & Opportunities Openstack - - PowerPoint PPT Presentation

Openstack Summit 2018 Sana Tariq (Ph.D.) – TELUS Communication

AI Driven Orchestration, Challenges & Opportunities

AI/ML Driven Orchestration Closed Loop Orchestration and Dynamic Policy Service Orchestration Operational Challenges Service Orchestration Journey

Agenda

NFV and Orchestration Journey…

Q2 2018 2016 2017 2018 2019 2020

IoT and OTT Services/ User-defined Services/International customers

2021 2022

PNFs Building NFV Cloud Applications Onboarding Increased Maturity OSS/BSS interlock, evolution of customer Portals, inventory compliance Automated Assurance AI driven/managed

• perations, capacity

and applications 5G IoT customers defined services through customized user portals AI driven/managed services (advanced) Increased Maturity of Catalogs/Templates/Blueprints to deliver software defined services

NFV Telco Cloud - is different …

NFV Telco Cloud

APIs Reliability Self serve Automation Scalability Standards based Secure Cost efficient 24x7 availability High Throughput Low Latency Fast and agile

SDN Controllers

DC WAN

OSS/BSS

Ticketing Alarms Analytics Billing

End to End Orchestrator VIM EMS

EMS 1

VNFs

VNF 1 VNF 2 VNF 3 VNF n

NFVI

EMS n WAN

VNFMs VNFMs Service Assurance

EMS 2

NFV and Orchestration Journey…

• Building robust cloud infrastructure
• Virtualizing Applications
• Provisioning workflows
• Assurance
• Automated scaling
• Traffic steered through SDN network
• Monitoring through holistic service

assurance

• Integration with OSS/BSS
• E2E Service Orchestration plays are

major “orchestration” role

Vendor lock-in/proprietary plugins Depends on Company size R&D driven roadmaps Higher licensing cost Trusted support model Vendor agnostic/shared community plugins Depends on community participation Community driver roadmaps Significantly lower costs Self/Community support

Commercial Opensource Opensource SI Vendors

Orchestration: Commercial or Open Source

Orchestration Functional Elements

SERVICE ORCHESTRATION SUCCESS

Cloud

Robust Cloud supporting automation features, APIs, elasticity etc.

SDN Network

Complete softwarization of DC and WAN for on-demand creation of services

Analytics

Robust analytics cross-functional domains for scaling, healing &

• ptimization for cloud and services

DevOps

Effective DevOps culture and support for short time-to-market and efficiency targets

Data Models

Homogeneity across stacks for consistent data models for seamless integration, reusability and abstraction

Chaos of Multi-Vendor Multi-Domain…

S-GW P-GW MME vEPC ... SD-WAN vIMS SBC S-CSCF TAS I-CSCF ... Enterprise NFV Cloud

NaaS/OTT Voice OTT 5G IoT VoD

E2E Service Orchestration

Customer facing services

Software Defined Service Operational Challenges…

Package and distribution

3

Orchestration (runtime)

4

Service Design

1

Troubleshooting

6

Assurance

5

Orchestration (testing in a sandbox)

2

Launch

Offline in lab Services Operation in real-time

Debug Service failure

Policy Engine Analytics BigData/Hadoop E2E Service Orchestration LB vFW vFW Service Orchestration VNFM VAS Collector

Closed Loop Orchestration Static…

Closed Loop Orchestration AI/ML…

BigData/ Hadoop

Inventory

Workloads Onboarding Resource Orchestrator Testing & Validation APIs Adaptors SDK Life Cycle Management Policy Service Definition Workflows

Service Orchestrator

Analytics

Conditional Rules Cloud Services Security Network Mapped Actions Predictive/proactive Inputs Reactive Inputs

AI/ML Orchestration

• Energy optimization
• Capacity optimization
• Faults prediction & healing
• Fast troubleshooting
• Traffic optimization SDN

controller

• QoS based routing
• Proactive/predictive threat

identification

• Closed loop decisions to

attacks mitigation

• Closer to the edge
• Service Healing
• Service optimization
• Differentiated QoS

Customer Experience Cloud Optimization Network Optimization

• 5G Services/ IoT

increase in traffic volume and patterns

Data Surge

• Reactive decisions
• False Spikes
• Too many policies

& conflicts

Static Policies Security

• Strategy to develop

dynamic policies and testing

Dynamic Policy

• Robust Analytics

framework for feeding AI/ML systems

Analytics Model WHY? HOW? What?

START PROCESS STEP 1

Problem definition

STEP 2

Gather real, sizeable training data

STEP 3

Prepping the data: cleansing, normalization, feature engineering

STEP 4

Training phase: robust training environment

STEP 5

Testing phase: Experiment with Models, Pick best Models and create feedback Loop

STEP 6

Evaluation, predictor improvement and re-training

RESULT

Building an AI Application

?

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AI is becoming easier…

Data Scientists needed, higher complexity Python libraries – lower complexity ONAP leverages SPARK, Mlib, MALLET, WEKA etc. H2O, TensorFlow Predictors & Models developed by shared community effort + data

Raw AI/ML ML Libraries AI Projects

Acumos

• Better Analytics Engines,
• Better data
• rganization
• Large volumes of

managed Data

Computing Power

• Low Cost
• High Power
• GPU, NPU

availability

Accessible Data

ONAP Intelligent Closed Loop Architecture…

Design Time SDC (Service Design Creation) CLAMP (Closed Loop Automation and Management Platform) GUI that generates TOSCA Run-time DMAAP POLICY EXECUTION ENGINE DCAE Policy Execution Hadoop Synthetic Data (Extension) Ceilometer collector Service Orchestration SDN-Controller APP-C (GVNFM) Ceilometer Ceilometer AI/ML (Python Library) TOSCA Policy1 TOSCA Policy1

Condition Cloud Region1 CPU Utilization >80% Action Shift VNF/ workload from Cloud Region 1 to Cloud Region2

Policy1 Policy1 VNF1 VNF2 Cloud Region 1 Cloud Region 2

ONAP Policy Execution Flow…

Ceilometer Analytics AI MS Policy MS Ceilometer Collector Hadoop Cluster DCAE DCAE DMAAP POLICY EXECUTION ENGINE Service Orchestration SDN- Controller APP-C (GVNFM) SDC CLAMP Cloud Region 1 Cloud Region 2 1 2 3 4 6 7 8 9 10 11 12 5 13 14 15

AI/ML Orchestration Industry Verticals

Study of VNFs performance requirements (MME, S/P-GW, vSBCs etc.) NSD and VNFD Package description to associate color tags for VNF types to support these use-cases DCAE: Dynamic Policy and Cloud

• ptimization use-cases

Develop ML Models for cloud

• ptimization use-cases

Enhance ceilometer & support cloud optimization configurations

BigData/ Hadoop/ Analytics

Customer/Resource Facing Portal

Customer with IoT Traffic (Dependency: Closer to Edge Location)

API Mediation Inventory

Workloads Onboarding Resource Orchestrator Testing & Validation APIs Adaptors SDK Life Cycle Management Policy

Inventory

Service Definition Workflows

Service Orchestrator

Policy Rules

· Initial Placement · Cloud Optimization related actions · Customer QoS related actions

NFV Cloud

Customer with real-time Voice Traffic Dependency: Performance/ TTR Customer with 5G Services: real time, high bandwidth Dependency: Performance/ TTR Customer with real-on-demand viewing Dependency: Performance/ TTR/ throughput Customer with off- line downloading Dependency: Throughput only Edge POD: High Bandwidth, Closer to customer Zone1 PODs Optimized for Efficiency/ throughput Zone2 PODs Optimized for Low Latency/HTTR (real- time traffic) Zone3 PODs Optimized for Low Latency/HTTR (real- time traffic)

Sana Tariq (Ph. D.) Sana.tariq@telus.com