Denis Murphy

Offering Management

Automating the journey to Cloud Native Intelligent Networks

“Ultimately 5G is about the move to software at the center of the network”

2-Appledore Research Whitepaper, Take a Revolutionary Approach to 5G Cloud Native, June 2020 2

The modernization of networks is key for 5G success and overall network transformation.

Managing environments with both physical & virtual resources

Lack of devops capabilities and skills needed to innovate

Lack of end-to-end orchestration capabilities

Comprehensive full stack automation

Hybrid solution spanning all resources

Adopting a cloud operating model

Challenge Opportunity

4

Poll 1

What do you see as the key drivers for network automation in a cloud native network?

1. Reducing time to market for new services

2. Improving ability to adapt to changing business conditions

3. Reducing OpEx

4. Reducing creation time for faster service innovation

Cloud networking and services for large scale NFV & 5G deployments are complex to manage

Edge Cloud

Micro Cloud

Edge Cloud

Edge CloudEdge Cloud

Micro CloudMicro Cloud

Micro Cloud

K8s restarts broken POD and notifies CP4NA

CP4NA auto configures the restarted POD and notifies any related CNFs of any changes to maintain service

CP4NA manages Network Services and VNFs across heterogeneous Clouds

Complex dependencies across multiple technology layers

OpenStack

Network Fabric

Hardware - CPU, Memory, Acceleration, FPGA

Kubernetes

Hardware design/tuning & VM/Container Placement within a site

Viewpoints

CNF Type PODsVNF Type VMs

internal virtual machine/container

placement, hardware

dependencies and configuration

Logical network design and xNF stitching across sites

VNF1 CNF1VNF2 CNF2

site placement and connecting external

logical interfaces

Network Service point of view

• What site do we put which version of a VNF/CNF?

• How do we fit everything we need for expected performance across these sites and WAN links?

• Figure out which existing network services to bind to?

• How to upgrade from one flavour of a network service to another with no down time?

• When to move an xNF from one site to another?

Hardware tuning point of view

OpenShift NFVIOpenStack NFVI

NodeNode

Network Switch Network Switch

NodeNode

Compute: CPU, Memory, Acceleration & FPGA

Network Switch

RHEL Host

Network Function VM

Network Function VM

Compute: CPU, Memory, Acceleration & FPGA

Network Switch

RHEL

Network Function POD

Network Function POD

RHV

RHEL Guest RHEL GuestContainer

Networking

Telemetry

System Config

OpenShift Config

Hardware accelerated

Provider Networking

TelemetrySystem Config

OpenstackConfig

DefaultHardware accelerated Default

xNF VMs/Containers require specific hardware and tuning to run in a performant manner (or at all).

Tuning parameters include • BIOS settings• NIC parameters• Hypervisor parameters• Operating System kernel

parameters• FPGA parameters

Container VIM Linux Kernel needs to have non-conflicting drivers and modules, e.g. • NIC drivers• Protocol Stacks, e.g.

SCTP/GTP• Container Networking

plugins

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Poll 2

What are the main actors that inhibit, or slow down, the adoption of network automation today

1. High learning curve due to complex technology and multi-vendor environments

2. Resistance of people to process and skill change due to automation

3. Limited ROI visibility/measurability

4. Upfront expenses

Apply Cloud Native techniques and machine enabled automation

Lifecycle Automation

0% 100%

xNF Standard Lifecycle Cloud based tool chainIntent driven Orchestration

• Wrap xNFs with a self contained operational lifecycle

• Natively onboard autonomous CNF Operators

• Focus on modelling the network service rather than programming lifecycles

• Auto reconcile network services to cope with planned and unplanned xNF changes

• Tools to enable automated onboarding and testing of xNFs and network services

• Self service network service/slice design and behaviour testing

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Normalized Lifecycle Modeling

• Maintaining different skills, methods & procedures for different vendors is expensive and slows down innovation

• Approach:

o Reduced complexity through standardized operational lifecycle and tools – standardize for consistency

o Common operational lifecycle for each xNF & Service

o Generate complete Network Service lifecycles using relationships and opinionated patterns

• Outcome:

o Increased levels of operational automation by modeling the end-to-end service with runtime operational requirements in mind

Standardized operations for all xNFs to enable consistent model-driven automation with CI/CD toolchains

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Intent Driven Orchestration

• Manual programming is time consuming, error prone and inefficient

• Approach :

o Design with intent using declarative based models

o Model the service rather than program its lifecycle workflows

o Auto-generate & execute the most efficient steps

o Reconciles the actual and target state of all network cloud stacks

• Outcome:

o Intended Operational State of complex Service maintained automatically

Models the desired service operational state rather than pre-programming workflows

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Service Design & TestingAutomation for the service itself and underlying resources for test, pre-production, and production environments

• DevOps approach is required to automate Network Cloud operational process, reducing complexity and manual effort

• Approach:

o Integrated design and test framework

o Quickly onboard xNF components into an automated CICD lifecycle

o Gain lifecycle visibility before deploying

• Outcome:

o Reduces service design time by up to 80% and diminishes the Network Service operations cost by up to 60% - all while reducing CAPEX upwards of 10%

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Dynamic Service Assurance

• Reducing time to diagnose incidents and avoid outages

• Approach:

o Embrace frequent changes with real time insight

o Fix issues before they become service and customer affecting

o Real time context for rapid resolution

o Go back in time - know what happened, when

• Outcome:

o Quickly isolate problems for faster mean time to repair

Real-time view of network and cloud infrastructures using AI to drive decision making and process automation

Collaborate

Natural LanguageProcessing

Event Processing

TopologyStore

ChatOps

Notification

Tickets, Collab tools

Events

Environment Topology

Probable Cause

Anomalies

• Event Clustering• Seasonal Analysis and Suppression• Weighted probable cause

• AI driven Model selection• Variance Analysis• Dynamic Threshold

Logs

Guide & Resolve

LogProcessing

Metrics

MetricProcessing

Anomalies

• Dynamic Dependency Mapping• Real-time and historical topology analysis• Visualize probable cause

Correlated Event Groups

Run Books

+ Unstructured dataImplementing AIOps – Structured

Closed Loop Automation

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Poll 3

What areas do you see most benefit from infusing AI into network automation?

1. Predictive maintenance

2. Self diagnostic, automatic problem detection

3. Self-healing of networks

4. Intelligent network operations

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Closed-loop Operations

Feedback loop of communication between assurance and orchestration to enable zero touch operations

• Networks are becoming increasingly dynamic and many applications require low latency—often, no time is available for human interactions

• Approach:

o Incident detection and resolution

o AI-driven resolution of identified errors, with further auto diagnostics for unknown errors

o Sense and Respond to issues or opportunities for optimization and select opinionated patterns to execute

• Outcome:

o AIOps to reduce operational expenses greater than 5x; Improve network visibility and customer responsiveness

Network Cloud Stack requires an SRE style operating model

Vendor A PoDs Vendor B PoDs

Fully automated delivery modelHighly scalable & repeatable for each PoD type

…

V/CNF & service definitions

Cloud deployment templates

Deployment

Closed-loop automationMaintenance

Key operating model changesFully software-defined system requires new tooling, processes & skills

Architecture Standard, open virtualization & software components

Ops tooling Intent-based (vs. workflow-based)

Process SRE (Site Reliability Engineering), DevOps

Skills Software eng + admin

Fully software defined

architecture ∞

Designing networks for the cloud with AI and automation

IBM Cloud Pak For Network Automation

Network

Service

Design

Reconcile the actual and target state

of all network cloud stacks

Set of pre-defined lifecycle types with

dependencies and relationships and a

library of opinionated patterns that address

planned and service restoration use cases.

Intent

Orchestration

AI ResolutionD

om

ain

Au

tom

ati

on

Mo

de

ls

Sense and Respond issues or opportunities for

optimisation and select opinionated patterns to execute

events/metrics

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Poll 4

What best describes where you or your client are today on the journey to cloud-based networking?

1. No plans in place or just considering

2. Starting to plan and evaluating solutions

3. In testing/evaluation or initial deployment

4. Already have some implementation in network

Lower Costs

Improves business process and service assurance while lowering operations costs

Exploreand schedule a free virtual consultation with an expert

Get started

Deploy Faster

Accelerates the delivery of networks and services through AI-powered automation

Run Anywhere

Runs on any cloud, anywhere, and manages any network vendor infrastructure

With IBM Cloud Pak for Network Automation, you can evolve to zero-touch network operations with AI-powered automation

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https://www.ibm.com/cloud/cloud-pak-for-network-automation