CONFIDENTIAL Designator

1

How RAN and Core are evolving

Journey to Cloud

Native - 5G

Azhar Sayeed

Chief Technologist

asayeed@redhat.com

2

These slides are created for the strict purpose of discussion only. Red Hat Inc. does not plan or

commit to offer any functionality described in this material as part of its portfolio of products.

Red Hat Inc. does upstream open source software development work and will continue to

participate in upstream activities and communities relative to technologies described in this

presentation. This does not imply Red Hat will or have products associated with those software

communities Nor is Red Hat committing to having such offerings in future.

Red Hat Inc. does not commit to or subscribe to any opinions, statements regarding third parties

described in this presentation. Any disclosures associated with third party companies are a

matter of conjecture and should not be taken as known facts or stated strategy of those third

parties.

DISCLAIMER

AGENDA• Background• RAN, Edge and Core Solutions for 5G

– Architecture– Requirements of Infrastructure

• Cloud Native Factors• Examples

– Customer Example– VCO 3.0

• What’s Next• Summary

3

AGGREGATION & DISAGGREGATIONThe swing of the pendulum

4

Aggregation• Main Frame• Shrink wrapped• HW/Software bundled• Closed EcoSystem• Monolithic Software

Dis-Aggregation• Intel - x86• Component integration• Un bundled• Open EcoSystem• Microservices Software

Argument for Aggregation• Speed• Performance• One Neck to Choke• Better User Experience• Support Models

Arguments for Disaggregation• Independence• Flexibility• Control of your destiny• Innovation• New Services

IN SEARCH OF A NEW EQUILIBRIUM STATE

5

Time

State

t1 t2

Equilibrium State

Equilibrium State

MonolithVertically Integrated

Multiplecomponents

Shrink Wrapped

Dis-aggregation Open-Source

NFV -VM

App - RefactoringRe-Aggregation

CNF/SDx

Value Migration

6

Hardware and Software all

bundled in one

Cables

Software

Cables

Hardware

Application

Cables

Boards

NICs

Processor

Storage

Proprietary OS

Application

Cables

Faster Boards

Faster NICs

Faster Processor ..xx

Faster Storage

Proprietary OS

Cables

Multi Function Boards

Even Faster NICs

Multi-core Processor ..xx

Even Faster Storage

Linux OS

Application

Cables

Multi Function Boards

Smart NICs

Faster and Multi-core Prcssr ..xx

Flash Storage

Linux OS

Hypervisor

Application

Cables

Multi Function Boards

Smart NICs

Multi-core Processor ..xx

Flash Storage

Linux OS

User space

Application

Application

Application

Application

Application

Application

Application

Application

Business Process/Integr

ation

7

RAN, Edge and Core

LTE RAN Evolution and 5G RAN

Mobile Broadband

Ethernet or Fiber Fronthaul

Compute Nodes at - RU (Radio Unit, DU (Distributed Unit), CU (Centralized Unit) and Edge Compute for Application in addition to packet core and DC

5G RAN

BBU/vBBU

CU

Functional Splits with LTE Radio

DU

DU

RU

RUFronthaul Midhaul

BackhaulDU

LTE vRAN

Regional or Core DCFor LTE EPC or NG-

Core (5G)Edge

Compute

Edge Compute

CU

5G New Radio

RU

PACKET CORE EVOLUTION 4G/LTE TO 5GMOVE TO CONTAINERS

9

eNB

MME

SGW PGW

HLRHSS

OCFPCRF

CP CP

DP DP

Box / Device centricLTE/4G

5G RAN4G

RAN Localized GW or Central GW Data Plane

Control Plane – Mobility, Sessions & Service Management

PGW HSSPCRF

5G - Cloud Based

OpenStack or KVM OpenStack

vBBU vMME vSGW vPGW

vPCRFvHSS/HLR

CP-DP SeparationUPF is controlled by AMF and SMFData plane extensibility

Infrastructure Requirements

11

5G Attribute Result Infrastructure Requirement - Software

FWA (Fixed Wireless Access) High Speed Broadband Service based on mmWave band spectrum

Performance, throughput – OSP and OCP, Smart NIC, FPGA support

NG-Core Next Generation Packet Core for 5G –based on Microservices and Containers

Orchestration of Containers - Kubernetes

High Frequency Spectrum with more spectrum bandwidth

High Speed Residential Broadband service => Instant Subscriber acquisition

Hardware Management, distributed computing

Massive Scale IoT Services => Hybrid Cloud Hybrid Cloud => Middleware, Messaging, API Management

Control-User Plane Separation Flexible placement of traffic exit points allows Edge compute for application optimization

Edge Compute foot prints, infra placement and management and assurance framework

Low Latency Services (URLLC) Drones, Autonomous Vehicles and Holographic calling

Low latency processing of information => RT Kernel, RT-KVM

Slicing Virtual Operators, Private Mobile Broadband, Distinct grades of service

Distributed Cloud deployment models, Infrastructure partitioning, multi-tenancy, Networking and VPN support

4x4 MIMO, Beam Forming Higher throughput per user on Macro cell and small cell

Higher Aggregate throughput => Smart NIC, FPGA etc

Flexibility, reliability, serviceability Disaggregation of software components, cloudiness

Standardized catalog, registry of components

• CNF - There aren’t many that exist today– Handful of vendors working on developing network functions to

microservices/containers• Unique requirements

– Multi-interface– Traceability– Service chaining– IP address management (flexibility)– Fast data path - acceleration– High Availability Reference Architecture– Common SDN environment– Real-Time Kernel for specialized Edge use cases– Multi-Cluster, Multi-Site, Private and Hybrid - Same experience expected

Cloud Native Network Function Challenges

12

CONFIDENTIAL Designator

Cloud Native

Design Factors

13

Factors influencing cloud native design

• Distribution of functionality, applications• Scale up and scale out => Cloudiness factors• Disaggregation and state management• Operations knowledge codification - for automation and

life cycle management• Instrumentation, telemetry and assurance• Orchestration and Management - Lifecycle and Day 2• Security

14

DISTRIBUTION & MULTI-CLUSTER ENVIRONMENT

15

Independent or stretched K8S clusters (1 node to n nodes remote sites)

Agg or Core Network

Far Edge

Edge AppvCPEStack or Multi-Tenant

RH

DU

CU

Metro Ethernet

DU

DU DU

DU Pool

Independent or distributed Kubernetes Clusters

Regional or Centralized Data Center

with NG-Core or NSA LTE Core

Near Edge

DISTRIBUTED & MULTI-CLUSTER - PRIVATE & HYBRID

https://github.com/kubernetes/community/tree/master/sig-multicluster

OpenShift Clusters c1 through c7

c1c2

c7...

Cluster Registry CRD

Single Source of Truth

Federated API

Base Federated Resources

Substitution Preferences

Substitution Outputs

Placement Preferences

Placement Decisions

Schedule and Reconcile

Auxiliary Resources

FederatedDeploymentFederatedSecretFederatedReplicaSetFederatedConfigMap

Bonus: Federate any CRD without writing code

$ oc get clusters$ openshift-install launch overrides:clusters:- clusterName: c1

replicas: 5- clusterName: c3

replicas: 10- clusterName: c7

replicas: 15

Scale Up● Exposing Hardware functionality for workload optimization (Node feature discovery, CPU

Manager)● Assigning pools of cores to specific threads vs thread pinning to specific cores

○ RT threads - deterministic behavior○ non-RT threads

Scale Out● Re-usability and load distribution

○ spawning new instances○ automatic management of component bundles - think service mesh

● Independence and abstraction from hardware/infrastructure adheres better to cloud principles

● State less

Scale Up and Scale out - CloudinessResource pinning vs dynamic allocation

17

Human operations knowledge in software - to reliably manage an application

For Builders and the community

● Easily create application on Kubernetes via a common method

● Provide standardized set of tools to build consistent apps

For application consumers and Kubernetes users

● Keep used apps up to date for security reasons and app lifecycle management

● Consume of cloud-native / kube-native applications more secure and easier

OPERATOR FRAMEWORK

Operations knowledge codification for automation and lifecycle management

EXTENDING ASSURANCE MODEL

19

Edge Collector

Edge

EdgeC

CCompute

AMQP Message Bus

Decision Engine

Regional or Central Data CenterEdge Sites

Collectdand/or

PrometheusAnsible Tower

Ansible play book Execution

PrometheusTime Series DB

AMQP Message Bus

• Automating placement of workloads that meet the criteria• Monitoring Change• Proactive and reactive action

• Rules based closed feedback loop• Requires all layers of stack to communicate• Correlation

CONFIDENTIAL Designator

Deployment/POC

Examples

20

GC Edge Clou

d

DEPLOYMENT– REAL CUSTOMER EXAMPLE

Peering

DC

The Internet

Pre-Agg Agg. IP Core

RIU

Internet

Public Cloud/OTT

NFVI SW NFVI SW

vDUvCU

SAEGW-U

vCU -Small Cell

SAEGW-C/U vIMS

PCRF, HSS, OCS

vCDNOTT CDN

CO-LO

RIU

NFVI HW NFVI HW

Apps

Partner Apps

Gi FW/NAT

MANO (VNF-M and Orchestrator)

eCPRICPRI

VCO 2.0 – ONS POC

SDRSGW MME HSS

PGW PGW

SD

VPN Internet

GiLAN

Internet

RU DUCU

Baremetal NodesManaged by OpenStack

Amsterdam Stage

Jumphost

OpenStack

CONFIDENTIAL Designator

Looking ForwardWhat’s next?

23

Unified foundation, hybrid interoperability

KUBERNETES NATIVE INFRASTRUCTURE

PHYSICAL

APP APP APP APP

VIRTUALPUBLICCLOUD

VM CC C

24

KUBERNETES NATIVE INFRASTRUCTURE ARCHITECTURE VISION

Kubernetes Container Orchestration (Apps & Infrastructure Services Lifecycle Management)

Linux Container Host on Bare Metal Servers (Immutable, Lightweight)

App

Automation

Container Registry

App & Infrastructure Services

On-premises Data Center(s)

Sof

twar

e D

efin

ed S

ecur

ity

DNS, Load Balancing

Container

VMs

App

Container

Functions

App

Service Catalog

Identity & Access

Management

Software Defined Compute Software Defined Networking Software Defined Storage

Monitoring

Cost Management

Public Cloud(s)

Container

25

Server Less Computing

Assumptions that computing, storage and networking are a commodityInfrastructure divorced from applications and servicesFunction aaS – FaaSTriggered computing

SUMMARY

• Cloud Native holds key to scale, distribution, efficiency as we build software for 5G• Careful disaggregation of components to utilize the hardware and infrastructure

without losing the cloudiness• As you make the applications and network functions cloud native, think about state

management, infrastructure independence and abstractions• Instrumentation and telemetry key to better assurance overall• Infrastructure components are more or less ready to start on boarding the cloud native

network functions (CNFs)• Careful planning wrt components drives better efficiencies - state management,

immutability etc• Contribute upstream

27

CONFIDENTIAL Designator

linkedin.com/company/red-hat

youtube.com/user/RedHatVideos

facebook.com/redhatinc

twitter.com/RedHat

28

Thank you