cognitive network control for 5g · •application layer protocol should be interfaced with the...

1 | Copyright © 2018 Tata Consultancy Services Limited

Cognitive Network Control for 5G

Hemant Kumar RathSenior Scientist,

TCS Research and Innovation,Bangalore/Bhubaneswar

India

Sept 2018

‹#›

Introduction - 5G Networks

5G Use Cases

Managing the Network

Cognitive Control

Middle Layer based solution

Conclusion

Copyright © 2018 Tata Consultancy Services Limited

Use Cases – Vertical Industries Categorization

• Industry process automation, automated production line, supplychain management, warehouse managementAutomated Factories

• Assisted driving, autonomous driving, in-vehicle mediaAutomated Transport

• Robotics Surgery, remote health monitoringHealthcare

• Smart grid, smart meterEnergy

• Real-time inspectionInsurance

• Remote education, skill developmentEducation

3


Use Cases – Vertical Industries Transformation

Robotic Surgery

Modern Warehouse

Modern Industry

Rescue Operation

Remote Machine Maintenance

4


Use Cases - Categorization

URLLC

mMTC

eMMB

5


Understanding the Challenges

• Guaranteed communication with mobility support

• Link, coverage and congestion prediction and handoff between stations

• Moderate to huge data volume - 360 degree stereo cameras !

• Moderate to low packet error rate - 10-6 to 10-10 !

• Extremely low to moderate latency requirement: 10 msec for applications, 200-500 µsecs for machine control

• Cost effective, reliable, stable, scalable and vendor-independent solutions

Transformation Challenges

• Quality control, data analytics, coordination, Inventory control support

• Minimal configuration, per-flow provisioning, parameterization of QoS

• Application layer prospective - technology and protocol agnostic

• Solution for Greenfield as well as existing use cases

Operational Challenges

6


5G, the Answer…

Network Speed

• 10 GB/s – 100 times faster than 4G

Latency

• 1 ms to support Augmented Reality and Tactile Internet

Density

• Very dense, 100000+/km2

Cost & Energy

• Low

IoT support

• Must

Mobility

• beyond 350 km/h

An agreement to set of new requirements for wireless

comm. systems that mature beyond 2020

7


Five G’s of 5G

•10 Gbps or more

Gigabit Speed

•Above 6 GHz

Gigahertz frequencies

•Rapidly adapt to a broader range of requirements and demands

Greater Flexibility

•M2M opportunities for billions of things

Gizmos and Gadgets

•Global race for leadership – device manufacturers, service creators, app developers and operators

Global Competitions

8


5G Realization!

Newer Frequency

bands are to be considered

More and more cells required

Mix technology

will rule

9


Network Topologies in 5G - what is desired

• Forwarding plane – Simplifications, sustainability, performance, unified control policy, guaranteed scheduling

• Centralized control plane – latency reduction

• Three clouds – Access, Control and Forwarding clouds

Decoupling of Control and Forwarding plane in the GW

10


Network Topologies in 5G - what is desired

Divide the control plane with more and more logical units

•Optimized process logic

•Signaling delay can be reduced

•Diverse networks can be realized and easy for standardization

State separated core

•State information (mobility) and processing logic plane can be decoupled

•State information can be centrally available

•Dynamic load management can be made possible

Control Function Reconstructions

Connection parameters will be diverse

•Terminal capability, location and mobility history, request type, service feature, etc.

4G Mobility management protocols GTP and PMIP should go

•Unified mobility management protocols needed

•Mobility as a service, location aware services, etc

• Improved vertical and horizontal handover

New Connections and Mobility Management

11


5G Network Characteristics (1/2)

• Inter-cell coordination

• Improved resource usage

• Different service provisioning

• Different network topographies

Access Plane

• Centralized control functioning

• Globally resource scheduling

• On-demand orchestration

• New Service exposure layer

Control Plane

• Simple gateway functions

• Distributed deployment

• Low latency and high data rateForward Plane

12


5G Network Characteristics (2/2)

• Mesh and ad-hoc networks

• Radio resource sharing

• Customized network and services

• Awareness and treatment

• New network technologies

Access Plane

• Control function reconstruction

• Mobility Management

• Network Capacity Exposure

• Value added services

Control Plane

• Gateway C/U Split

• Mobile edge contents and computing

• End-to-end requirements Forward Plane

Multi-RAT Convergence

On-demand Networking

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Network Topologies in 5G – Key Points

On-Demand Networking

• Network orchestrator plays the major role

• NFV/SDN brings vritualization

Multi-RAT Cooperation

• Intelligent access control and management

• Wireless resource management and Dynamic load management

• Protocol and signaling optimization

• Multi-node and connections

• Spectrum sharing can be thought of

14


Network Architecture for 5G

Service layer architecture for 5G

• Micro service based architecture for auto scaling with access network focus• Multi-operator and

multi-technology support: Hybrid network support –WiFi, LTE/LTE-A, LAA…

• Service based slicing. vs. application based slicing vs. flow based slicing

Vertical business services supt. – apps

with comm. c/s

• Application aware network slicing, virtualization, cloud management, prediction and control

• Viz: Automotive Vehicle: Driving Ast(URLLC), Ent. (eMBB), Sensors (mMTC)

5G Overall Architecture (5GPPP)

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SDNization – Possible Solution

• Adaptive applications and networks

• Should be able to use for existing and green-field solutions

• Dual connectivity – New Radio can be realized

Application layer specific solution to manage both the application and lower

layer protocols

• Independent application layer modules to be defined which are to be used as the core for a new service

• Service or application agnostic modules vs. application dependent modules

• Brings programmability and separation of network with slices – same hardware

• Slicing is possible

SDN vision for control and data extended to

applications

• Application should be flexible to run in different networks & devices

• Networks should be flexible, scalable and easily configurable

• No physically separate network for different application/services

SDNization requires adaptive applications and

networking

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Translating the SDNization (1/2)

• QoS guarantee, compute and communication issues

Understanding the applications/use cases

• Kind of network, protocols, controllability of the network and protocols

• Real-time behavior of the network and protocols

• Capability of the underlying elements

Understanding the network

• Based on the application requirement and network behavior

• Can be real-time or off-line

Taking a decision

• Fine tuning the application, controlling the protocols and provisioning the network

• Deciding the optimal deployment decision based on optimization

Deploying the decision

17


Translating the SDNization (2/2)

• Application parameters can be decided based on the service definition

• Real-time application parameters can be tuned based on the network data

• Device data also plays a role

• Telematic and sensed + synthetic data have different QoS requirements

• Application layer protocol should be interfaced with the cross layer data

• Data collection/feedback – is a key

Network–aware Applications

• Network provisioning can be performed based on the application parameters

• Provisioning includes scheduling, path selection, routing, congestion control, power control, interference management

• Modules to be written which can be interfaced with the applications and appropriate commands can be generated to appropriate nodes/links/path

Application-aware Networking

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Orchestration and Management

• Should be capable of end-to-end orchestration

Fully automated and real-time orchestrator is required

• Multi-domain service orchestration: spanning across admin, operator and user (!!) domains

• Automated analytics and end-to-end FCAPS (fault, configuration, accounting, performance and security) support

• Heterogeneous network and application support

• Self-aware, self-healing, self-management….

Key Functionalities

• 5GPPP – Measure, Analyze, Policy and Execute (MAPE)

• ETSI – NFV Management and Orchestration (MANO)

• MEF – Lifecycle Service Orchestrator (LSO)

• TMF – Zero-touch Orchestration, Operation and Management (ZOOM)

Possible architectures today

19


Cognitive Network Control – Overall Architecture

Decide

Analyze

API Layer

Applications

Application

Sensing

Respond

RSSI, Configuration, congestion, route, link

state, port & flow parameters

Network, Protocol

and Application

state - Logs, Real-

time params

Current State: Features,

synchronized params, QoE

params

Protocol

parameters,

desired state,

iterated

parameters

Configuration, Slicing,

Virtualization, Power, Route ,

control…

KPIs &

App

params

L1-L4

params

for cross

layer opt

Sense

ApplicationNetwork &

Protocols

Device & Network

Layer

SDN NFV Legacy

SADR Framework

• Sensing of Application and

Networks

• Sensing can be triggered or

automatic

Machine Learning

Classification

Feature Extraction

Model Tuning

SDNization can be

achieved through a

cognitive control

framework

20


Cognitive Network Control – Sensing & Analysis

Sensing

• Sensing of device, network and application data

• Probe agents can be used along with the controller for sensing

• Type of sensing

• Active, Passive/Indirect Sensing

• Device & Services

• Browsing habit, location, CDR

• Edge & Core

• KPIs, flow-level, service-level, user-level

• DC & Cloud

• Application specific, Security and privacy

Analysis

• Derived Analysis

• Root cause analysis, understanding the fault

• Predictive & Proactive Analysis

• Expected problem and main causes

• End-to-end big data analysis

• Event pattern and relationship

• Market analysis – possible !!!

• Prescriptive Analysis

• Recommendation for specific action

• Estimation of future action plan

Machine Learning

Classification

Feature Extraction

Model Tuning

Note: Regulatory issues play a role in

collecting the data; all the data may

not be available

21


Cognitive Network Control - Decision

Output

Input

Emulated Nodes

Simulated Network Topology

External Real Network

Applications

Model

Classification

Algorithms/Models

Emulation

Model Validation

To Respond

Sensed &

Analyzed data

Training

Decision – Rule based or Cognitive

• Optimal vs. sub-optimal solution

• Use case analysis is required – use of extended emulation module to recreate the scenario and to form the rule or models.

• These rules or models are to be trained further and appropriate emulation based testing is required for validation

• Any decision to be deployed (as a part of respond framework) has to be validated in the emulator

• Along with the observed data (sensed data), synthetic data generated by the emulation are to be used for modeling, training and decision making

22


• Real-time sensing of network/application/protocols

• Cognitive network prediction & provisioning

• SDNization - SDNized APIs in the south bound

• APIs support for OpenStack and other Cloud orchestrators

Key Features

• Middle layer based solution

• Auto sensing, analyzing, prediction & configuration

• Cognitive control of network and protocol

• Vendor and technology agnostic solution

• Heterogeneous network support

• Emulator based decision making

Novelties

Cognitive Network Control – Our Solution

23


Cognitive Smart Engine Operation – 5GPPP

Monitor the Traffic (eMBB, mMTC, URLLC), Mobility, Radio + KPIs

Analyze the Achieved/Projected performances, Real-time learning

Plan for new RRM Algos, D2D context, Spectrum usage, dynamic slicing

Execute with virtual or physical N/W slicing & orch., SDN & NFV support, Mode selection

24


Cognitive Controller – Solution Architecture

Application

Respond

Analyze

SenseMiddle

Layer

Decision

Network Control Controller

Network

Control AgentNetwork

Devices

• Cloud based application

• SDN based network softwarization to realize network slicing

• Support for HetNets

• Rule based decision making process

• Learning modules – network, usage, QoS, applications

Middle layer based solution

• Applications with all possible traffic type

• Applications with single traffic type – mainly video application, smart city applications (smart meter!)

• Vertical industries – Healthcare, factories, multimedia, automative, energy

Applications & use cases

Emulation

25


What Can be Achieved ? – Multi-tenancy, 5GPPP

26


Multi-tenancy through Cognitive Control, 5GPPP

27


References

1. View on 5G Architecture (5G PPP White Paper), Ver. 2, Jul 2018

2. 5G-PPP, Living Document on 5G PPP use cases and performance evaluation models, 2014

3. Study on Architecture for Next Generation System – Sept 2016

4. 5G – Personal Mobile Internet Beyond What Cellular Did to Telephony, G Fettweis et al., IEEE Communication

Magazine, Feb 2014

5. A Study on 5G V2X Deployment (5G PPP White Paper), Ver .1, Feb 2018

6. 5G Vision, 5G PPP, 2012

7. NGMN Alliance 5G White Paper, Feb 2015

8. 5G Service-Guaranteed Network Slicing White Paper, Feb 2017

9. 5G Services and Use Cases, 5G Americas White Paper, Nov 2017

10. Cognitive Network Management for 5G, Robert Mullins et al., Waterford Institute of Technology, Mar 2009

11. 3GPP TS 38.413: NG Application Protocol (NGAP).

12. 3GPP TR 21.866: Study on Energy Efficiency Aspects of 3GPP Standards.

13. 3GPP TS 23.214: Architecture enhancements for control and user plane separation of EPC nodes.

14. 3GPP TS 23.501: System Architecture for the 5G Systems

15. 5G Network Architecture, A High-Level Perspective, Huawei, 2016

16. Draft Recommendation Y.IMT2020, reqts, “Requirements of IMT 2020 network”

Disclaimer: Some of the figures used in this presentation are taken from most of the above references and these figures are

used only for academic purpose and illustration of the concepts.28

cognitive network control for 5g · •application layer protocol should be interfaced with the...

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