PoC#2: Elastic Network Slice

Management

▪ Nicola di Pietro, CEA-LETI, France

▪ Marco Gramaglia, UC3M, Spain

▪ PoC Team: UC3M, CEA-LETI, Huawei,

Telecom Italia, Samsung UK

2

Outline

▪ Service description

▪ Implementation Architecture

▪ Implementation Details

▪ Showcase

3

Scenario

▪ Palazzo Madama Museum, Turin Italy

▪ It was the first Senate of the Italian Kingdom

▪ The Palazzo Madama houses the Turin City Museum

of Ancient Art.

▪ A 5G scenario is deployed here

▪ Fondazione Torino Musei, i.e., the 5G Vertical

supports the use case developed in this Poc

4

The Services

▪ 360 degrees Video Stream from the MadamaReale room

▪ 3D Virtual Reality reconstruction of museum exhibits

▪ Multi-user haptic experience using 5G multi-slice network

▪ 3D Avatar representation for each user

▪ Exhibit restoration interactive scenario requiring low-latency network

▪ Guide user assists Visitors to fulfill the tasks through VoIP communication

▪ Collaborative manipulation of objects demonstrates latency issues

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5

Network Slices

▪ The two services are offered through two network slices

▪ eMBB for the 360 video

▪ URLLC for the VoIP and the haptic interaction between the guide and the

tourist

▪ The infrastructure comprises a radio site, an edge cloud and a central cloud

Orchestration

Transport Network

Central Cloud

360 Video

Server

VR Server

5G NR

gNB-DU5G NR

gNB-CU

5G Radio Link

UE

Edge Cloud

6

Setup

▪ The testbed spans three rooms of

the museum

▪ New 5G Radio functions are

implemented

5G UE (Guide)

360° video server

360° VR streaming

camera

HDMI

Oculus Rift

Oculus TouchVR App client

5G UE (Tourist)

Oculus Rift

Oculus Touch VR App client

HDMI

USB

BBU

RFU

GbE

GUI

Orchestration Framework

Haptics server

GbEGbE

GbE

BBU

RFU

5G Radio Interface

7

Implemented enhanced 5G Functionality

▪ Network Slice Blueprinting and Onboarding

▪ VNF re-location due to latency

▪ VNF scaling due to CPU utilization

▪ Network Slice Admission Control

8

Reference Architecture

▪ We take as a reference

architecture a mixture of 3GPP

and ETSI

▪ Main objectives, study the

interactions between these

modules

– 3GPP management – ETSI MANO

– ETSI MANO – ETSI ENI

CSMF

NSMF

NSSMF

NFVO

EM (Controllers)

VIM

NFVI

VNFSPNF

3GPP

ETSI

ENI

VIM

VNFM

9

Network Slice Blueprinting and Onboarding

▪ We implemented the 3GPP SA5 Network Resource Model for the Core

Network functions in the testbed and included them into the Infrastructure

Manager

▪ Relevant KPIs:

– Service Creation Time: ~ 7 minutes to build the two slices

10

Re-location and Scaling

▪ We implemented a KPI monitoring to assess

the system health during time

▪ Especially, we monitored the E2E delay in the

URLLC slice and the CPU consumption on the

eMBB

▪ Two actions:

– Re-location of the URLLC VNFs to the edge cloud in

case of excessive latency

– Scaling of the UPF VNFs due to CPU load, according

to the algorithm in [1]

[1] 5G MoNArch D4.2 “Final design and evaluation of resource elastic functions”

11

AI algorithms

▪ Due to the limited size of the testbed, we trained our

algorithms on synthetic data coming from larger size

evaluation data

– For the CPU consumption we leverage extensive measures of

computation load coming from [2]

– For the admission control algorithm, we employed the

synthetic dataset generated in [3]

– The VNF relocation is performed on delay thresholds set

according to the application requirements

▪ Then we focused on the implementation of the

interfaces between the AI algorithms and the ETSI

NFV MANO[2] 5G MoNArch D3.2 “Final overall architecture”

[3] D. Bega et al “A Machine Learning approach to 5G Infrastructure Market optimization”, in IEEE TMC, 2019

12

Implementation Architecture

▪ We leveraged Open Source software

– OSM version 4

– OpenStack

▪ And extended it with ad-hoc Python software

VNFM

NFVO

VIM

Transport

Network

vCPU vRAM vDisk

Virtualization Layer

CPU RAM Disk

NF

VI

uVNF

uVNF uVNF cVNF

cVNF cVNF

B

SB

A

13

Technical Enablers

▪ VNF re-location and scaling are performed through a novel software

implementation paradigm for VNFs

▪ Instead of moving the entire VM (as done by state-of-the-art-solutions) we only

move the context between VNFs

Virtualization Layer

Virtual environment

VNF

Context

Execution

Virtualization Layer

Virtual environment

VNF

Context

Execution

Old Infrastructure New Infrastructure

Context Relocation

14

Showcases

▪ The system was showcased twice

▪ On May 22nd-24th in Turin

▪ On June 17th, 21st in Valencia

https://www.youtube.com/watch?v=L-5XzBvAZyY

https://www.youtube.com/watch?v=hLCkgdOhVJ4&t=8s

15

Conclusion

▪ We successfully demonstrated the feasibility of the usage of AI for network

management

▪ We confirmed that some important 5G System KPI goals can be attained using

our technology

– Service Creation Time

– Reduced Latency

▪ The system has been successful also from the exploitation point of view

– The Turin Municipality asked for a permanent setup of the PoC

▪ The possible interfaces between MANO and ENI had to be experimentally

implemented

– This will provide feedback for the future work on Release 2 of ENI’s architecture