• Aucun résultat trouvé

Mosaic5G® - Agile network service delivery platforms

N/A
N/A
Protected

Academic year: 2022

Partager "Mosaic5G® - Agile network service delivery platforms"

Copied!
19
0
0

Texte intégral

(1)

Network Service Delivery Platforms

OAI Workshop, Nov. 7-8

th

2017

Navid Nikaein

Communication System Department, Eurecom

(2)

Why Mosaic-5G.io?

 Need for a software-based 4G-5G service delivery platforms for telecom to

 Increase network flexibility

 Add/customize network intelligent through control apps

 Experiment new use-cases and business applications

IaaS PaaS

Host the service

Build the service and open APIs

SaaS Consume the service

(3)

Technology Enablers

Application

MEC

SDN

Network

Infrastructure

Video Optimization

Recommendation System

IoT Gateway

Data Analysis

RESTful API, Message Bus

Edge Packet Service

Application Manager

Radio Network Information

SDN API Library

SD-RAN controller

SD-CN controller

Forwarding Engine

Datapath Driver

LTE eNodeB, NR, NB-IOT

Xhaul transport network

Switches, Routers

NFV

Cloud Infra

(4)

What is Mosaic5G.io?

Mosaic-5G.io was formed to develop, promote, and share an agile network service delivery

platforms

 Transform today’s static RAN and CN infrastructures into extensible, software-based platforms as a service

 Explore new ideas and use-cases for 4G-5G R&D

 Bridge the gap between communication, computing, and data analysis

 Founded by Eurecom in 2015.

(5)

Objectives

Software- Defined Network

Service Delivery Platform

Network Intelligent

Network Applications

Custom

Use-Case

(6)

Mosaic-5G Ecosystem

A Flexible & Programmable SD-RAN Platform

A Low Latency SDN-based MEC Platform

An event-driven juju-based service orchestrator core

A Flexible & Programmable SD-CN Platform

Network function & application distribution Repository

Remotely accessible

experimentation testbed

(7)

Mosaic-5G Ecosystem

(8)

FlexRAN Platform

RAN runtime

Abstraction and programmability of network functions

Extendable RAN APIs

Virtualized resources and state for a slice

RAN realtime controller

Slice state and resources

C-plane SDK

Slice control plane

Realtime and flexible RAN monitoring, configuration, control and programmability

Centralized and/or distributed control

SB-IF interface

FlexRAN control protocol and controller

(9)

Supported FlexRAN API Calls

API Target Direction Example Applications

Configuration (synchronous)

eNB,

UE, Slice Controller  RAN

• UL/DL cell bandwidth, Reconfigure DRB,

• RSRP/RSRQ/TA

• Monitoring,

• Reconfiguration,

• SON  cognition Statistic,

Measurement, Metering

(Asynchronous)

List of eNB, UE,

Slice

RAN  controller

• CQI measurements,

• SINR measurements,

• UL/DL performance

• Monitoring,

• Optimization,

• SON  cognition

Commands

(synchronous) Agent controller RAN

• Scheduling decisions,

• Admission control

• Handover initiation

• Hard Realtime Control,

• Soft realtime control

• SON  cognition

Event Trigger Master RAN  controller

• TTI,

• UE attachment,

• Scheduling request,

• Slice created/destroyed

• Monitoring,

• Control actions

Control delegation Agent Cpntroller  RAN • Update DL/UL scheduling,

• Update HO algorithm

• Programmability,

• Multi-service

(10)

LL-MEC Platform

 Application manager (mp1)

low-latency: CoreAPI, MBus

Elastic: RestAPI, MBus

 Platform (mp2)

Edge packet service

Multi OF libs, OVS

Static and dynamic rules

Radio network information

Real-time control and monitoring

Event manager

 Abstraction

Data plane APIs: OpenFlow protocol

C-plane Radio API: FlexRAN protocol

(11)

Supported LL-MEC APIs

API Target Direction Example Applications

Configuration (synchronous)

MME,

X-GW CN LL-MEC

• UE IP

• Bearer ID, TEIDs,

• X-GW IPs

• Monitoring,

• Reconfiguration, Statistic,

Measurement, Metering

(Asynchronous)

List of eNB, UE,

Slice

OVS LL-MEC

• byte_count, packet_count

• direction, in_port

• duration_sec

• Priority, table_id

• Monitoring,

• Optimization,

Commands

(synchronous) OVS Ll-MEC OVS • Copy

• Redirect

• Analytics

• Programmability

(12)

Network slice template Network slice template

JoX Platform

 JoX Core

NB-IF for Slice templates

Slice controller

Distributed Slice DB

Interface to Juju VNFM

 Plugins frameworks

fast reaction to the underlying network and infrastructure Passthru

 Stores

Local store

Charm store

Network slice template

(13)

Net Store

Network function & control application distribution Repository to recompose the network service across a reusable modules

OAI Charms

Network Service templates

Juju bundles

JoX templates

SDKS

FlexRAN, LL-MEC, and JoX

Control network applications

Monitoring apps

FlexRAN RAN sharing apps (RRM+SMA)

LL-MEC video optimizer app (local breakout+update of video transcoding)

Performance predictions app (RRM_KPI)

(14)

License models

 Mosaic5G Platforms’ licenses

 FlexRAN and FlexCN

FlexRAN Controller : MIT  OSA license

FlexCN controller : Apache v2.0 or OSA license

 LL-MEC: Apache v2.0 or OSA license

 JoX: Apache v2.0 or OSA license

 Store: Apache v2.0 or OSA license

 Contributors

 Recommend OSI compatible license

14

(15)

EXAMPLE UCS

(16)

@MWC, ITU, MobiCom, EUCNC

0 1 2 3 4 5 6

x 104 0

1000 2000 3000 4000 5000 6000

Time(ms)

DL Throughput (kB/s)

DL Rate

eMBB Slice DL Throughput URLLC Slice DL Throughput mMTC Slice DL Throughput

(17)

@ETSI,MWC, MEC Congress, MobiCom

(18)

Success Stories

MWC 2016, 2017 ITU, FG-13, 2016, 2017

Mobicom 2014,2016,2017 EUCNS 2015, 2016, 2017

ETSI 2016, 2017

OPNFV 2016

(19)

Info

 Mail : [email protected]

 Website : mosaic-5g.io (coming soon)

 Twitter: @mosaic5g

Références

Documents relatifs

This paper explains how Service Network Analysis (SNA) can be used to study and optimize the provisioning of complex services modeled as Open Semantic Service Networks (OSSN),

— This paper présents a method for finding the probability distribution f miction for the capacity of a muititerminal network, The capacities of the arcs of the network are

We study both the single-vehicle and multi-vehicle routing problems in this context, i.e., the Generalized Traveling Salesman Problem with Time Windows (GTSPTW) and the

The two distribution strategies considered are indirect delivery paths, where products transit through a single secondary warehouse between their origin and destination without

In the deterministic case no ad-hoc capacity increase is used (when the demand is deterministic), while the stochastic case uses some ad-hoc capacity increase (less than 0.1% of

see also Pedersen and Crainic, 2007; Pedersen et al., 2007) present service network design models with design balance constraints for locomotives.. All the model formulations

The table indicates the number of commodities (|K|), the number of terminals (|N|), the number of time periods in the planning horizon (T), the average available time for delivery

The fundamental contributions of this paper are: (1) to be among the first to address the issue of introducing environmental concerns into freight transportation and distribu-