Internet of Programmable Things for In-Network Data Analytics

Internet of Programmable Thingsfor In-Network Data Analytics

All Rights Reserved by Akihiro Nakao, 2018 1

The University of TokyoAkihiro Nakao

All Rights Reserved by Akihiro Nakao, 2018

Marriage betweenSoftwarization and Data Analytics

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• Flexible infrastructure for sensing/analyzing/learning traffic in data plane

Data Plane ProgrammabilityIn-Network Machine Learning

• Data analytics for complex actuation (automated agile operation) human may not achieve


Mobile Network Prediction for 2021Visual Networking Index (VNI)

The number of mobile users

The number of mobile terminals connected

7x

In 2021, mobile traffic will amount to 48.3 EB per month1EB = 1018B

Global increase in mobile network traffic

Cisco Visual Networking Index

Global annual data center traffic 1ZB=1000EB = 1021B

Global increase in data center traffic

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Data Type / Field Project Summary

Location Information NTT Docomo“Mobile Spatial Statistics”

Provide population statistics from the anonymized location data of mobile phones

Automobile Probe Toyota“Telematics Service”

Provide traffic information and statistics generated from telematics data for improving traffic congestion and public safety

Automobile Probe Sony Assurance Inc.“Telematics Insurance”

Analyze customers’ telematics record and provide cash back for safety driving

Medical Information NTT Docomo Health-Care”Moveband3”Omron Health-Care“Wellness Link”

Provide services for improving health and life style by visualize and analyze activity data obtained from wearable smart wrist bands.

Financial Information HitachiFinancial API Service

Enable personal asset management across multiple financial accounts

http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h29/pdf/n2100000.pdf

Utilization of Mobile / IoT Data

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WP1�Coordination & Management (AALTO, UT/NESIC) WP6�Promotion and Standardization (All Partners)

Multi-RAT Front/Backhaul Core Network Data-center Network

Sensor Device Network

Network Resources

Computing/Storage resources Mobile Edge Computing

Terminal/ Device Data-center

WP2�Network Softwarization Architecture (All Partners)

WP5�Federated Testbeds and Experiments (UT, WU, NESIC, AALTO, Ericsson, Orange, FOKUS)

Data center Base station

WP4�E2E Slice Orchestration (UT, KDDI, HITACHI, AALTO, Ericsson, Orange)

Orchestration

Creation of specific slice for each service

Advanced ITS Slice

WP3�Network Slicing Mechanisms (UT, WU, NESIC, Ericsson, AALTO, FOKUS, Orange)

CDN/ICN Slice

IoT/M2M Slice

10-100Gbps broadband/new protocol oriented

1ms Low- latency

106 devices/km2 oriented

Operation of virtual network for different mobile services

Operation of virtual network for different mobile services

5G/IoT Service Centric Network Slicing Control and Operations over Multi-Domains and Multi-Technologies Future Infrastructure

10-100Gbps broadband/new protocol

1ms Low-latency

106 devices / km2 oriented

R&DStatementandGoals1. WP1: Coordination and Management

Project management through iterating PDCA cycles

2. WP2: Network Softwarization Architecture Development of unified E2E Mobile Infrastructure Architecture enabling multiple hundreds of slices instantiation

3. WP3: Network Slicing Mechanisms Development high-performance and highly programmable C/D-plane execution platform Development of ICN, advanced protocols, over slices

4. WP4�E2E Slice Orchestration Development of distributed schemes and platform for achieving scalable operations and management

5. WP5�Federated Testbeds and Experiments Technical integration and federation of technical assets of WP2-3, and PoC systems development and evaluation

6. WP6: Standardization Contribution and leadership in Global SDO, e.g. ITU, 3GPP

TechnicalGoalsStandardization and Verification of E2E Network Slicing Technologies through EU/Japan Collaborative R&D efforts (1) Network Softwarization and E2E

Network Slicing Architecture (2) Data Plane Programmability and its

Advanced Networking Protocols Instrumentation

(3) Scalable E2E Slice Operations and Managements (Orchestrations)

Blue-PrintofE2ENetworkSlicing

Anetworksliceforeveryservice!

EU-Japan Jointly Funded Project on 5th Generation Mobile Network (PIs: Akihiro Nakao@Utokyo and Tarik Taleb @Aalto University)

5G!Pagoda is funded by the European Commission’s H2020 program under grant agreement n° 723172.

EUJ-01-2016 - 5G – Next Generation Communication Networks

EU Total cost:EUR 2.2MJP Total cost:225 M JPY

Funding Size

Duration: 3 years(2016-2019)

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https://cordis.europa.eu/programme/rcn/700192_en.html


https://www.u-tokyo.ac.jp/adm/fsi/en/sdgs.html

UTokyo FSI promotes SDG-oriented projects in a wide range of fields throughout the University, and showcases them as actions taken by the University as a whole.In particular, in regards to collaboration with the industrial sector, the University utilizes the SDGs as a basic common vision for new business growth.

Sustainable Development Goals (SDGs) as Common Vision

As of 2018/4/5, 170 SDGs projects have been registeredhttps://www.u-tokyo.ac.jp/adm/fsi/ja/projects.html

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SoftwarizedProgrammable UE/Sensors

SoftwarizedProgrammableeNB/EPC/MEC

“Softwarization Everywhere”= E2E Programmability

SoftwarizedCloud Data Centers

DataAnalyticsPossibility

DataAnalyticsHeavily Conducted



SoftwarizedProgrammable IoT GateWay

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Sliceable Software Defined Data Planes

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Control-PlaneElements

Network Applications

ProgrammableData-PlaneElements

Applications

Control Plane

Data Plane ProgrammableData-PlaneElementsProgrammableData-PlaneElementsProgrammableData-PlaneElements

AI/ML

AI/ML

• ML Offload• Autonomous OAM• Annotation• Sampling• Characteristics Extraction

• Deep ML• AI OAM


In-Network Machine Learningfor Application Identification

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C C A A

CC A C

CDAFCA C

AC A

AC

D A

DAA

CDA FCA C C C C

A C

E C

CDA FCA C

CDAC

A

A C

CDAC

AA

S N P M

DC

IV R LWTU OGW

CE A A CAD CDA

CE A

C C

JHPCN Project JH170041-NWH @ NakaoLabThe Joint Usage/Research Center for Interdisciplinary Large-scale

Information Infrastructures

C



Application-Specific Bandwidth Control(FLARE and P4)

Youtube (mediaserver): 3Mbps

android.browser: 500kbps

Chrome 2Mbps


Association betweenLow-Level Operation Data

andHigh-Level Application Identification

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Softwarized Base Station For Data Analytics

RRCPDCPRLCMACPHY

eNB

L1

L2

L3 CPU

DSPFPGA

Offload complex processing To FPGA and DSP

RRCPDCPRLCMACPHY

CPU

VM

eNB EPC

VM

SP-GWMMEHSS

MEC

VM

Sharing H/W resources among eNB, EPC and MEC

service

SoftwarizationOf all digital

Signal processingn Challengesü Processing power for complex data handlingü Stable operation especially fluctuation in latencyü Sharing computational resources with network function virtualization

Conventional Approach Our Approach

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OAI Field Experiment on Hongo CampusCollaboration with Fujitsu

�

�

�

��

Aki Nakao,, “R&D on End-to-End Network Slicing”, OAI Workshop Paris 201715

Experimental License @1.7GHz



LTE Softwarized Base Station

Attach to LTE

Voice Call

Video Streaming Data DL

ü 5510 ü 2ü 2ü 5 45 + 1 4 5 + 1

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Preliminary Experimental Results

DL: 67.27MbpsUL: 13.88Mbps

20MHz5MHz


10MHz


• The experimental network is deployed on LTE-FDD Band3 (1.7GHz) to avoid interfering with other carriers (Docomo/au/softbank). • Throughput was measured with SpeedTest Application running on a Fujitsu’s Arrows M04 Android phone.

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Collecting Operation Data in Real Time

n 0 4 0 0 44 04 04

0 40 04 4 0 4 4 04

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GDPR

What constitutes personal data?Any information related to a natural person or ‘Data Subject’, that can be used to directly or indirectly identify the person. It can be anything from a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer IP address.

22https://www.eugdpr.org/eugdpr.org.html


50% of citizens share data by 2019: Gartner

Predicts 2017: Government CIOs Are Caught Between Adversity and Opportunity

Gartner predicts that by 2019, 50 percent of citizens in million-people cities will benefit from smart city programs by voluntarily sharing their personal data.

https://www.canadianunderwriter.ca/keyword/predicts-2017-government-cios-are-caught-between-adversity-and-opportunity/https://www.gartner.com/doc/3510217/predicts--government-cios-caught

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Challenges• Flexible (data plane) Infrastructure• Machine Learning Offload• Autonomous OAM• Annotation• Sampling• Characteristics Extraction• In-Network Machine Learning • Sensing / Inference without privacy violation• Operational Data• Traffic Data• Social Network Application Data• Viable Use Cases

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Internet of Programmable Things for In-Network Data Analytics

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