CLOUD-NATIVE NETWORKS FOR THE ADVANCEMENT OF AI/IoT

0Copyright©2018 NTT Corp. All Rights Reserved.M.Kawashima at IEEE CloudNet 2018
CLOUD-NATIVE
NETWORKS
FOR THE ADVANCEMENT OF AI/IOT
October 22 2018,
Masahisa Kawashima
Software Innovation Center, NTT

1Copyright©2018 NTT Corp. All Rights Reserved.
M.Kawashima at IEEE CloudNet 2018
WHO I AM
MASAHISA KAWASHIMA
Started my professional career with developing
SDN controller software for ATM packet networks
Working in NTT Group for 20+ years
Bridging between R&D and businesses
1997-2000: CDN for Fiber Broadband Users
2003-2006: Anti-SPAM
2005-2006: Open Source Software for Enterprise IT
2008-2009: Marriage of Fiber and Mobile Broadband
with Femtocells
2009-2012: Wi-Fi Broadband for Smartphones
2013-2017: SDN for Enterprise Cloud, SDWAN
Currently serving as the Head of NTT SIC

WHO WE ARE?
NTT Software Innovation Center
AN NTT R&D ORGANIZATION
ｗith “IT UNIVERSITY HOSPITAL”
DIGITAL TRANSFORMATION & IT INFRASTRUCTURE
Machine/ Deep Learning,
Data Management,
Agile Software Development
Cloud/Edge Computing,
SDx, DB, Linux OS
273 IT PROFESIONALS
1600+ code commits to OSS projects
35 core developers/maintainers in OSS projects
8 paper acceptance in rank A conferences
(in the last 12 months)
Copyright©2018 NTT Corp. All Rights Reserved.

“IT UNIVERSITY HOSPITAL”
◼ IT professionals engaged in both R&D and technical support services
◼ Tier 4 technical support for NTT Group system integrators
NTT GROUP
COMPANIES
Internal
IT
Departments
CUSTOMERS
NTT GROUP
SYSTEM
INTEGRATION
COMAPNIES Solution
Business
Departments
NTT OSS CENTER in NTT SIC
technical
Support
R&D
Application Development Framework
Validated Vertical Stack
Maintenance Information, e,g. patch
Troubleshooting
Consulting
Tier 4/3 Tier 3/2 Tier 2/1

OUTLINE
DEMAND DRIVERS
IMPLICATIONS
TECHNOLOGIES
CONVERGENCE
ISSUES TO BE SOLVED = R&D TOPICS
CONCLUSION
Copyright©2018 NTT Corp. All Rights Reserved.

DEMAND DRIVERS

National IoT Strategies
USA
- Fostering the Advancement
of the Internet of Things
Europe
- Industry 4.0 (Germany)
- IoTUK(UK)
- Basque Industry 4.0
(Basque)
China
- Ministry of Industry and
Information Techology, 12th Five-year
Plan
South Korea
- Manufacturing Innovation 3.0
Japan
- Society 5.0
Taiwan
- Productivity 4.0
Singapore
- Research, Innovation,
and Enterprise 2020

Japan with "Society 5.0"
Source: The Cabinet Office of Japan

IoT Devices
and 5G
Smartphones
and LTE
Expected Application Systems
PC and
Wireline Internet
Systems of Record
Productivity ImprovementSoR
SoE
SoI
SoD
SoA
Systems of Insight
Data-Driven Business Operation
Systems of Engagement
Relation Enhancement
Systems of Detection
Alert Systems
Systems of
Autonomous Processes
=
=
=
=
=

IMPLICATIONS

More Industrial Devices
Source: "IoT Trend Watch 2018", IHS Markit
16.80
5.40
5.90
0.93
0.41 1.70
2018
28.79
24.90
14.58
3.61
1.52
1.48
2025 ※1
※1: Estimated from the growth rates in the source
Communications
Commercial
and Industrial
Consumers
Automotive and Transportation
Medical
Computers
# of devices (billions)

More Cognitive Analytics
The amount of the global datasphere subject to data analysis
will grow by a factor of 50 to 5.2ZB in 2025;
the amount of analyzed data that is “touched” by cognitive systems
will grow by a factor of 100 to 1.4ZB in 2025!
Source: IDC White Paper, sponsored by Seagate, Data Age 2025, April 2017

More Realtime IoT Data
By 2025, more than a quarter of data created in the global datasphere
will be real time in nature,
and real-time IoT data will make up more than 95% of this.
Realtime Data
Source: IDC White Paper, sponsored by Seagate, Data Age 2025, April 2017

More Data from the Ground
(※1) Connected cars will send 25 gigabytes of data to the cloud every hour
https://qz.com/344466/connected-cars-will-send-25-gigabytes-of-data-to-the-cloud-every-hour/
(※2) Just one autonomous car will use 4,000 GB of data/day
https://www.networkworld.com/article/3147892/internet/one-autonomous-car-will-use-4000-gb-of-dataday.html
30~100
MB/ Hr
25 GB (※1)/ Hr
~
300 GB (※2)/ Hr
cf

More Enterprise-Driven
Consumer-Facing
Service Model
Enterprise-Driven
Service Model
Service
Specs
Defined by
Telecom carriers
Defined by
Enterprises
Connectivity Provisioned by
Telecom carriers
Provisioned by
Enterprises
Network
Operation
Operated by
Telecom carriers
Operated by
Enterprises
(May be outsourced)
Network
Examples
PSTN, ISDN, 3G/LTE IP
IoT will accelerate infrastructure deployment
through the enterprise-driven model

More Private Access
Data Source: "IoT Trend Watch 2018", IHS Markit
Private/hybrid LTE eNodeBs – shipments by application area
0
5
10
15
20
25
30
35
40
2015 2016 2017 2018 2019 2020 2021
Other Utilities Industrial Transport Public Safety
（×1000 units)

TECHNOLOGIES

SDWAN
Key Technical Ingredients:
▪ Software-Defined X
▪ Zero-Touch Provisioning
▪ Cloud-style Management :
Management Portal, etc
Benefits:
▪ Enterprise-driven Networking
▸ Flexible use of multiple telecom carriers
▸ Support of global enterprise activities
▪ Reduced on-site engineering
▪ Enhanced security
▸ Centralized management and
monitoring
▪ Traffic optimization
▸ Local breakout of internet traffic
PORTAL
View Status
Manage CPE
Carrier A’s
MPLS
Network
ENTERPRISE WAN
Carrier B’s
ISP
Network
SDN STACK
CPE
ENTERPRISE OFFICE
Manage Policy

SDWAN'S EVOLUTION PATH
Universal CPE (uCPE)
AT&T submitted its uCPE spec to OCP in May, 2017
https://www.opencompute.org/files/ATT-OCP-Specs-April-2018-002.pdf
▪ Software-Defined X
▪ Zero-Touch Provisioning
▪ Cloud-style Management
▪ X86-based NFVI on CPE
Benefits:
▪ Enhanced/Customized Security
▸ Security VNFs on CPE
▪ Traffic optimization with maintained security
▸ Local breakout with distributed security VNFs
▪ Support of edge computing
▸ User-Defined Functions (UDFs) on CPE

AT&T Universal CPE specification submitted to OCP
https://www.opencompute.org/files/ATT-OCP-Specs-April-2018-002.pdf
Component
CPU Denverton 4 Core C3558
Memory 8GB DDR4 ECC
Storage 64GB Emmc
Switch Sillicon Marvell 88E6190X
Switched Ports
LAN : 4 RJ45, (2 with PoE+)
WAN : 2 RJ45& 2 SFP
Non-switched Ports 1 RJ45 (Shared with BMC)
BMC Yes, uBMC
TPM Yes, Infineon SLB 6970 TPM 2.0
QAT Included in Denverton
USB 2 External
Serial Console 1
Power Single, with Dying Gasp from BMC
Form Factor Desktop
Extra Small
AT&T Universal CPE Specs

NTTi3's CLOUDWAN
Controller
VPN UTM (UCPE) AI & RPA RETAIL OIL & GAS MANUFACTURE
SOLUTION EXCHANGE (B2B2X Catalog)
PLAN DELIVERY ON-BOARD MONITOR UPDATE
CLOUDWAN CORE (Service Lifecycle Management)
Office Data
Center
Home Public Cloud Others
Vertical Solutions
UNIVERSAL SERVICE GATEWAY
(NTT Certified Devices)
https://www.ntti3.com/cloudwan/

NFV
▪ Separation of Forwarding Hardware and
Service Software
▪ Cloud-style IT Resource Management:
Pooling, Elasticity
Benefits:
▪ Reduced CAPEX
▸ Less inventory
▪ Agile service development

NFV's EVOLUTION PATH
Microservice
▪ Functional decomposition of VNFs
▪ Runtime environment for
granular functions, e.g. Linux containers
Benefits:
▪ Traffic optimization
▪ More efficient use of IT resources
▪ Handling of uncertain/bursty traffic
▪ Fit for small NFVI, e.g. CPE
VNF
A μ VNF A2
μ VNF A3
μ VNF A1 CLOUD
FOG
GROUND

Mobile Packet Core
Benefits:
▪ Authenticity of endpoints
▪ Policy enforceability
▪ Global interoperability
▪ Very difficult to mention a few
▪ A suite of technical standards for global
interoperability
▪ Device/subscriber authentication with SIM
▪ Worldwide roaming
SIM eNodeB
HSS
IP
NETWORK
PGWSGW
MME
ENTERPRISE
IP NETWORK
ENTERPRISE
CLOUD

Mobile Packet Core's EVOLUTION PATH:
Cloud/IP Native
Benefits:
▪ Addition of authenticated endpoints
to enterprise IT
▪ Traffic offloading/optimization
▪ Dedicated services for B2B2X models
▪ DECOR: Dedicated Core
▪ CUPS: Control / User Plane Separation
gNodeB
UPF
F/M CONVERGED
ENTERPRISE
IP NETWORK
ENTERPRISE
CLOUD
SIM
5G CORE C-PLANE
FUNCTION GROUP
HSS
private or
virtually private
software-defined

CONVERGENCE

THREE TECH. DEV. ACTIVITIES
Mobile
Packet
Core
SDWAN
NFV
Agile Service Delivery
Enterprise-Driven
Networking
Interoperable Networking
of Authenticated Endpoints

EVOLUTION PATHS
Enterprise-Driven
Networking
Interoperable
Networking of
Authenticated Endpoints
microservices for
optimized handling of
uncertain/bursty traffic
uCPE for enhanced
security/computing
CUPS/DECOR for
"Cloud/IP native"

CONVERGENCE
Cloud-Native
Networks
for AI/IoT
Enterprise-Driven
Networking
Interoperable
Networking of
Authenticated Endpoints
microservices for
optimized handling of
uncertain/bursty traffic
uCPE for enhanced
security/computing
CUPS/DECOR for
"Cloud/IP" Native

ISSUES TO BE SOLVED
= R&D TOPICS

Issues with NFV
How to reduce the performance gap
between ASIC-based hardware and software ?
Possible Approaches:
Acceleration: DPDK, FPGA, GPGPU,...
and
Topology optimization by virtue of NFV
ASIC
SOFTWARE
Performance
gap

ACCELERATION
100 GbE Software Router: Kamuee
"Kamuee“ developed by NTT Communications
▪ 100GbE software router on a general-purpose computer
▪ Fast route search using a proprietary technology "Poptrie“
▪ High-speed packet forwarding enabled by DPDK

Topology Optimization with NFV
Relocating network functions instead of redirecting packets
will reduce total traffic.
INTERNET
Enterprise
WAN
User
Branch A
User
Branch B
DC
FW
DC
vFW
Manager
vFW vFW
INTERNET
Enterprise
WAN
User
Branch A
User
Branch B

Issues with Microservices
How to mitigate increased complexity?
New framework for networking with μVNFs

VNF NETWORKING
SPP (Soft Patch Panel)
https://www.dpdk.org/
SPP
pNIC
Shared Memory
VM/Container
▪ OSS project for high-performance and flexible VNF networking
▪ Zero-copy function chaining with shared memory
▪ Flexible chain reconfiguration with intuitive UI
originally developed by NTT R&D

Issues with IoT Applications
How to handle flood of realtime data?
▪ Distributing application functions, i.e. UDFs, instead of aggregating data
▪ Hyperconvergence of computer/network/storage to support the above

Issues with Small Edges
How to overcome the resource constraints of small edges, e.g. CPEs?
Multi-tier computing with VNF/UDF decomposition
Universal CPE (small)
RAM: 8~16 GB
5GC
UPF
4~8GB
OS～
NFVI
2~4 GB
Security
VNF
2~4GB
UDF
e.g. DL Inference
4~8GB
×
out of memory

Issues with Agile Service Development
How to go agile with layers of orchestrators/controllers ?
New framework ???
data-centric architecture ???
Orchestrator
Controller X Controller Y Controller Z
Controller x1 Controller x2

CONCLUSION

DEMAND DRIVERS AND IMPLICATIONS
National IoT Strategies
AI/IoT Applications
Implications
▪ More Industrial Devices
▪ More Cognitive Analytics
▪ More Realtime IoT Data
▪ More Data from the Ground
▪ More Enterprise-Driven
▪ More Private Access
▪ Systems of Detection
▪ System of Autonomous Processes

TECHNOLOGIES
SDWAN
evolving with Universal CPE
NFV
evolving with microservice architecture
Mobile Packet Core
evolving toward "Cloud/IP Native"
The convergence of the above three technologies will
create ideal networks for AI/IoT.

ISSUES TO BE SOLVED
But we still have a lot of issues:
Performance gap
between ASIC and
software
Management
complexity added
by microservice
approach
Flood of
realtime data
from IoT devices
Layers of
orchestrators
and controllers
degrading agility
Resource
constraints at
small edges

ISSUES TO BE SOLVED
But we still have a lot of issues:
Performance gap
between ASIC and
software
Management
complexity added
by microservice
approach
Flood of
realtime data
from IoT devices
Layers of
orchestrators
and controllers
degrading agility
Resource
constraints at
small edges
Network researchers and engineers will remain busy.

THANK YOU

CLOUD-NATIVE NETWORKS FOR THE ADVANCEMENT OF AI/IoT

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