Cisco Connect 2018 Thailand

1. © 2017 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Innovation Towards SP Transformation
Sean Wang
Customer Solutions Architect, Cisco Systems

2. © 2017 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Digitization leading Innovation
More Video
7X
Mobile Traffic Increase
More IoT
HALF
Connected Devices Are M2M
5G Journey Begin
4.7X
Traffic of LTE
4X
Cloud Traffic Increase
More Cloudification
0
10
20
30
40
50
60
2016 2017 2018 2019 2020 2021
2G 3G 4G 5G
79%
19%
Exabytes
per Month
69%
24%
1.5%
Source: Cisco VNI Global Mobile Data Traffic Forecast, 2016–2021
2016 2021
4G 2.4 GB/Mo 6.3 GB/Mo
5G - 40 GB/Mo
❖ Generated traffic
Digitization5GTraffic

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Telecom Operators under Intense Pressure
Operators account for shrinking share of overall telecom industry profit despite their central
role in the ecosystem
Source: World Economic Forum, Accenture
Insatiable consumer demand
for more bandwidth and
better “value”
Endless and ever increasing
spend on network capacity
and coverage
Web-scale
substitution
& free-riding
Peer /
market
pressure

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Service Providers’ biggest Challenges
Challenges Strategy
Operation Efficiency through
Network Automation
Secure Business Agility through
Virtualization
Network Transformation through
Best Optimized Architecture
“Network for IoT, Enhanced 5G service”
1
2
3
• Bandwidth consumption is growing; our revenue
is not. (CAPEX Pressures, Shift to OPEX focus)
• Web scale breaks our current cost and design
models.
• I need compelling services to retain and grow my
subscriber base.
• l don’t want to be locked into a single vendor
network.
• Legacy systems have made my networks very
high touch
• Need to improve Customer Experience with
Security and Compliance

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SP Revenue Growth Opportunity
Core
$
Automotive Healthcare
Transport
Utilities
Consumer
Today
Operator business mostly focused on
the saturated consumer market
Source: European Commission Report, 2016
Source: Cisco VNI Global Mobile Data Traffic Forecast, 2015–2020
Core
$
Automotive
Healthcare
Transport
Utilities
Consumer
2025
Network stratregy to engage in new
vertical markets (B2B)
Core
$
Automotive
Healthcare
Transport
Utilities
Consumer
2020
Vertically targeted services will
accelerate operator business growth

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Network Design Improvement
1~3 Month
Automated
Operation thru
NSO
Legacy Network -
Manual
operation
1~3 Hours
(10 to 15 Mins)
“Automation” “SDN/NFV” “Architecture
Transformation”
• Need to learn
multi-vendor
equipment
commands
• Takes long time to
plan service
creation, change,
removal by vendors
• No need to learn
multi-vendor
equipment
commands
• Can reduce work
operation hours
• OPEX saving and
operation
excellence
Policy based
Automation /
Virtual Network
Separated HW
based Network
• Increase Service
agility and time-to-
market benefit
• TCO Saving
• Support various
value added service
based on
programmability
• Need separate PNF
by each customer
• Low service velocity
• Large TCO
“Secure Network efficiency
through distrubuted Cloud
Architecture”
• Network design suitable to new business,
such as 5G, IoT
• Distributed Datacenter architecture for
efficient traffic processing
• Optimize resource and increase service
velocity through virtualization
Network from
the Service
point of View
Service from
the Network
point of view
Fixed
Mobile
B2B
. . .
Distributed Datacenter
Fixed
Mobile
B2B
Improve
Biz agility TCO Increase
Svc continuity
Fixed
Mobile
B2B

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Automation
Network
Engineer
Service
Applications
Automation Solution
(NSO)
PNFs
VNFs
Network Service Automation solution based on standard
Service request
Real-time
Service config
Product Provisioning thru Automation tool
▪ Automatic provisioining based on Modeling
▪ Fast process for service add, change, delete
▪ Multi-vendor support thru NEDs
References
▪ Sites : AT&T, DT, Comcast, etc
▪ Cases : MPLS L2/L3 VPN, etc
Standardize config information
▪ Manage standard based config information
▪ Minimize Human error
▪ Easy to add and delete config information
Cisco / Juniper / Huawei, etc

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Effect through Automation and Virtualization
AS-IS
100
Work
plan
docume
ntation
(21,868)
Work
hour
(17,314)
Trouble
shooting
(29)
57
Work
plan
docume
ntation
(9,649)
Work
hour
(10,024)
Trouble
shooting
(20)
20 ~ 30
service
delivery
Trouble
shooting
Work
operation
56% saving
42% saving
31%
improvement
25% saving
27% saving
Telco A simulation result (for IP Core & Edge)
Based on Softbank case study (for Cloud VPN)
[Major saving items]
• Simplify overall work process through
YANG modeling
• Reduce work hours through config
automation
• Protect human error
[Major saving items]
• Reduce service delivery time because of
no physical work
• Reduce work hour for service add /
change / remove
• Reduce TCO through virtualization
based service
No
change
Automation
Virtualization

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Virtualization – Use Case
Customer A
Customer B
Customer Z
Telco DC
Service concept
• Customer office is connected with Telco
through IP SEC
• FW, IPS, WLC are located at Telco’s
DC, not at customer office
Benefits for SMB
• Save office cost and power cost
(U$200 / month based on
60 employee)
• Get Telco’s operators support 24X7
• Fast service launch
Benefits for Telco
• No Truck roll
• Fast service delivery
• CAPEX saving
WLC
VM
FW
VM
IPS
Container
FW
Container
Managed Service Virtual Managed Service

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Architecture Transformation – Traffic Pattern
FTTH HFCLAN
Internet
BB
Edge
MobileVoIP
VoIP
Consumer
VoD
Internet / LTE / B2B
Data
Core
Within Metro
Core - Regional
Core –
Cross-Country
58%
20%
22%
41%
23%
35%
2016 2021
Source: Cisco VNI Global IP Traffic Forecast, 2017–2021

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Architecture Transformation
FTTH HFCLAN
Internet
BB
Edge
MobileVoIP
VoIP
Consumer
VoD
Internet / LTE / B2B
Data
Core
▪ Network topology for
North <-> South traffic
▪ Not efficient for real-time
big data analytics and
streaming video data
FTTH HFCLAN
MobileVoIP
Consumer
Data
Distributed Cloud Core
Internet VoIPVoD
Interent / LTE / B2B
▪ Recommended topology for East <-> West traffic
▪ Distributed core architecture for real-time big
data analytics and video streaming
▪ Architecture suitable to accommodate 5G, IoT
through network slicing)
▪ Serviced based virtualization Network
vCPE
VM
vOLT
VM
CachevFW
VM VM
Virtualized (NFV) service zone
Internet VoIPVoD
FTTH HFCLAN
MobileVoIP
Consumer
Data
▪ Distribute X center nodes to XX center nodes
(need to discuss together)
▪ Design Service oriented network through
virtualization
▪ Apply application based SDN routing (Segment
Routing)
AS-IS TO-BE

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5G Architecture
Fronthaul backhaul
C-RAN
vCore
User plane
Analytics/
Telemetry
NSO
WAE
Automate
IP Edge
Central Datacenter
SR SR SR SR
vCore
User plane
Managed
Video
vCore
Control plane
MEC
E2E Network Slicing
E2E Security
5G Architecture
IP Core
SR
Single Pane of Glass Management and Orchestration
5G
Small Cell
Wifi
LPWA
backhaul
IP Edge
Central Datacenter
EPC
4G Architecture
IP CoreRU DU
CPRI
Backhaul aggregation
Decomposed and virtualized Decomposed & Virtualized & Distributed
eNodeB

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Segment Routing
Simplified Routing Protocol
Simplified Carrier Ethernet
Autonomic &
Segment Routing
BGP & SDNT-LDP
BGP
RFC 3107
RSVP-TE
MPLS-LDP
IGP & IP
Application Enabled Source Routing
Native IPv6 transport
1 0
Dynamic-path
Explicit-path
SRv6 without
MPLS Label
With Automation
WAN Automation and optimization
SDN based peering and optimization
SDN WAN
controller
SDN Peering
controller
DC WAN
HOST
Peer’s
ISP
ISP
ISP
WAN
Segment Routing Benefits

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Telemetry
Data collection - Legacy
SNMP
Server
Syslog
Collector
CLI
SNMP
SyslogNon real
time
Scripts
Storage &
analysis
• Scale issues / unstructured / incomplete
• Strong burden on back-end for normalization of different
encodings, transports, data models, timestamps
Data collection - Telemetry
• Free the data
Real
time
Telemetry Analytics
As much Data
As Fast
As Useful
As Easy
As Possible
Hardward and Software Sensors - Telemetry
Sensing &
Measurement
Control Plane events, state & stats
• Resource utilization (CPU, Mem, buffers)
• Environmental properties (temp, fans)
• L3 protocol state and stats
Data Plane events, state & stats
• SPAN/ERSPAN on drop
• Flow table – events (Forwarding drops, policy
drops, latency, bueefr drops…)
• SSX (Streaming stats eXchange) & eCPU stats,
TCAM util, RMON counters

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Advanced Network Automation
Network Data Platform
Data Collectors
Machine
Learning
and Analytics
Derive
Actionable Insight
(Data Analytics)
Multi Layer Topology
Optical, Routing, Overlay
1 Extract Data from the Network
Streaming
Telemetry
Open APIs
Network
State
Path
Optimization
Network
Planning
Device Life Cycle
Management
Security
Dos, DDoS: Detection,
Prevention, Remediation
Service Deployment Automation
Deploy, Manage, Optimize
Automate Network Operations
Configure,
Provision Apps
2
3

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Case Study – OTT Datacenter Complex (1/2)

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Case Study – OTT’s Automation and Operation Efficiency(2/2)
• Operation efficiency is a key in the hyper, web or
massively scalable datacenters
• Much of this efficiency is based on the reduction or
elimination of human to machine interaction unless
absolutely necessary.
• Configuration, Orchestration, Standup, Provisioning, etc
are all handled in a machine to machine basis for hosts
and network devices
• NOC is all software
• Everyone writes code
• 1 sysadmin to every 50,000 servers
• 1 Network Engineer to every 5,000 network devices
• Automate everywhere
• It is not an issue of if, it is an issue of where and
how often
• Remediation
• Up to 98% of all in app, on host, in network
events remediated with ZERO human intervention
• Service Requests opened with Cisco TAC directly
from their FBAR system. Time reduction on case
open to case close is at 60%.
Automation Prerequisite Automation Effect

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Reliance Jio – Disrupting Highly Competitive Market
• Quicker introduction of new
services
• Faster time to revenue
• Launch next generation services
• Enable new business models and
new breed of partners / customers
• Higher service quality and customer
retention
• Avoid overinvestment by “evening-
out” network loads
• Increased competitiveness
• Support penetration pricing vs.
incumbents
Business Benefits
Solution Characteristics
• Simplification and Automation of
service provisioning
• Open APIs, standards based on
virtualized network functions
• Network optimization based on
real-time service demands and
traffic patterns
• Cost efficiency vs. traditional,
non-SDN technology approach
Market Disruption!
• 0100Mn subs in 6 months
• Top 4 out of 10 in 6 months
• 15Mn+ new adds per month
• 2800 n/w sites deployed per wk
• EBIT positive since 1st quarter
• Breadth of services from start
• Hyper-low “Sachet” pricing
• Wealth and variety of partners

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Summary
Network
Transformation
VirtualizationAutomation
New business capabilities built on the network as the platform;
Enabling customers to achieve business outcomes faster with ease-of-operation