At the Society of Cable Telecommunications Engineers Expo 2014, Andy Smith of Juniper Networks presented Juniper’s vision and architecture for a cable oriented packet optical core and metro transport system. Access insights and network diagrams in his presentation and learn more in his blog post: http://juni.pr/1rwapCG.

1. Cable Metro
Packet Optical Transport
Juniper Networks
Distinguished Engineer & Chief Architect for Cable MSO Networks
Andrew Smith

2. The Network is Fundamental
But there are too many manual, fragmented parts
Manually Operated Fragmented Domains
The Network Must:
•Remove barriers
•Synchronize dynamically
•Respond to accelerating change
IP
TRANSPORT
SERVICES

3. Networks Need to Customize
Cable networks need to adapt to changing requirements and conditions
COMPLEX
Exponential Growth, Fragmented, and Manual
OPERATIONS
INEFFICIENT
Over-provisioned and Hardware Dominated
INFRASTRUCTURE
RIGID
Limited Analytics, Fixed Policies, Months to Change
SERVICES

4. Packet Transport is Key
Dynamic, efficient, flexible, programmable
EFFICIENT
Any to Any
Low Latency
Dynamic
PACKET SWITCHING
ELASTIC
Load Adaptable
Resource Optimized
Highly Available
MPLS TRAFFIC ENGINEERING
PROGRAMMABLE
Intelligence, Programmability & Abstraction
APPLICATION AWARE

5. Silicon –Heart of Modern Routers
Capacity exceeding Moore’s Law
Total single system capacity will exceed 100Tbps shortly
Switching latency, port-to-port, < 10 usec
640G
1.6T
3.2T
24T
2002
Single System n-Degree Capacity
100+T
2017
Moore’s Law
Power/Thermal Efficiency
Power Utilization
(Tbper KW)
1.2
Thermal Emission (BTU per Gbps)
2.8
Capacity Footprint
Per Rack Capacity (Tbps)
7.68
Capacity Footprint (Gbps per cu- in)
533
Platform Characteristics
Depth (mm)
270
MinimumPower (KW)
1.2
Latency (us)
5
PTX3000 Reference Platform

6. Packet System Efficiency
Elasticity and Efficiency are Mandatory
Statistical multiplexing –resource sharing and adaptation to service demands
MPLS LSP Bandwidth reservations dynamically adapt to the constantly changing network demands
Persistent and ephemeral path creation via SDN controller
Dynamic and planned adaptation via SDN controller
Packet Forwarding Engine
Efficiency
Elasticity
Reconfigurability

7. Peak value P1
Bit-rate
time
Peak value P2
Bit-rate
time
PE1 to PE2 traffic
PE3 to PE4 traffic
OTN circuit size
OTN circuit size
Wasted Bandwidth
Wasted Bandwidth
PE1
PE2
PE3
PE4
Core
B/W ?
Packet Elasticity
Statistical Multiplexing
With packets, required bandwidth in core is less than P1 + P2
–Statmuxgain is more pronounced with greater flows and higher variation of each flow. Modern cable networks exhibit this behavior.
–In contrast, circuit-switched networks require one circuit of size P1 (at least) and another of size P2 (at least)
TDM circuit sizes are quantized with large steps, bandwidth is wasted by ‘rounding up’
–OTN granularity is relatively very course. ODU0 –1.25 Gb/s,
Cable networks are also very asymmetric!

8. A Packet Transport MPLS LSP
Elastic and Adaptive, Service Oriented
B/W reservation & guarantees
Elastic
Adaptive
Loss & delay reporting
Unidirectional
Bi-directional & associated
Facility protection
Path protection
Node protection
Control-plane separation
Multi-path aware
Point-to-point
Point-to-multi- point
QoS
OAM reporting
Hierarchical LSPs
Multi-protocol
Explicit or dynamic or loose hops

9. Multi-layer Meshed Network
Collapsed Packet-Transport
Network Fabric
Modern Cable Transport
Packet Applications, Services, Transport
Cable networks are dominated by
router-to-router IP services and data
center interconnect
– Point-to-point DWDM designs
– Higher speed router links (Nx100G)
A response function of the network
– Analytics at every transport node
– Offered load is visible at the transport layer
– Network fabric can react
Transport modern cable services
– Full suite of contemporary high-speed
residential Internet services, IPv4 / IPv6
– Emerging cloud and multimedia services
– Commercial L2/L3VPN, Internet
– Cell tower backhaul
– Enterprise services

10. Peer Discovery
Property Exchange
1Tb Transport- group
800G Transport- group
Automate for Simplicity
Optical Plug-n-Play: Dynamic DWDM
Optical Peers
–Dynamic discovery of adjacent peers with optical capabilities –100G coherent transponder
–Via supervisory channel or DCN
Optical properties exchanged
–Session establishment if and only if optical properties of physical link are interoperable
–Exchange of “transport group” characteristics
Transport-groups
–Definition and exchange of desired bandwidth, inclusive of wavelength restrictions
–Wavelength channel allocation

11. MPLS Transport Service Creation
Merge Benefits of Packet & Transport
•LSP Control, Creation, & Path Optimization
•Path Diversity (Link, Node, Facility)
•Bandwidth Scheduling & Calendaring
•Fast Reroute Planning
•Programmable Path Cost Functions
•Optimized Exit Control
•Global Concurrent Optimization
•Container LSP association (Auto-B/W & LSP multi-path/load-balancing)
ROUTING
Distributed control
Stat mux gain
Multi-hop resilience
Service integration
Feature breadth
TRANSPORT
Centralized control
Predictability
Dedicated bandwidth
Fast recovery
Operational simplicity
SDN TE
NETWORK
CONTROLLER

12. SOFTWARE-DRIVEN POLICY
Topology Discovery
Path Computation
Path Installation
PCEP -LSP discovery
IGP-TE, BGP-LS-TED discovery
jVision–Streaming Analytics
PCEP–Control/Create traffic engineered LSP
Netconf/YANGMay include: BGP, DMI, OpenFlow, I2RS
ANALYZE
OPTIMIZE
VIRTUALIZE
Routing
Netconf/YANG
PCEP
JunosCSPF
Algorithms
Self Optimizing Efficiency
StatefulWAN Controller Creates Paths
Bin-Packing
Defrag
Premium Paths
Scheduling
Calendaring
Predictability
Adaptive TE
Inter-Domain Routing
Global concurrent optimization
Network lifecycle management

13. Agent
RPD
Kernel
PFE
PFE
PFE
Sensors
Programmable objects to tap into any state of the network node
Capabilities can be enhanced over time
Standard IP based interface with data store
Decoupling
Telemetry data is outside JUNOS
Extendible client interface
Different analytic views can be provided independent of JUNOS
PCE Controller
S
S
S
Analytics
Leverage Deep Analytics
Multiple sources of data for optimization
Jvision–provides analytical insight into Juniper products. Data ‘pushed’ to external nodes in band
Other sources of intelligence via PCE plugin --real time video application feedback for cable networks

14. In Conclusion
Open standards, interopand innovation
AUTOMATE
Open multi-vendor technologies for simplicity and agility
SCALE
Ultra-high performance and efficient systems and solutions
OPEN STANDARDS
Open, multi-layer, multi-domain SDN and comprehensive NFV