1. Power-Aware Network Design
«Power Awareness in Network Design and Routing»
J. Chabarek et al.
«Energy-Minimized Design for IP Over WDM Networks» G. Shen, R. S. Tucker

2. Introduction The Internet is expanding tremendously
Growth in the number of end users and connection speeds -> exponential increase in bandwidth demand
Increase in energy consumption
Cost of transmission and switching one of the major barriers
Energy consumption may become a barrier
1% - 2% of total electricity consumption in US
A cut of 1% in the Internet energy consumption means about US$5 billion per year
Increase in power density
Thermal issues -> limitations of air cooling
Increase in operational costs
Increase greenhouse footprint
Save the Earth!!!

3. Power Aware Design Areas (I)
Three main areas for power aware design
System Design
Development in CMOS technology -> improvements are slowing down
Multi-Chassis Systems: separate physical components clustered forming a single logical router
Aggregate power consumption increases -> heat spread over a large physical area -> existing cooling techniques used
Alternative Systems: optical switches
Terabits of bandwidth at much lower power dissipation
Protocols
Investigated in wireless networks -> Opportunities in wire-line networks
Basic notion: put components to sleep if low traffic load
Routing protocols: routes calculated with power consumption constraints

4. Power Aware Design Areas (II)
Network Design
Deploy routers such that the aggregate power demand is minimized
Satisfying robustness and performance
Two approaches
Multiple router-level topologies satisfying capacity, robustness and power consumption
Limit power-hungry systems to a subset of routers
Selection of chassis and line cards in routers is a main issue to reduce power consumption
In IP over WDM networks
IP routers use more than 90% of total power
Lightpath bypass is used to reduce the number of IP router ports -> IP ports consume major energy in IP routers

5. Router Power Consumption
Router power consumption depends on
Type of router chassis
Type and number of line cards deployed in the chassis
Configuration and operating conditions
Size of packets
100 bytes / 576 bytes / 1500 bytes
Size of forwarding table
1000 entries / entries
Type of traffic
UDP
TCP
Employed protocols and techniques
OSPF
Netflow
Unicast Reverse Path Forwarding (uRPF)
Access Control List (ACL)
Active Queue Management - Random Early Detection (AQM – RED)

6. Router Power Consumption
Chassis and line card combinations
Chassis: Cisco GSR / Cisco 7507

7. Router Power Consumption
Chassis and line card combinations (cont.)
Base system is the most consuming
7507 chassis + router processor -> 210 Watts
GSR chassis + router processor + switching fabric -> 430 Watts
It is best to minimize the number of chassis and maximize the number of line cards per chassis
Calculated power consumption of different cards

8. Router Power Consumption
Configuration and operating conditions
A 4-port Gigabit Ethernet line card and a OC-48 card in a GSR chassis is used
Deployed testbed:

9. Router Power Consumption
Configuration and operating conditions (cont.)
Constant bit rate UDP traffic and different packet sizes
1500 bytes / 576 bytes / 100 bytes
Power consumption increases as packets get smaller!!!

10. Router Power Consumption
Configuration and operating conditions (cont.)
Constant bit rate UDP traffic, medium packets and different features
Large forwarding table / ACL / uRPF / OSPF
uRPF is the most consuming
Large forwarding table is less consuming!!

11. Router Power Consumption
Configuration and operating conditions (cont.)
Self-similar TCP traffic, 75% offered load and different features
Netflow / AQM - RED
Power consumption similar to UDP with large-sized packets

12. Router Power Consumption
Configuration and operating conditions (cont.)
Maximum variation in previous slides -> 20 Watts
Extrapolating a fully loaded chassis -> Watts
Less significant than chassis/line card configuration
General Model:
PC -> power consumption of router
X is a vector defining chassis type, line cards, configuration and traffic profile
CC -> power consumption of a chassis type
N -> number of line cards
TP -> scaling factor (traffic utilization)
LCC -> cost of line card

13. Power Consumption Optimization
Main focus: allocation of line cards and chassis in nodes to minimize power consumption
Mixed-Integer resource allocation problem with multicommodity flow constraints
Inputs
Network with OSPF link weights
Traffic matrix
Line card and chassis options
Outputs
How each node should be provisioned
Multipath routing
Implemented with General Algebraic Modeling System (GAMS)

14. Power Consumption Optimization
Networks are taken from the Rocketfuel project
Inferred weights and link latencies
Link weights -> calculate approximate bandwidths of each link
Traffic matrixes generated with a gravity model
Three additional random graphs with 12 nodes and varying number of directed edges (Waxman method)

15. Power Consumption Optimization
Network design problem: deploy different chassis/line card configurations such that provisioning requirements are satisfied and power consumption in minimized
Traffic is scaled for each origin-destination pair -> linear scaling factor
Varies provisioning requirements
Traffic flows might be altered to put cards/chassis to sleep in low utilization
First scenario includes only GSR chassis and OC-48 line card
Only 10 line cards allowed per chassis
Scaling factor varies from 0.1 to 40

16. Power Consumption Optimization
Other experiments relaxing line cards per chassis, chassis type and card types (not in the paper)
Minimum power consumption -> chassis accommodating large numbers of line cards and line cards capacities that closely match demand

17. Power Consumption Optimization
Power savings
Compared to a non-power-aware network design (shortest path)
Using a specific chassis (GSR) and line cards (OC-48 or 0C-12)
OC-12 line cards achieve smaller savings -> more ingress/egress node ports
Cost of additional connectivity is zero as long as the number of ports does not require additional line cards

18. IP Over WDM Network IP layer: Optical layer
Core IP router aggregates data traffic from low-end access routers
IP router ports consume major energy (forwarding process) -> number of IP ports as measure of total power consumption
Optical layer
Optical switches interconnected with physical fiber links
May contain multiple fibers
Each fiber needs a pair of multiplexer/demultiplexer
Each wavelength require a pair of transponders -> full wavelength conversion is assumed
EDFA amplifiers are deployed on fiber links

19. IP Over WDM Network Two implementation approaches Lightpath non-bypass
All data carried by lightpaths is processed and forwarded by IP routers
All lightpaths incident to a node must be terminated
Lightpath bypass
IP traffic whose destination is not the intermediate node -> directly bypasses the intermediate router
Saves IP router ports

20. Energy Consumption Optimization for IP over WDM
Network design problem: design an energy-minimized IP over WDM network
Serving all the traffic demands
With a limited maximal number of wavelengths in each fiber
With a limited maximal number of IP router ports at each node
Inputs
Physical topology -> N nodes and E links
Traffic demand matrix
Number of wavelength channels per fiber and capacity of each wavelength
Maximal number of IP router ports at each node
Energy consumption of router ports, transponders and EDFAs

21. Energy Consumption Optimization for IP over WDM
The optimization problem is solved using a Mixed-Integer Linear Programming (MILP) model including
Energy consumption of IP routers, EDFAs and transponders
Layout of EDFAs
Ports for aggregating data from low-end routers
MILP model minimizes also the number of network components -> could be used for cost-minimized IP over WDM network
The computational complexity is high
O(N4) variables and O(N3) constraints
Heuristics are needed for fast solution

22. Energy Consumption Optimization for IP over WDM - Heuristics
Direct Bypass: directly establish virtual links (lightpaths) whose capacity is sufficient to accommodate all the traffic demands between each node pair
Routing of lightpaths -> shortest path routing
Simple
Could lead to low capacity utilization
Multi-hop bypass: traffic demands between different node pairs could share capacity on common lightpaths
Elongate lengths of some IP traffic flows
Fewer lightpaths -> fewer IP router ports

23. Energy Consumption Optimization for IP over WDM - Heuristics
Multi-hop bypass heuristic:

24. Energy Consumption Optimization for IP over WDM - Setup
Five study cases
Linear relaxation of the MILP model -> lower bound
MILP optimal design
Non-bypass -> upper bound
Direct bypass
Multi-hop bypass
Inputs
Traffic demand between each pair node:
Uniform distribution within a certain range centered at an identical average

25. Energy Consumption Optimization for IP over WDM – Test Networks
NSFNET
USNET

26. Energy Consumption Optimization for IP over WDM – Total Power Consumption
NSFNET
Larger topology ->
higher power consumption,
heuristics closer to lower bound
Non bypass -> upper bound
LP relax. -> lower bound
Linear relationship between total power consumption and total traffic demand intensity
USNET

27. Energy Consumption Optimization for IP over WDM – Power Consumption Saving
NSFNET
Larger topology -> higher savings,
longer lightpaths bypassing more
nodes -> fewer IP ports
Multi-hop bypass heuristic performs better than direct bypass ->
Small traffic flows are aggregated
USNET

28. Energy Consumption Optimization for IP over WDM – Component Consumption
NSFNET

29. Energy Consumption Optimization for IP over WDM – Geographical Distribution
NSFNET
All bypass design have a more uniform power distribution
Solve problems associated with:
Supplying large amounts of energy
Removing associated heat

30. Energy Consumption Optimization for IP over WDM – Cost Analysis
The model could be used for minimizing cost
Changing the optimization weights from energy to cost
May NOT be valid if components with low energy consumption are the most expensive ones
N6s8 network based on the MILP optimization model

31. Conclusions Energy consumption may become a barrier for the Internet
Operational costs
Greenhouse footprint
Cooling issues
Supplying large amounts of energy
Power aware design could solve it
Power aware system design
Power aware protocols
Power aware network design
Power aware network design could achieve important savings
In IP over WDM networks, lightpath bypass could save power consumption

32. References
[CHA08] J. Chabarek et al., «Power Awareness in Network Design and Routing», Proc. Of IEEE INFOCOM, 2008
[SHE09] G. Shen, R. S. Tucker, «Energy-Minimized Design for IP Over WDM Networks», Journal of Optical Communication Networks, June 2009.