green network

8/6/2019 Green Network

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Prof. Lian Kuan Chen IEG 6250 Optical Performance Monitoring P.1Department of Information Engineering

The Chinese University of Hong Kong

Green Network

IERG 6250

Prof. Lian Kuan ChenProf. Lian Kuan Chen()

Acknowledgement: with materials provided by Francis Yen


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Green Network related papers

Power Awareness in Network Design and Routing (J. Chabarek, et al, INFOCOM

2008, pp.457-465, 2008)

Energy-aware Backbone Networks: a Case Study (L. Chiaraviglio, et al, ICC

Workshops 2009,pp.1-5, 2009)

Analysis ofPower Consumption in Future High-Capacity Network Nodes (S.Aleksic, et al, JOCN vol.1, no.3, pp.245-258, 2009)

Energy Consumption in Optical IP Networks, (J. Baliga, et al, JLT, vol.27, no.13,

pp.2391-2403, 2009)

A Review of Energy Efficiency in Telecommunication Networks (K.George, et al,

Telfor Jounel, vol. 2, no. 1, 2010)


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Power Awareness in

Network Design and Routing Makes power-awareness as a primary objective in the network

design and configuration, and in the design and implementation of

network protocols.

Power-aware system design Power-aware network design

Power-aware protocols

Ref: Power Awareness in Network Design and Routing (J. Chabarek, et al,

INFOCOM 2008, pp.457-465, 2008)


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Prof. Lian Kuan Chen IEG 6250 Optical Performance Monitoring P.4

Department of Information Engineering


Power-aware System Design1) Multi-Chassis Systems:

Allows separate physical components to be clustered together to forma single logical

router.

Consists of several line card chassis connected to a non-blocking scalable switch fabric

chassis.

Solves the bandwidth scaling problem by providing a growth path that does not rely on

increasing the bandwidth density and power density.

Although the aggregate power consumption increases, the heat load is spread over a large

physical area which allows existing air-cooling techniques to be used, at the cost of

requiring additional physical space.

2) Alternative Systems:

Optical switches provide terabits of bandwidth at much lower power dissipation than

electronic switches, which can be almost entirely bit-rate independent.

Current Limitations:

Number of ports < 100 (makes them suitable only for the core network)

No feasible Optical buffering


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Power-aware Network Design Offers the opportunity to deploy routers over a set ofPoPs such that the aggregate power

demand is minimized.

Two approaches:

Multiple router-level network topologies that can satisfy a given set of capacity

robustness and power consumption design objectives. Network can be designed such that power-hungry packet processing operations are

limited to a subset of the routers.

Current network design, configuration and management practices are based on deploying

and maintaining infrastructures that are extremely reliable.

Infrastructures that are densely interconnected with many redundant paths using state-of-

the-art high bandwidth routers in the core, lower bandwidth but high connection density

distribution routers around the core and even lower bandwidth access routers and

switches at the periphery.


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Power-aware Protocols

The most basic notion is to include mechanisms for putting components to sleep.

Development of new data link and routing protocols could

make traffic profiles more efficient

(e.g., auto-negotiate rate or minimum packet size)

enable portions of a line card to be turned off if certain features or ports are

not in use

enable entire line cards to enter a hibernation state which could be an

objective of a power-aware routing protocol


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Energy-aware Backbone Networks: a Case Study

1. Evaluates the possible savings in an actual ISP network topology.

Considers a topology which is similar to the actual one adopted by one of the

largest ISPs in Italy

2. Estimates the power consumption of nodes and links using realistic figures thathave been derived from available products.

Proposes a new algorithm which exploits nodes' and links' power consumption to

select the set of elements that have to be turned off.

3. Most network capacity has to be fully available during peak hours, traffic

variation over time allows to improve the energy efficiency up to 34% during offpeak hours.

Ref: Energy-aware Backbone Networks: a Case Study (L. Chiaraviglio, et al, ICC Workshops

2009,pp.1-5, 2009)


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Physical Topology Consider a possible network composed by 372 routers: 8 core nodes, 52 backbone nodes,

52 metro nodes and 260 feeders. Links have a cardinality equal to 718.

(virtual topology)(topology used in experiment)


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Turning off Technique Start with all the devices in on state; then we try to selectively power off them.

First, go through the ordered list of nodes and check which nodes can be powered off

while guaranteeing the network connectivity and the maximum link load constraints at

each step:

1) Sort node set in decreasing energy footprint

2) For each node i

Turn off node i and all links originating/terminating at i

Recompute the minimum hop paths

If network is disconnected, power on node i and goto to next node

Compute all link ows by routing T

If any link is congested then power on node i

Similar procedure for the links that are left powered on after the first step.

Sort links in decreasing order according to their power consumption.

Selectively try to power off them by checking if the connectivity and maximum link

load constraints are met.


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Results

Consider both a simple sinusoidal pattern,

and a real traffic profile observed on the real

network.

Results:

Node saving is constant during night, since

the connectivity is the tightest constraint,

being the offered traffic much smaller than

during peak hour.

As expected, during the day the node power

saving decreases as the traffic increase, since

more capacity is required in order to

guarantee the maximum link utilization

constraint.Seed 1-3: three different traffic


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Results The saving is higher than in the node case

since a much larger number of links can be

switched off during off-peak hours.

During the day instead, it is not possible to

save a lot of energy.

Additional resources used to recover from

possible faults are not exploited to carry traffic

during off-peak time, and then they can be

powered down to save energy. During peak hours on the contrary, the saving is

much lower, as only about 10% of nodes can be

powered off, being the majority of them

backbone nodes.


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Analysis ofPower Consumption in

Future High-Capacity Network Nodes A generic architecture of a high-

capacity network node.

It is composed of a high port-countswitching fabric, a large number ofinput and output interfaces, a switchcontrol module, and transmissionsubsystems.

High-capacity links between two corenodes will probably be based onoptical WDM fiber transmissionsystems, as is to a large extent alreadythe case in current networks.

Analysis of Power Consumption in Future High-Capacity Network Nodes (S. Aleksic, et al, JOCN vol.1, no.3,pp.245-258, 2009)


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Electronic implementation of a packet-switched core node consisting of a large

electronic switching fabric and many line

cards whose structure.

Optical packet- or burst-switching node using alarge optical packet switch based on SOAs.

At the input of the node, incoming packets are

first synchronized respectively to become aligned

with each other.

Contentions can be resolved by using wavelength

conversion modules located at input ports and

optical buffering at outputs.

Four different core node architectures

and technologies

(1) (2)


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Four different core node architectures

and technologies

Circuit-switched WDM core nodecomprising wavelength converters at input

ports and an optical cross connect that is

realized by using MEMS switch.

Circuit-switched electronic core node is

shown in Fig. 5. It uses a large electronic

cross-point switch and comprises line

cards with a simplied structure.

(4)(3)


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Results and Conclusion

Power savings from 40 to 100 Gb/slinecards are not large for packet-

switched because the transmission

subsystem (Optics/Phy/MAC)

contributes only 12% to the total power

consumption.

For circuit-switched node, savings of upto 30% can be achieved, while the

power consumption of packet-switched

line cards can be reduced by only 9%

when using the 100 Gbit/s DQPSK

format instead of 40 Gbit/s NRZ data

transmission

When using current state-of-the-art technologies and approaches, optical nodes consume

generally less power than electronic ones.

Optical circuit-switched architectures based on MEMS switching devices seem to be the most

scalable solution among the four considered architectures with respect to power

consumption.


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Energy Consumption in Optical IP Networks

A network model includes core, metro and edge, access and video distribution networks, and

takes switching and transmission equipments into calculation of energy consumption.

AT = AI + AC + AMC (1)

where AT is the total no. of downstream bits at terminal unit per customerAI is per customer capacities in public Internet

AC is per customer capacities in VDN

AMC is the multicast video traffic

Ref: Energy Consumption in Optical IP Networks, (J. Baliga, et al, JLT, vol.27, no.13, pp.2391-2403,

2009)


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Power Consumption ModelAI=AP/M (2)

where AP is peak access rate in Mb/s per customer, M is oversubscription rate

AMC=LB/NTU (3)

whereLB is no. of backhaul links from terminal unit

NTU is no. of customers sharing a terminal unit

In core network, the power consumption also includes estimates of increased efficiency infuture generation of core router and switches.

Assumes that the efficiency improvement of a router/switch is in exponential model, then the

energy consumption per bit of a core router (PR/CR) is

PR/CR=P0 (1-)t/C0 (4)

where P0 is current power consumption of a router/switch, C0 is current capacity of

a router/switch

PR is future power consumption of a router/switch after t years

CR is future current capacity of a router/switch after t years

is the annual rate of improvement of state-of-the-art technology


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Power Consumption Model

As power consumption is a function of the capacity required to support a given access rate,estimates of efficiency improvements relating to peak access rate is included in the paper.

The Internet traffic (access rate) grows exponentially.

A=A0t (5)

whereA0 is current access rate, A is future access rate after t years

is per year Internet traffic growth rate

Using 2008 per customer public Internet capacity,

AI=100kb/s, then AP=1Mb/s for M=10

AI=100kb/s, then AP=2.5Mb/s for M=25

From D. T. Neilson, Photonics for switching and routing, IEEE J. Sel. Topics Quantum Electron.,vol. 12, no. 4, pp. 669678, Jul./Aug. 2006

Annual router improvement rate, = 0.2

From Cisco white paper, Global IP Traffic Forecast and Methodology, 2008., traffic growth rate,=1.42


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Access Rate Estimation

After getting those values, the trends in router capacity and energy efficiency over time is

found.

The per customer power consumption in each section of network are calculated using datafrom datasheets. Cooling requirements are included in calculation.

For every watt of power consumed in metro and core networks, another watt of power is

required for cooling.


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Access Network

The per customer power consumption, Pa of ADSL, PON, FTTN and PtP is:

Pa=PCPE+PRN/NRN+2PTU/NTU (6)

wherePCPE is power consumed by customer premises equipment

PRN is power consumed by remote node; NRN is no. of customers sharing a remote node

PTU is power consumed by terminal unit; NTU is no. of customers sharing a terminal unit

The factor of 2 in the last term accounts for additional overheads (external power supplies and

cooling requirements)

All variables can be obtained directly from datasheets except for the term NTU ofPtP network

obtained by:

NTU (PtP) = min [(total port capacity.-LB)Gbs-1/(1Gbs-1+AT), (switching capacity-LB)Gbs

-1/2AT ] (7)

The first term is due to the limit of port capacity, the second term is due to the limit of

switching capacity. For Cisco 4503 switch,

total ports capacity = total no. of GE ports (116) * 1Gb/s = 116Gb/s

switching capacity = 64Gb/s


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Access Network

PTU(kW) NTU PRN(W) NRN PCPE(W) Technology Limit

ADSL 1.7 1008 0 N/A 5 15Mb/s

PtP 0.474 Eq.7 0 N/A 4 1Gb/s

PON 1.34 1024 0 N/A 5 2.4Gb/s

FTTN 1.34 8192 47 16 10 50Mb/s

The per customer power consumption, Pa can be calculated by the values of access

network parameters:


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Prof. Lian Kuan ChenIEG 6250 Optical Performance Monitoring P.23



Metro and Edge Network

the per customer power consumption of metro netowkr, Pm is

where PES is per customer power consumption of the edge Ethernet switches

PGateway is power consumption of a gateway routerPPEdge is power consumption of a provider edge router

CGateway is capacity of the gateway router

CPEdge is capacity of the provider edge router

The first factor of 2 is to include the requirements for cooling.

The second factor of 2 is to include the requirements for redundancy upstream of the Ethernetswitch.


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VDN Network

The model does not include the power consumption to transport multicast traffic

through the VDN as the per customer power consumption of this transport is

negligibly small.

A Cisco 7613 router consumes 4.6 kW and serves a capacity of 120 Gb/s. Assumefor a traffic through the VDN taking two hops, the per customer power

consumption of the VDN is

(10)

where the factor of 4 accounts for the power requirements for cooling andredundancy, and the factor of 3 is because three routers are transited for two

hops.


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Core Network

A single-rack Cisco CRS-1 core router consumes 10.9 kW and has a full-duplex

switching capacity of 640 Gb/s.

The per customer power consumption of the core node PC is:

(11)

where H is the no. of core node hops

The factor of 8 (2*2*2) is because

Core routers are provisioned for future growth of double the current peakdemand

Power requirements for cooling

A factor of 2 for redundancy


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Results

In Access Network:

Power consumption in access network depends on its capacity and equipments used

from equation (6).

For a fair comparison of different architectures, energy consumption per bit of eachaccess network is calculated (per customer power consumption/max. access rate AT) .

The most energy efficient of access networks is PON for low access rate, PtP for very

high access rate.

Max AT (Mb/s) Power per customer (W) Min. energy per bit (J)

ADSL 2 7.8 3.8

PtP 125 12.2 0.1

PON 16 7.6 0.5

FTTN 2 13.2 6.6


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Power Consumption of the Internet

The relationship between the total per customer power consumption of the Internet and the

peak access rate, for access rates from 1 Mb/s to 400 Mb/s per home, with an oversubscription

rate (M=10, M=25), technology improvement rate = 0.1, are found.

For Fig. (a) , the total power consumption is based on a PON in the access network and no VDN.

At low access rates, the access network consumes over 90% of the total network power.

At an access rate of 100 Mb/s and an oversubscription rate of 25, the core, metro and edge

networks together consume 30% of total network power with the WDM links consuming 4.5% of

total network power.


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Power Consumption of the Internet

For Fig. (b), the total power consumption is based on a PON and PtP are showed for M=10.

At a peak access rate, the power consumption of the routers and switches in the core,

metro and edge networks becomes dominant of only 100 Mb/s.


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A Review of Energy Efficiency in Telecommunications Networks

Concerning the fixed line networks, more

than 70% of the overall power consumption

occurs in the user segment (power is

distributed) and only 30% is due to the

operator OPEX.

Neglecting the core network operation,

fixed line networks suffer great losses due to

cable transmissions, switching/routing,

broadband access and data centers whereas

mobile networks consume a huge amount of

energy for base station operation.

Ref: A Review of Energy Efficiency in Telecommunication Networks (K.George, et al,

Telfor Jounel, vol. 2, no. 1, 2010)


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Power Consumption in Telecommunications Networks

The energy consumption is higher at theaccess part of the network and theoperation of data centers that providescomputations, storage, applications and datatransfer in a network.

This makes clear that an energy efficient

architecture should focus on intelligent andefficient access techniques and efficientoperation and data manipulation by datacenters.

In core network, the largest part of energy isconsumed for routing/switching,regeneration and processing of data.

This imposes challenges for moresophisticated transport techniques, thermalremoval from switches or the servers andless redundant data transfers.

RBS:

remote base station


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