energy efficient pons with service delay guarantees and...

EE-LSDS Conference Apr. 22-24 2013 Wien, Austria

Energy Efficient PONswith Service Delay Guarantees

and Long Reach

L. Valcarenghi, P.

CastoldiTeCIP Institute

Scuola Superiore Sant’Anna, Pisa

M. ChincoliCNIT, Pisa, Italy

P. Monti and L. WosinskaONLab, KTH, Kista, Sweden

2

Summary

• Energy consumption in optical access networks overview

• Scenario– PON– Long Reach (LR) PON

• Cyclic sleep with Service based Variable sleep Period (CSVP)– System description– System model– Results

• CSVP in Long-Reach PON – Results

• Conclusions

3

Summary





• Conclusions

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Energy Consumption in Communications Networks

• Access networks + mobile radio networks (except home networks) major contributors to energy consumption in communications networks:– high number of Customer

Premises Equipment (CPE)– bandwidth underutilization

Remaining part home networks

Access=Fixed Access Network= Fiber to the Exchange (FTTE) and Fiber to the Cabinet (FTTC): fiber + xDSL; FTTH (PON)

Wired optical access network energy consumption50%-80%

of wired networks energy consumption

Source: C. Lange, D. Kosiankowski, R. Weidmann, and A. Gladisch,

“Energy Consumption of telecommunication networks and related improvement options”, IEEE

JSTQE, March/April, 2011

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Considered Scenario – PON and LR PON

ONU-2

Passive Splitter

Central Office

Idle traffic, butactive receivers and service lines

ONU-1

OLT

ONU-N

Differential reaches

ONU1

ONU2

ONUk

LR-PONUp to 100 km and beyond

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Long Reach (LR) PONs

• Site reduction implies that longer distances must be covered by the same ODN– Reach up to 100 km– Longer distance -> longer RTT

• Node consolidation decreases energy consumption by decreasing the number of central offices

• Node consolidation increases energy consumption because of presence of amplifiers

7

Summary





• Conclusions

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Service based variable sleep period

• Two main decisions must be made– When to sleep– For how long

• Reduce ONU energy consumption– By means of service-based variable sleep period – initially proposed by R. Kubo, et. al , “Adaptive Power Saving Mechanism for 10

Gigabit Class PON Systems,” IEICE Transactions on Communications, vol. 2, no. E93.B, pp. 280–288, 2010

• Guarantee delay constraints to applications subscribed by end-users @ONU– By computing the maximum allowed sleep time based on subscribed

services and queuing theory system model • “End-user multimedia QoS categories,” ITU-T Recommendation G.1010, nov. 2001.

• “Network performance objectives for IP-based services,” ITU-T Recommendation Y.1541, Dec. 2011.

• “Ethernet frame transfer and availability performance,” ITU-T Recommendation Y.1563, Jan. 2009.

IPTD=IP packet Transfer Delay; IPDV=IP packet Delay Variation

Cyclic Sleep with Service-based Variable Sleep Period (CSVP) Rationale

Tsl is a function of the delay constraints

For each QoS class a specific sleep period is assigned to maximize the energy efficiency while guaranteeing the constraints

Each ONU is subscribed to a list of services

The OLT builds a table containing the IPTD, IPDV, and minimum bandwidth requirement per each registered ONU

The OLT is costantly aware of the services subscribed to each ONU

The OLT has a table in which services are categorized in terms of QoS class with specific bandwidth and delay constraints

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10

CSVP System description

Modified SPW with service-based variable sleep period

ONU Finite State

Machine

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ServiceSubscription

Update IPTD(min IPTD)

Tsl = 2*(IPTDmax – S – RTT) - TOH Inform ONU

Tsl

( )( ) ( )Sλ

Sλ+

Sλ

Sλ+V+

V

VV=Wq ⋅−⋅

⋅⋅−⋅⋅⋅

⋅−

1212

1

2

222

22

RTT+T+T=

VW OHsl

q ≈

CSVP: IP packet Transfer Delay (IPTD) model

2/max RTTSWIPTD q ++=

RTT+T+T=V OHsl

1,1 2 <<<< SS λλ

• Constant V

• Low load condition

Hp:

POLLING SYSTEM WITH GATED SERVICE POLICY WITH IDENTICAL STATIONS

Queuing

delay

V Vacation

time=Idle time

S Service time

qW

Assumption: 1 ONU and 1 OLT

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ServiceSubscription

Update IPDV(min IPDV)

Tsl = IPDV - TOH - RTT Inform ONU

Tsl

CSVP: IP packet Delay Variation (IPDV) model

IPDV = IPTDupper-IPTDmin

IPTDupper = 1-103 quantile of the IPTDIPTDmin = minimum IPTD

IPTDupper ≈ IPTDmax = TDS,max+RTT+Tsl++TOH+S+RTT/2

Frame arriving right after the CONFIRMATION

Frame arriving right before the CONFIRMATION and no frame in the OLT

queue

IPTDmin=S+RTT/2

IPDV = TDS,max+RTT+Tsl+T

OH0,1 max, ≈<< DSTSλ

Hp: low load condition

IPDV = RTT+Tsl+TOH

Tsl = min{TslIPTD,TslIPD

V}

ITU-T Y.1541 def.


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Parameter Variable Value

Overhead time TOH 2ms

Round-trip time RTT 0.4ms

Power consumption in sleep mode Psl 1 W

Power consumption in active mode Pa 10 W

Channel rate C 10 Gb/s

Number of span n 8

Simulation Scenario CSVP

• 1 OLT, 1 ONU

• Event driven simulation

• Different QoS classes mix

• Negative exponential frame interarrival time distribution

Class ID

Frame Payload size

[Byte]

Data rate Bi [b/s]

IPTD[ms]

IPDV

[ms]

1 1500 30.4k 125 125

2 560 1k 50 125

3 1067 256k 125 125

4 200 64k 12.5 6.25

Service characteristics

Simulation parameters

Results – IPTD constraint guaranteed

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IPDV computation based on the cumulative distribution

function (CDF) of IPTD, uniformly distributed

Tsl as a function of IPTD only

Results – IPTD and IPDV constraint guaranteed

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Tsl as a function of IPTD and IPDV

IPDV computation based on the cumulative distribution

function (CDF) of IPTD, uniformly distributed

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Summary





• Conclusions

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Parameter Variable Value

Overhead time TOH 0 - 2 ms

Reach R 20 - 100 km

Power consumption in sleep mode Psl 1 W

Power consumption in active mode Pa 10 W

Channel rate C 10 Gb/s

Number of span n 8

LR PON - Simulation Scenario CSVP

• Event driven simulation

• Different QoS class mix

• Negative exponential frame interarrival time distribution

Class ID

Frame Payload size

[Byte]

Data rate Bi [b/s]

Wq,a

[ms]

Wq,b

[ms]

1 1500 30.4k U 200

2 560 1k 400 100

3 1067 256k 1000 40

4 200 64k 100 5

Service characteristics

Simulation parameters

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LR PON - Results

• Stricter delay constraint -> Reach heavily affects energy efficiency

• Increase in overhead time (i.e., an increased synchronization time) is more detrimental for energy efficiency than a reach increase


TOH = 2ms

Tsl = 2*(IPTDmax – S – RTT) - TOH

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LR PON - Results (2)

• With fixed cycle time and higher number of ONUs, energy efficiently apparently increases because the slot for an ONU decreases but most of the time spent in scynchronization

• Contemporarily the bandwidth utilization per cycle decreases (smaller slots)

58%

60%62%

64%66%

68%

16 32 64 128 256 512 1024

# of ONUs

eta

[%

]

59%

60%

61%

62%

63%

64%

65%

66%

67%

68%

16 32 64 128 256 512 1024

# of ONUs

eta

[%

]

1.00E+06

1.00E+07

1.00E+08

1.00E+09

Bw

per

ON

U

eta Bw per ONU

−−

−=−=

C

OH

a

sl

T

T

N

N

P

P

E

EE 11

'

η

Tc=2 ms,

TOH=0.5ms

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Conclusions

• Energy consumption in optical access networks• Cyclic sleep with service-based variable sleep period

(CSVP)– Energy consumption reduction while providing

delay guarantees• Queuing theory model to compute sleep time• Both average frame delay and frame jitter QoS

constraints are met with different energy efficiencies• Reach might heavily impact energy savings • An increase in the number of ONUs decreases the

energy consumption per ONU but at the expenses of a decreased bandwidth share, higher loss rate and delay per cycle

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energy efficient pons with service delay guarantees and...

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