service and energy awareness in optical networks dan kilper g

Service and Energy Awareness in Optical Networks Dan Kilper G. Atkinson, K. Guan, J. Llorca, Y. Pan

What is GreenTouch?

Broad, open and global consortium executing research projects to

achieve aggressive goal

Roadmap organization establishing reference architectures and

research targets to overcome major challenges facing network

scaling and energy

Venue for cooperation and enabling demonstrations among research

organizations

Forum for the exchange of information on energy trends,

challenges, & research on communication networks

2 GreenTouch Introduction | 2012

GreenTouch Mission By 2015, our goal is to deliver the architecture, specifications and

roadmap — and demonstrate key components — needed to increase

network energy efficiency by a factor of 1000 from current levels.

© 2012 GreenTouch Consortium

GreenTouch Members

3

Athens Information Technology (AIT) Center

Bell Labs, Alcatel-Lucent

Broadcom

CEA-LETI Applied Research Institute for Microelectronics

China Mobile

Chunghwa Telecom

Columbia University

Commscope/Andrew

Dublin City University

ETRI

ES Network/Lawrence Berkeley Labs

Fondazione Politecnico di Milano

Fraunhofer-Geselleschaft

France Telecom

Fujitsu

Huawei

IBBT

IMEC

Indian Institute of Science

IIT Delhi

INRIA

KAIST

Karlsruhe Institute of Tech.

Katholieke Universiteit Leuven (K.U. Leuven)

King Abdulaziz City for Science and Technology

KT Corporation

National Chiao Tung University

National ICTA Australia

Nippon Telegraph and Telephone Corp

Politecnico di Torino

Portugal Telecom Inovação, S.A.

Samsung (SAIT)

Seoul National University

Shanghai Institute of Microsystems & Information Technology

Swisscom

TNO

Tsinghua University

TTI

TU Denmark

TU Dresden

University College London

University of Cambridge

University of Delaware

University of L’Aquila

University of Leeds

University of Manchester

University of Maryland

University of Melbourne CEET

University of Missouri-KC

University of New South Wales

University of Paderborn

University of Rochester

University of Toronto

Utah State University

Vodafone Group

Waterford Institute of Technology

ZTE

GreenTouch Introduction | 2012

© 2012 GreenTouch Consortium

ALL RIGHTS RESERVED. COPYRIGHT © ALCATEL-LUCENT 2012. 4 |

SEASON: Architecting an Energy-Efficient Service-Centric Network

IP InternetIP Internet

WirelessWireless

PONPON

EnterpriseEnterprise

App

Center

IP InternetIP Internet

WirelessWirelessWirelessWireless

PONPONPONPON

EnterpriseEnterpriseEnterpriseEnterprise

App

Center

• Historically networks have found success by providing a common agnostic platform to support a range of activities, but this comes at a cost in terms of energy that increases as the diversity of Internet services expands…

• SEASON is a clean-slate network design project focusing on opportunities to design networks for maximum energy efficiency through awareness of service requirements

• Focus on services with high bandwidth

• Elephant flow picture: 90% of traffic due to 10% of flows

• Understand from clean-slate perspective how service requirements (bandwidth, duration, latency, multi-cast, security, protection,…) impact energy

• Focus on core network dynamic functionality and connect with other groups and projects on transmission design, switching hardware

YEAR

2000 2005 2010 2015 2020 20251995

AN

NU

AL

GR

OW

TH

RA

TE

(%

)

250

200

150

100

0

50

300

High growth,

early phase

Constrained growth,

mature phase


Different Directions

Energy follows load,

maximum efficiency in

today‟s network

„Service aware‟,

function specific

hardware

2008 2010 2012 2014 2016 2018 20201

10

100

Po

we

r/U

se

r (W

)

Year

BAU

Optimistic

Improvements

1010.1

Average Area Efficiency (GOPS/mm2)

0.001

1

10

0.01

Ave

rage

En

erg

y Ef

fici

en

cy

(GO

PS/

mW

)

µProc

Prog. DSP

Dedicated

100

0.1

FPGA

ISSCC & VLSI 1999-2011, averaged

> 100x improvement

< 10x improvement

VLSI chips

Apply ‘ASIC’ concept to networks

A.

B.

Kilper, et. al., JSTQE 2011


Bell Labs Univ. of Toronto

CEET

SEASON Activities & Partners

Participants: • Bell Labs (Holmdel, Murray Hill, Seoul, Stuttgart): D. Kilper

• Columbia: K. Bergman, G. Zussman (CIAN Center)

• UNSW: V. Sivaraman

• INRIA: L. Lefevre

• Toronto: L. Pavel

• CEET: K. Hinton

Energy & locality aware

placement and execution of

m-data center services

Energy-aware wavelength

routing & protocols

Univ. of Toronto

Columbia Univ.

INRIA

CEET

Bell Labs

Multi-fiber, silicon-photonic

fast switching & control

devices End-to-end coding

Robust & distributed

multi-layer control

Service-aware

flow switching

/CCN/CDN

Enterprise

App

Center

Bell Labs

UNSW

Bell Labs

Bell Labs

Columbia Univ. Univ. of Toronto

• ESnet: I. Monga


• Service-dependent networks: two steps

• Determine minimum network energy for service categories with different characteristics

• Identify how energy depends on characteristics: delay, transaction size, security, multi-cast,…

• Merge service category solutions to find a total network solution

• Clean-slate: consider bottom up solutions

• Highly efficient optical transmission

• How do we minimize large non-transmission (control, coding, thermal,…) energy load?

• How do we maximize efficiency of optical link? Move toward interconnect levels?

• How do we achieve bandwidth efficiency?

• Simplified switching & routing

• How do we match circuit and packet operation to service needs at hardware level

• Determine energy cost for service dependent requirements: latency, security, QoS,…

• Determine protocols based on hardware & service requirements

• End-to-End

• Service dependent coding and capacity impacts all layers

• Utilization, QoS, wavelength switching require coordination across layers

• Low utilization at high efficiency: dynamic operation

Key Issues in SEASON

Main SEASON

differentiators


Large gap between basic devices and commercial

systems

Where does the energy go?

2010 2015 2020 20251E-12

1E-11

1E-10

1E-9

1E-8

1E-7

1E-6

1E-5

Switches

Transmission

En

erg

y p

er

Bit (

J)

Year

Transmission

Routers &

Switches

Lower Bound

Analysis

Current

Trends

~ 104

Difference

Tucker JSTQE 2011

Kilper JSTQE 2011


• QoS, security, coding, queuing:

• Can we eliminate redundant processing or push to the edge?

• Efficient placement of content/processing

• Layering, protocols oblivious to HW

Service Requirements…

Metro

Client

Data Center

Long Haul

Access

• Source Coding

• Cloud processing

Channel Coding Source Coding

• Pattern-

matching

• Security

• QoS

• Addressing

• Queuing

Chop data

up into

individually

wrapped

packets


Tucker 2011

XC

Clean-Slate Perspectives

Network

Equipment

Policy matching, IP

addressing, QoS,

Security, Protocols buffer

Buffer & Switch

Networks

~ mJ/bit

~ 1 nJ/bit

By understanding the energy cost of

different functions we can design

networks for the requirements of high

bandwidth services & make choices

buffer

Add new eff

optical

capabilities

Dynamic l Network End to

End


Bandwidth Leverage: More with Less

• Reduce buffering & switch energy through excess transport bandwidth

• Illusion of excess bandwidth by turning up and re-directing wavelengths

• Use service awareness to schedule app center processes

• First need to minimize common equipment/infrastructure energy

• Second need to solve challenges prohibiting dynamic capability

Dynamic l Network

Enterprise

Monthly

Daily

Micro-Data

Center


• Duration of wavelength connection: T* = B/C

• B = Total data flow, C = wavelength capacity

• For wavelengths C > 10 Gbits/sec

• ~ 1 Gbyte = 1 sec

• Wavelength tear down delay: Td

• Wavelength setup delay: Tu

• Total WoD delay: T0 = Tu + Td

• EWoD ~ Plink (T0 + B/C) = gPlinkB/C

• γ >> 1: Inefficient wavelength utilization

• Today T0 ~ 10 min. B > 750 Gbytes for g < 2

CB

CBT

/

/

g

Compare B with CT0:

Control-delay bandwidth

product is key to efficiency

for dynamic wavelengths

Wavelength on Demand: Turn up + Tear Down Delays

λ2

λ3λ1

B C

A


Network and Traffic Model

CDN Networks

• Characterize the relationship between hop

distance Hr and no. of caches n

Traffic Model

• File download rate vs. file size

• Use bounded Pareto distribution to model

heavy-tailed traffic

12802064 N

0 200 400 600 800 1000 1200 14000

5

10

15

20

No. of Copies

Ho

p D

ista

nce

46.0

64.0)(

n

NnHr

102

103

104

10-7

10-6

10-5

10-4

10-3

10-2

10-1

File Size

Dow

nlo

ad R

ate

Large Small - frequentdownload oflarge files

UL

B

B

L BxBxB

xf

U

L

,1

)()1(


10-4

10-3

10-2

10-1

100

101

10-8

10-7

10-6

Normalized File Size (Bk/C

l T

)

Op

tim

al C

om

bin

ed

Tra

nsp

ort

a

nd

Sto

rag

e E

ne

rgy P

er

Bit (

J/b

it)

CDN without Bypass, =0.2

CDN with Bypass, =0.2, pd

wdm/p

d

r=10

-1

CDN with Bypass, =0.2, pd

wdm/p

d

r=10

-2

~ 100 Mbits ~ 1 Gbits ~ 10 Gbits ~ 100 Gbits~ 10 Mbits

Dynamic Wavelength Power Reduction Potential for CDN

pwdm/pr = 0.1

pwdm/pr = 0.01

Frequent download of very large files

• Better wavelength utilization

• Trade transport energy with storage energy

BL=10 Mb, BU=1 Tb, t=1 day, R=105, To=5 s, Cλ=10 Gb/s

r

d

wdm

dk

p

pTCB

l

K. Guan, et. al., INFOCOM 2011


Our Approach

Connection Time/Setup Time = Size/T0BW

En

d t

o E

nd

Eff

icie

nc

y (

bit

/J)

Pacing, Sched EFIC

OST

Service

Constraints

How do we define these curves?

Where are the transition points?

What controls these curves?

Dedicated

Network

Rest of

Network

Today 2020 Beyond


Dynamic Wavelengths

• Small Networks: solved problem

• Matisse networks, lab demos

• ~ 4 amplifiers, ~ <40 chans, rings, OOK

• Large Networks: complex/challenging engineering rules & network control, transients & power dynamics, advanced modulation formats

• > 4 amplifiers, > 40 channels, complex mesh, phase sensitive

• Dynamic wavelength capability similar to transmission technologies:

• With infinite money and resources can completely solve problems

• Problems get harder depending on distance and capacity (and speed in this case) – need higher performance devices, algorithms

• Trade-off performance vs cost for given network

• Additional trade-off related to energy

• Algorithms/control that leads to lowest overall energy

• Trade-off performance/service req. and energy

SEASON

Stability Problem

19/09/2012 17

𝑣1

𝑦21

𝑦12 𝑃1

𝑃2

𝑦11

𝑣2 𝑦22

𝒘𝟏𝟐 = 𝒚𝟐𝟐

𝒘𝟐𝟏 = 𝒚𝟏𝟏

19/09/2012 18

Case Description Result

1 1 WSS + integral

control

Impossible to oscillate

2 2 WSS + 2 sets of

integral control

Impossible to oscillate

3 1 WSS + (state +

integral control +

delay)

With small adaptation dynamic on one

of the channels. Relaxation oscillation

is possible

4 2 WSS + 2 sets of

(state + integral

control + delay)

Network of passive oscillators (as case

2) will oscillate at right bifurcation

values

Oscillation Analysis Results

Z. Wang, J. Tsai, P. Yan, D. C. Kilper, and L. Pavel, “Oscillation Analysis for a Quasi-

ring Optical Network” American Control Conference, Montreal, July 2012.


Enhanced Network Simulator (NS3)

(ECOFEN)

ATOM

Packets, IP,

TCP, buffers

Wavelengths,

RWA, GMPLS,

Impairments

ATOM: A Transparent Optical Mesh Simulator ECOFEN: Energy Consumption mOdel For End-to-end Networks

Bell Labs Columbia Toronto

SEASON Contributors INRIA UNSW CEET

• Cross-layer simulation platform: combine widely used NS3 with ATOM simulator

• Enable cross-layer analysis that would not otherwise be possible

Simulator for SEASON & GreenTouch


Application Centers: Energy Optimized Selective Services

GW 1 2 n …

• Micro Data Center (MDC)

• Limited number of pre-provisioned high quality, high-bandwidth services

• (CDN ++: Content distribution + processing)

• Includes transparent pass-through services

• Use natural cooling, PUE ~ 1.05

• Enhanced capabilities for on demand instantiation of “personalized” services

• residential “co-processor”, virtual home gateway

• Super-central office

• Highly efficient, optical access to the HQ HB services: low power, long-reach PON

• GW: Separation and bandwidth restriction of IP (Internet) traffic

• Connects directly to other App Centers

• Scheduled & adaptive dedicated network highly coordinated with service delivery/CDN

IP

Internet

PON

Enterprise

Wireless

Micro Data Center

Applications

l

Dynamic l

Network


Multi-View Video J. Llorca, et. al. INFOCOM 2012, ICC 2012

Trade-off Video Processing & Transport Energy

More Views = More Data & More Processing

Best Architecture Depends on Processing Energy

Use dynamic wavelengths to stay on low energy curves

Greater than 10x difference depending on architecture

Greater energy

savings with

dynamic network

topologies

Software Coding Hardware Coding Increasing Processing Power

Distributed processing centers use more power as the processing power increases

Distributed

Optimal

Access

Video

Processing

Special

View 1

Core network

Special

View Ms

User

User

View

1

View

2

View

M

View

1

View

2

View

M

Transport M

captured views Processing

node

Transport

processed views

Transport

processed views


CCN vs. Centralized CDN with Optical Bypass:

CCN becomes more energy efficient as the frequency of requests increase

• Break-even point depends on topology and traffic

Energy-Efficient CCN

CCN vs. Distributed CDN:

Energy efficiency depends on characteristics of content library

• Content catalog size and popularity distributions

• E = Energy/bit

K. Guan, et. al., “On the Energy Efficiency of Content

Delivery Architectures” ICC 2011

0 0.2 0.4 0.6 0.8 10.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

EC

CN

/EC

DN

Ring, N=64

8X8 Grid, N=64

IP Backbone, N=24

EON, N=19

0 0.2 0.4 0.6 0.8 10.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

in Zipf Distribution, F=1000

EC

CN

/E

CD

N

Ring, N=64

8X8 Grid, N=64

IP Backbone, N=24

EON, N=19

CCN is more efficientfor small-sized catalog

Distributed CDN is more efficientfor large-sized catalog

in Zipf Distribution, F=50

101

102

103

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4x 10

-6

No. of Requests, Rk

Op

tim

al

En

erg

y p

er

bit

, E

bit (

J/b

it)

Ring, N=64, CCN

Ring, N=64, Bypass

8X8 Grid, N=64, CCN

8X8 Grid, N=64, Bypass

IP Backbone, N=24, CCN

IP Backbone, N=24, Bypass

EON, N=19, CCN

EON, N=19, Bypass

Break-even points


• Potential for deep energy savings through service aware/centric design

• Most of the energy is consumed by service driven features

• Requires clear understanding of trade-offs and choices

• Bottom up clean-slate, end-to-end

• Takes large effort over time to build up models, tools

• Use flexible models to incorporate full range from ideal to commercial

• Better enable use of optical switching for efficient high BW network functions

• GreenTouch SEASON Project

• Large effort both inside GT and in cooperation with other organizations

• Address problems from transmission to applications

Conclusions


Kilper et. al., JSTQE 2011

2010-2020:

10x

2010 2015 2020

0.1

1

10

100

Pow

er/

Use

r (W

)

Year

Fixed Access WDM

Mobile Routing & Sw

Low

High

Reduce

Processing?

BAU Energy Trends & System Over-provisioning …

Time

Cap

acit

y

Over-

provisioning

service and energy awareness in optical networks dan kilper g

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