going green introduction to network energy- efficiency research · 2013-01-16 · going green...

Going Green Introduction to Network energy-efficiency research

Inder Monga

Chief Technologist

Energy Sciences Network, Scientific Networking Division

Lawrence Berkeley National Lab

January 16th, 2013

Lawrence Berkeley National Laboratory U.S. Department of Energy | Office of Science

Talk Objective: Three Questions

1.  Why is network energy efficiency important as a research topic?

2.  What is Greentouch doing, and why is it relevant?

3.  What can network operators, administrators do right now?

Thanks to Greentouch, especially Thierry Klein, for their approval and Sharing slides to be presented at this meeting


The New World of Devices and Data

Lawrence Berkeley National Laboratory U.S. Department of Energy | Office of Science 4

2010 2015 202010-2

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Traf

fic (T

b/s)

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Wireless Data

Internet Video

Wireless Voice

P2P

Data from: RHK, McKinsey-JPMorgan, AT&T, MINTS, Arbor, ALU, and Bell Labs Analysis: Linear regression on log(traffic growth rate) versus log(time) with Bayesian learning to compute uncertainty

North America

Traffic doubling every 2 years

• 40% per year • 30x in 10 years • 1000x in 20 years

Exponential growth in total traffic continues

ESnet is growing at 10x every 4 years


Data Growth continues to be Worldwide

MORE DATA MEANS MORE

POWER

78 Mtons of CO2 5 000 000 towers = 5 000 000 000 people

without broadband

Today Future

§  17.5 GigaWatts §  ~ 9 Hoover Dams §  ~ 15 nuclear power

plants

§  ~ 15M car emissions a year

§  ~ 150,000 Paris to New York round-trip flights

Lawrence Berkeley National Laboratory U.S. Department of Energy | Office of Science 6

Slow-Down in Technology

Network energy efficiency

only increasing at 10-15% per year


Growing Network Energy Gap!

+27% INCREASE

ENERGY

Energy consumption in communications service provider (CSP) networks is forecast to increase by 27% from 2012 to 2016

=5th INTERNET

If the internet was a country: energy consumption is higher than Russia and a little less than Japan

HIGHEST COUNTRY

2010 2015 202010-2

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Wireless Data

Total Backbone

Internet Video

Wireless Voice

P2P


North America

2010 2015 202010-2

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fic (T

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Wireless Data

Total Backbone

Internet Video

Wireless Voice

P2P

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P2P


North America

2005 2010 2015 2020

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Growth

Y ea r

Mobile Data

Internet Backbone

Mobile Efficiency

WirelineEfficiency

Growing Gap!

Traffic

2005 2010 2015 2020

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Growth

Y ea r

Mobile Data

Internet Backbone

Mobile Efficiency

WirelineEfficiency

Growing Gap!

Traffic





3.  What can network operators, administrators do right now?



By 2015, our goal is to deliver the architecture, specifications and roadmap — and demonstrate key components and technologies —

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

2010 2015 20201E-4

1E-3

0.01

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Effic

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b/s/W

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1000x Target

Total Network: BAU

2010 2015 20201E-4

1E-3

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Total Network: BAU

GREENTOUCH MISSION (www.greentouch.org)

Global research consortium representing industry, government and academic organizations

Launched in May 2010

52 member organizations

300 individual participants from 19 countries

25+ projects across wireless, wireline, routing, networking and optical transmission


Over 15 research programs and 25 research projects underway

•  Wireless and mobile communications

•  Wireline access

•  Core networks and optical transmission

•  Services, applications and trends

New approaches being taken: •  Devices and low power electronics / photonics

•  Architectures, algorithms and protocols

•  “Power-follows-load” intelligent management

•  Service and energy optimized networks

Two major public demonstrations in wireless and fiber-to-the-home technologies

Establish and define common reference architecture and roadmap with strategic research directions

© 2012 GreenTouch ConsortiumGreenTouch Introduction | 2012

GREENTOUCH Status

0.0

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2010 2012 2013 2014 2015 2016 2017 2018 2019 2020 2022

W/s

ubsc

riber

Wireless LANOLT(/subscriber)HGW processorWireline LAN (Eth.)PON digitalOE PON

GPONXGPON

EE HW design

Long reach

Virtual HGW

BI PON

Low power electronics

Transparent CPE

Low power Optics

Sleepmode 2

Sleepmode

Short Term Long TermMedium Term0.0

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2010 2012 2013 2014 2015 2016 2017 2018 2019 2020 2022

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ubsc

riber

Wireless LANOLT(/subscriber)HGW processorWireline LAN (Eth.)PON digitalOE PON

GPONXGPON

EE HW design

Long reach

Virtual HGW

BI PON

Low power electronics

Transparent CPE

Low power Optics

Sleepmode 2

Sleepmode

Short Term Long TermMedium Term


Buffer

InputQueuing

ReceiveFwd

Engine

FabricInterface

OutputFwd

Engine

OutputQueuing

L2 BufferingOptics Framer

Buffer Mem

L2 BufferingOptics Framer Switch

Fabric

Switch Fabric

Switch Fabric

Fabric Interface

L1+L2 L3

Switch

18 Chip-to-chip Interconnects

Buffer

InputQueuing

ReceiveFwd

Engine

FabricInterface

OutputFwd

Engine

OutputQueuing

L2 BufferingOptics Framer

Buffer Mem

L2 BufferingOptics Framer Switch

Fabric

Switch Fabric

Switch Fabric

Fabric Interface

L1+L2 L3

Switch

18 Chip-to-chip Interconnects

Router T1600 (640Gb/s)

0

1,000

2,000

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5,000

6,000

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0% 20% 40% 60% 80% 100%

Load

W

Router T1600 (640Gb/s)

[From Kharitonov 2009]

D.Kharitonov, “Time-Domain Approach to Energy Efficiency: High-Performance Network Element Design” 2009 IEEE GLOBECOM Workshops

http://www.caida.org/research/traffic-analysis/pkt_size_distribution/graphs.xml

IPv4 Cumulative

INTERCONNECTS

PACKET SIZE

ENERGY DOES NOT

FOLLOW LOAD

Router Limitations


Network equipment hardware (routers and switches)

•  Architecture and components •  Functions, features and dimensioning •  Low energy technologies (including electronics,

photonics, etc) •  Power measurements

Network topologies and architectures •  Tradeoff between optical and electronic data transport •  Optimal joint IP-optical network design •  Packet versus circuit-switched architectures •  Energy efficient and simplified routing

Integration of application and transport layers �  Cross-layer optimization for efficient content distribution

•  Traffic engineering - Bandwidth allocation & traffic grooming - Elimination of over-provisioning - Efficient protection and restoration - Multicasting, elimination of junk and redundant

traffic ….

•  Network management, operation and control �  Quality of service support �  Network-wide reconfiguration and control of

network elements (offline or online). Holistic, end to end approach

�  Protocols and algorithms for managing and controlling network elements

�  Control and data plane �  Energy and traffic monitoring

Focused on components, technologies, systems, algorithms and protocols at the data link layer (L2), the network layer (L3) and the transport layer (L4) as well as interactions with lower and higher layers and research efficiencies that can be obtained from cross-layer optimizations and

joint designs

Core Routing and Switching Research Group


§  Athens Information Technology (AIT)

§  Bell Labs (Chair: Thierry Klein)

§  Broadcom

§  Chunghwa Telecom

§  Columbia University

§  Dublin City University

§  Electronics and Telecommunication Research Institute (ETRI)

§  Energy Sciences Network / Lawrence Berkeley Labs

§  Politecnico di Milano

§  Freescale Semiconductor

§  Fujitsu

§  Huawei Technologies

§  IBBT

§  IIT Delhi

§  INRIA

28 members organizations with 67 individual members

§  KAIST

§  Karlsruhe Institute of Technology

§  Nippon Telegraph and Telephone Corporation

§  Politecnico di Torino

§  Samsung Advanced Institute of Technology (SAIT)

§  Seoul National University

§  University of Manchester

§  University of Melbourne

§  University College London

§  University of Cambridge

§  University of Leeds (Co-Chair: Jaafar Elmirghani)

§  University of New South Wales

§  University of Toronto

Membership from GT members


Timeframe: •  Consistent with GreenTouch timeframe •  Algorithms, architectures and technologies that can be demonstrated by 2015

•  With evolutionary improvements through 2020 •  Applied to 2020 traffic •  Comparison with 2010 traffic and 2010 technologies

Assumptions: •  Consider the traditional IP packet data network framework •  Alternative paths and technologies are possible, but more speculative and are not

expected to fit in the timeframe: •  Optical burst switching •  Content centric networking •  Adiabatic switching, quantum dot cellular automata, ….

Background and Assumptions


Contributing Members

SCORPION: Silicon Photonic Interconnect and single chip linecard



OPERA: Optimal End to End Allocation


Shufflenetwork

Shufflenetwork

4x4 switch element

4x4 switch element

Inputshufflenetwork

Outputshuffle

network

GatingSOAs

4x4 switch element

4x4 switch element

4x4 switch element

4x4 switch element4x4

switch element

4x4 switch element

4x4 switch element

4x4 switch element4x4

switch element

4x4 switch element

IP layer WDM layer


STAR: Switching and Transmission



ZEBRA: Zero Buffer Router Architecture



SEASON: Service Energy Aware Sustainable Optical Networks





3.  What can network operators, administrators do?



Green Research in networking is HARD!


Network is a significant part of power consumption for Network Operators Footprint of communication networks

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customer premises equipment

office network equipment

telecom operator networks

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219 TWh

CAGR: 10.1% 354 TWh

Paper submitted for the Optics Express ECOC 2012 Special Issue “Worldwide electricity consumption of communication networks”


Network is a significant part of power consumption for Network Operators Joint footprint

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data centres computers comm. netw.

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CAGR: 7%


Mantra

“you can’t manage, what you don’t measure”

OR “You can observe a lot by just watching.”

Yogi Berra


Monitoring and Visualization of Energy consumption in networks (MAVEN)

Goal:

•  Establish a baseline power profile for end-to-end networking

•  Pushing for efficiency and optimization, both at device-level and network-wide

•  Expose live power dataset to researchers

ESnet5: First nationwide R&E network instrumented for live power monitoring

•  100G optical and routing layer

•  Wide ranging traffic load: elephant and mice flows

•  Tentative plan for comprehensive power data by Jan 2013

Figure: Visualization of energy (alpha version, unreleased) consumed by ESnet’s ANI prototype network.


GreenSONAR (GLIF/OGF) Paola Grosso (UvA), Inder Monga (ESnet) and Cees de Laat (UvA)

1.  Measure Power Data, real-time (next presentation by Jon Dugan)

2.  Apply NM & PerfSONAR methods and architecture to Green & Energy information

•  What is the right model to represent and share this data?

3.  Share data multi-domain!

4.  Let researchers worldwide, use the data to build better, more-efficient network devices


§  ICT networks are growing rapidly §  Scaling networks is becoming more difficult §  Bringing focus to energy efficiency

§  ICT and research communities are organizing to address challenges

§  Dramatic, holistic change, but over long term evolution §  Cooperative organizations such as GreenTouch guiding evolution

§  Several promising research directions and initial results have been obtained

§  We can be providers of “real” data to the researchers

Conclusions

going green introduction to network energy- efficiency research · 2013-01-16 · going green...

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