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Case studies in energy consumption of Internet services:
Kerry HintonCEET
University of MelbourneAustralia
2
The future energy efficiency gaps
• Current data growth rate >> traditional energy efficiency improvement rate
• Technology is not keeping up with traffic growth– May suffer an “energy bottleneck”
0
2
4
6
8
10
12
14
16
2014 2015 2016 2017 2018 2019 2020
Nor
mal
ised
incr
ease
Year
Global mobile data 57% pa
Global IP data 23% paTrend mobile efficiency 20%
Trend core efficiency 15%
Efficiency gaps
Cisco, 2015GreenTouch, 2015
3
Centre for Energy-Efficient Telecommunications• Research centre located in the University of Melbourne
• Launched in March 2011
• Partnership between Alcatel-Lucent, the University of Melbourne and Victorian State Government
– $10 million for 2011 to 2015
– Additional funding of $2 million has extended CEET to 1st July 2016
• World’s first research centre focusing on energy-efficient telecommunication technologies
• Focus on collaboration between business and academia
• Major contributor to GreenTouch international consortium
4
Case study 1: Wireless access to the cloud• Cloud services widely promoted as greener than
on-site facilities:– Cloud Computing – The IT Solution for the 21st Century
• Carbon Disclosure Project Study 2011
– Salesforce.com & the Environment
• WSP Environment & Energy 2011
• Strong case for enterprise private cloud
• What about the public cloud?– Apple iCloud
– Google drive
– Microsoft sky drive
5
Consumer, interactive wireless cloud
- “free Google online storage, so you can keep ..- anything.”- “Your files .. can be reached from any smartphone, tablet, or computer. - “.. wherever you go, your files follow..”
- “It’s like an extra hard drive that’s available from any of the devices you use. ”- “whether you’re on your laptop on your new tablet, or on your phone .. you can get to your files in OneDrive.”
- “automatically and wirelessly store your content”- “automatically and wirelessly push it to all your devices”
Source: www.apple.com/au/pr/products/icloud/icloud.html
Source: www.google.com/drive/
OneDrive
Source: windows.microsoft.com/en-us/windows-8/getting-started-onedrive-tutorial
6
1.78
1.65
0.30
0.38
0.01
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Ave
rage
po
we
r (
GW
)
Wireless cloud 2015 averagepower consumption
Metro & Core
Data Centre
WiFi hotspot
Home WiFi
4G
90%
10%
Wireless cloud power
4G LTE67%
HOME WiFi25%
WiFiHOTSPOT8%
User technology
JO
UL
ES
PE
R B
IT
1
10
100
1000
10000
Home WiFi WiFi hotspot
4G Network Data Centre
Energy efficiencyEnergy efficiency
WiFihotspot
Home WiFi+ PON
4G
Metro&
Core
DataCentre
InternetUser
Public data centre
CEET, 2013
7
Case study 2: Interactive cloud servicesx105
Source: Vishwanath et al. 2013
(AJAX)
Interactive cloud services
8
• Local processing (not online) 0.2 W
An order of magnitude less power than on-line
• Decision to use the cloud depends on ...
Power consumption of end-user device (high vs. low)
Network to access the cloud (wired vs. wireless)
Complexity of the task (less vs. more computation)
Exchange of data with cloud
Cloud services User goes online User already online
3G 5.9 W 2.2 W
WiFi 3.1 W 2.3 W
Ethernet 2.5 W 2.2 W
9
• Stunning growth of Facebook traffic:
– 240+ billion photos
– 350+ million photos added per day
– 750+ million photos were uploaded over New Year’s Eve
– 7000+ Tera-Byte memory added per month
• Facebook reports its annual data center energy consumption
Case study 3: Photo sharing via cloud
Then Now
10
Facebook eco-system
• Hot & Warm photos are distributed by a Content Delivery Network• Cold Photos are distributed directly from data centres
Data Centre
CDN
CDNAccess
Edge
Data CentreData Centre
Edge
Access
Access
Distant
Local
Core
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Traffic (measurement)
• Packet analyser software utility (Wireshark)
• Further compression on photos in user browsers before uploading to Facebook
• Exchanged Bytes for a
5MB Photo: – Laptop (Ethernet, WiFi)
• Upload = 500KB
• Download = 200KB
– Smartphone (4G, WiFi)
• Upload = 1.1 MB
• Download = 120K 0
0.2
0.4
0.6
0.8
1
0 1 2 3 4 5 6 7 8 9 10
Ob
serv
ed
tra
ffic
(M
Byt
e)
Original size of photos (MByte)
Download
Upload
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• Direct measurement : Power-mate (resolution of 10 mW)
• Uploading and downloading same 5 Mbyte photo
Energy: user device
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10
11
12
13
14
15
16
0 20 40 60
Po
we
r (W
att)
Time (sec)
Pidle
Start End
9
10
11
12
13
14
15
16
0 5 10 15 20
Po
we
r (W
att)
Time (Sec)
Pidle
Start End
5MB photo Laptop Mobile Phone
Ethernet WiFi 4G WiFi
Upload 106 J 114 J 40 J 23 J
Download 23 J 33 J 18 J 8 J
Upload Download
13
Cellular44%
WiFi Hotspot
24%
Ethernet16%
Home WiFi16%
Users’ traffic profile
• 350+ million photos upload every day
• Users have 140 friends on average.
• For a new uploaded photo
– Assume 90% of friends wants to look at the photo (126 friends)
Source: Cisco VNI, Global Mobile Data
Traffic Forecast Update, 2012–2017
Source: Cisco The zettabyte era,
2012-2017
Friend access technologies
14
12 GWh 292 GWh
• Facebook 2012 total data centre IT energy : 516 GWh(Source: Facebook)
• Total network energy consumption: 304 GWh
• Photo sharing network energy 60% of FB total data centre IT energy
– Wireless (4G/LTE) access network is main energy consumption
Sharing online network energy
15
Case study 4: Cloud storage for “Digital Universe”
• Digital Universe: all the data created by humankind
Source: EMC Digital Universe – Research & Analysis by IDC (7th annual study) – April 2014
http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm
16
How much power to store all this data?
• Today 40% of digital universe is stored in data centres
• Seagate: By 2020 60% will be stored in data centresSource: Seagate 2014
• Expect move toward more energyefficient storage technologies
• Will this save energy?High Perf.
HDD/SSHD
SSD
Capacity HDD
Near Online HDD(MAID – Massive Array of Idle Disks)
Offline Removable Media(tape, optical disks)
Warehoused Removable Media(tape to 30 years, optical disks to 1,000 years)
Tier 0
Tier 1
Tier 2
Tier 3
Tier 4
Tier 5
– Solid State Drive
– Solid State Hybrid Drive
– Hard DiskDrive
Incre
asing e
ne
rgy efficie
ncy
17
Data storage in 2020• Consider three scenarios for 2020:
1. Same storage Tiers as 2013 (BAU)
2. Better use of lower power storage Tiers
3. Best likely use of storage Tiers
30%40%
50%
12%
20%
22%52%
34%22%
5% 5% 5%1% 1% 1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
3.BAU-60% 4.Improved 5.Best Likely
Pro
po
rtio
n o
f St
ora
ge
Tier 0 - SSD
Tier 1 - Hi PerfHDD/SSHD
Tier 2 - Cap. HDD
Tier 3 - Idle HDD
Tier 4 - Removable
1.BAU 2.Better 3.BestSource: Horman & Campbell 2014
18
2020 Digital Universe data storage power
• Without a significant transition to lower power storage data storage power will almost triple
• Need to re-think data retention policies
40.23
28.40
15.84 19.64
0
5
10
15
20
25
30
35
40
45
2013 2020
Pow
er (G
W)
Year
BAU
Better
Best
Source: Horman & Campbell 2014
19
Case study 5: Internet of Things (IoT)
• IoT forecasts– 212 Billion connectable things by 2020
– 4.6 Petabyte /month in Global M2M traffic by 2019
– Data increasing at 71% increase per year
• Mainly small IPv6 packets
• Increasingly sophisticated “things”IoT eco-system
Gray et al. 2015
Dominant power consumption
Cisco VNI, 2015
IDC, 2014
20
Power Consumption Model
• From “thing” to terminal unit (central office)
PGateway PCPE PRN PTU
RN TUIoT Gateway CPE RN TU
RN TU
P PP P P X X
N N
Gray et al. 2015
Case Study 5
21
Results• VDSL2 is least power-efficient while Shared Wi-Fi (with PON) is most
efficient.
• LTE is mostly efficient for bitrates below 75 kb/s but least efficient above250 kb/s
1
10
100
1 10 100 1000
Pow
er C
on
sum
pti
on
(W
atts
)
IoT Data Access Rate (kb/s)
PON
Single UserWi-Fi
VDSL2
Shared Wi-Fi
PtP
LTE(40% of BS traffic Ave.)
LTE(20% of BS traffic Ave.)
LTE(100% of BS traffic Ave.)
Gray et al. 2015
BS = LTE base station
22
Case study 6: Energy efficiency, CO2 and “rebound”
• Will the energy efficiency of e-services, of itself, provide for carbon footprint abatement?
• “Jevon’s paradox” (Rebound effect)– Improving efficiency will stimulate economic activity & increase
power consumption
Reduce energyconsumption
Improve energyefficiency
Intended cycle
Reduce energy costs
Stimulate demand
Increase energy consumption
Jevon’s
Paradox cycle
Suesβpeck, 2015
23
Australia’s National Broadband Network
• Nationwide broadband network
– Multiple technologies
– Covers entire nation
– Completion date: early 2020’s
• Replace hi-CO2 materialservices with lo-CO2
e-services
Access technology % premises
FTTP: Fibre to the premises 24%
FTTN: Fibre to the node 30%
FTTdp/B: Fibre to the distribution
point or building11%
HFC: Hybrid fibre-coaxial cable 28%
FTTN (remote footprint) 1%
Fixed Wireless 3%
Satellite 3%
Total premises 100%Source: NBN Co. Strategic Review, Exhibit 4-2, p. 97.
24
Economic model• Use Computable General Equilibrium (CGE) economic model
– TERM at Centre of Policy Studies: Victoria University, Aust.
• Include added national debt to pay-off NBN construction
• Compare economy relative to “No-NBN” future
– FTTH in city centres & business areas
– Primarily DSL with some HFC elsewhere
• Two data rate scenarios
Scenario Down stream Up stream
Modest data rate > 12Mbps entertainment, < 2.5Mbps rest
< 5Mbps
High data rate 2.5-10 Mbps Higher Ed. &Telehealth,10-25 Mbps rest
≤ 10 Mbps
25
Six Broadband Services1. Cloud Computing for business.
– Savings depend on the ICT intensity in economy sector.
2. Electronic Commerce for business & government.
– Significant productivity improvements.
3. Online Higher Education
– MOOCs’ with some face-to-face supervision.
4. Telehealth Practice
– Telehealth for aged care, teleconsulting & teleconsulting.
– Broadband-enabled locations in regional areas
5. Teleworking improving productivity & enabling greater labour-force participation.
6. Household entertainment Services
26
-2 -1 0 1 2 3 4
Export volume
Real government consumption
Real investment
Average real wage
Aggregate employment
Consumer price index
Nominal household consumption
Real household consumption
Nominal GDP
Real GDP
Percentage change (%)
Cloud Computing
Electronic Commerce
Online Higher Education
Telehealth Practice
Teleworking
Online Entertainment
Debt-servicing Repayments
Long-Term: High Bitrate Requirements
27
Long-run carbon footprint
• Jevon’s Paradox applies: Increased CO2 due to 2% increase in economic activity
• Need policies to adopt lo-CO2 practices
-0.5 0 0.5 1
Cloud Computing
Online Higher Education
Electronic Commerce
Teleworking
Telehealth Practices
Online Entertainment
Transport
Debt-servicing repayments
NBN incremental energy use
Percentage change compared with BAU (%)
NB
N im
pact
are
as
Scenario: High Service RequirementsTypical Long-Run Year post NBN Deployment
Carbon dioxide equivalent
28
National Broadband Networks
• The NBN will boost the Australian economy by about 2% with increase in national welfare
– Telehealth practice and Teleworking will be most beneficial
• It takes more than better Entertainment to make the NBN economically beneficial
– But a couple of valuable services using the NBN’s increased capabilities will be enough to make the NBN worthwhile
• Ubiquitous broadband alone will not move an economy to lower carbon footprint
– Need “greening with ICT” policies
• For example GeSI: SMARTER 2030
29
Case study 7: Minimising network power• Does minimising network power require global controller?
– Enforce optimal solution on all parties
• Real networks have many independent players
– Service providers, carriers, regulators, users
• Use Game Theory
– Multiple service providers (players) use a common network
– Seek Nash Equilibrium for the game
• Each service provider strategy is weighted balance of:
– Minimising their power consumption
– Balancing the network load
– Minimising their service delay
• How close is Nash Equilibrium to globally controlled power?
30
• Partial USNET topology
– 4 user nodes (Input)
– 4 server nodes (players)
– 14 network nodes
– 50 predefined routes between users and server nodes
• Input traffic over a diurnal cycle
SimulationUser node
server nodenetwork node
31
Results• Provided
– Players share enough network resources
– Objectives are relatively aligned• Weights for three factors approximately equal
• Power consumption of Nash Equilibrium is close to the global solution
– Don’t needglobal controller
32
Power consumption of the global Internet
Year
15% p.a. technology improvement
Po
we
r C
on
su
mp
tio
n (
W)
109
1011
1010
108
1012
2010
2015
2020
Access (PON)
Global electricity supply (3% p.a.)
TotalTotal (2010 Technology)
Tucker, OFC 2011
33
GreenTouch
• Bell Labs initiated global research consortium including
– Industry
– Government
– Academic organizations
• Fundamental research to pre-competitive innovations
• 48 member organizations with 350+ leading scientists
• Recognized by the World Economic Forum as an industry-led best practice toward sustainability
• Finalist for Edison “Collective Disruption” Award 2016
• Launched in May 2010
• Final report on June 2015 (www.greentouch.org)
• Silver medallist “Collective Disruption”: Edison Awards 2016
Deliver by 2015 architectures, specifications and solutions and demonstrate key technologies to increase network energy efficiency by a factor 1000 compared to
2010
34
GreenTouch
• Energy efficiency technology roadmap for 2020:– Requires all network equipment to be the latest generation
– Will require all equipment to be upgraded by 2020
– This is financially challenging over a 5 year period
• However GreenTouch has shown that the technologies are there to secure major improvements
Efficiency improvement
Traffic growth(2010 to 2020)
Net energy reduction
(2010 to 2020)
Mobile Access 10,000x 89x 99%
Fixed Access (consumer) 254x 8x 97%
Core network 316x 12x 96%
GreenTouch, 2015
35
The longer term future
• Most of the energy efficiency gains are “once-of”– Can not be used time and again to get continual gains each year
• Expect traffic to continue increasing
• Need a new paradigm to make ICT perpetually sustainable into the future
Energy bottleneck?
M. Weldon, 2015
Energy efficient solution
Business as Usual
Wireless access
“once-of” solutions
New paradigm
Thank you