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This article provides an overview of a network-based model of power consumption in Internet infrastructure. This model provides insight into how different parts of the Internet will contribute to network power as Internet access increase over time. The model shows that today the access network dominates the Internet's power consumption and, as access speeds grow, the core network routers will dominate power consumption. The power consumption of data centers and content distribution networks is dominated by the power consumption of data storage for material that is infrequently downloaded and by the transport of the data for material that is frequently downloaded. Based on the model several strategies to improve the energy efficiency of the Internet are presented.
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IEEE Network • March/April 2011
6
0890-8044/11/$25.00 © 2011 IEEE
he Internet has become an integral component of the
economies of all developed and developing nations.
The virtual cycle of improvements in telecommunica-
tions supporting economic growth, which, in turn, sup-
ports growth in telecommunications infrastructure has served
many nations very well. However, this cycle cannot continue
without end because all telecommunications networks require
resources to function, particularly (electrical) power, to oper-
ate. The larger the network becomes (in both capacity and
physical size) the more electrical power it consumes. Today
the information and telecommunications sector is responsible
for approximately 5 percent of the total electrical power con-
sumption in developed national economies [1]. The Internet’s
infrastructure consumes approximately 1 percent of a devel-
oped nation’s total electricity consumption in these countries
[2–5]. This percentage will grow as higher-speed national
broadband access networks are rolled out over the coming
years.
The rate of growth of the Internet, in terms of both uptake
and capacity increase, means that actually reducing its total
power consumption is unlikely to be a realistic goal. The net-
work is growing too fast. A more practical goal is to improve
the “energy efficiency” of the Internet. By energy efficiency
we mean the amount of data that could be conveyed from end
to end per quantum of energy consumed by the network. This
measure of energy efficiency is simply the reciprocal of the
energy per bit of data transported and/or processed.
Note that although we identify those parts of the Internet
that dominate its power consumption (i.e., watts or
watts/user), we discuss methods for improving energy efficien-
cy (i.e., reducing Joules per bit). The relationship between
these two quantities is power consumption (watts) is equal to
energy efficiency (Joules per bit) multiplied by the traffic vol-
ume (bits per second). We adopt this approach because the
Internet is a complex engineering structure, and any attempt
to improve the overall energy efficiency is best focused on
those parts that consume the most power. Therefore, a key
step in this process is identifying those parts.
In this article, we provide a broad picture of power con-
sumption by Internet infrastructure. We present a “high-level”
analysis of the factors that influence the power consumption
of the Internet’s infrastructure and investigate their relative
contributions. We conclude with an overview of some current
strategies for improving the energy efficiency of the Internet.
We initially focus on the power consumption of the consumer
Internet, excluding enterprise networks, data centers, and con-
tent distribution. We look at data centers and content distri-
bution later in the article.
Modeling Power Consumption of the Internet
A widely accepted method for modeling the power con-
sumption of Internet infrastructure and related information
and communications technology (ICT) infrastructure is
based on equipment inventory and/or sales figures [1, 3–6].
Using historical sales data of telecommunications equip-
ment, a broad picture of the quantity of equipment in the
network can be estimated. Together with information about
the energy consumption of this equipment, this approach
can provide a good “order of magnitude” estimate. Howev-
er, it does not expose the inter-play between demand growth
and the consequential power consumption. This is impor-
tant for estimating how future growth trends may change
power consumption patterns as more Internet-based ser-
vices are taken up.
A complementary approach uses a model based on telecom-
munications network design principles [2, 7]. In this approach,
the Internet is segmented into parts as shown in Fig. 1. For a
range of access rates, the energy consumption of each part of
the network is calculated using a paper design of the network
combined with manufacturers’ data on equipment energy con-
sumption for a range of typical types of equipment. This
approach enables an overall model of network power con-
sumption to be constructed and provides a platform for pre-
dicting the growth in power consumption as the number of
users and access rate per user increase.
Figure 1 is a minimalist representation of the network con-
figuration of the Internet. The major components of the net-
T
T
Kerry Hinton, Jayant Baliga, Michael Feng, Robert Ayre, and Rodney S. Tucker,
University of Melbourne
Abstract
This article provides an overview of a network-based model of power consumption
in Internet infrastructure. This model provides insight into how different parts of the
Internet will contribute to network power as Internet access increase over time. The
model shows that today the access network dominates the Internet’s power con-
sumption and, as access speeds grow, the core network routers will dominate
power consumption. The power consumption of data centers and content distribu-
tion networks is dominated by the power consumption of data storage for material
that is infrequently downloaded and by the transport of the data for material that is
frequently downloaded. Based on the model several strategies to improve the ener-
gy efficiency of the Internet are presented.
Power Consumption and
Energy Efficiency in the Internet
HINTON LAYOUT 3/2/11 12:01 PM Page 6
IEEE Network • March/April 2011
7
work are the access, metro, and core networks plus data cen-
ters and content distribution networks (e.g., for IPTV). This
model is a “first cut” representation of the Internet, and, as
such, does not include much of the fine detail of the Internet’s
true structure and topology. The model does account for the
typical hop count for packets that traverse the Internet [8].
The refinement to include a more realistic representation of
the Internet’s topology is ongoing.
The access network connects individual homes and busi-
nesses to their local exchanges. There is a range of technolo-
gies in use today and undergoing development. Digital
subscriber loop (DSL) uses the copper pairs originally
installed to deliver fixed-line telephone service. Fixed-line
telephone service, which uses the bandwidth below 3.4 kHz, is
left in place, and the higher-frequency bandwidth is used for
broadband services. Fibers to the premises (FTTP) installa-
tions most commonly use a shared passive optical network
(PON) or a point-to-point (PtP) Ethernet connection. In a
PON, a single fiber from the network node feeds one or more
clusters of customers through a passive splitter. An optical
line terminal (OLT) is located at the local exchange, and
serves a number of access modems or optical network units
(ONUs) located at each customer premises. ONUs communi-
cate with the OLT in a time multiplexed order, with the OLT
assigning time slots to each ONU based on its relative
demand. In a PtP access network, each ONU is directly con-
nected to the local exchange with a dedicated fiber to the
exchange.
In areas where the copper pairs are in good condition, a
fiber-to-the-node (FTTN) technology may be used. This tech-
nology uses a dedicated fiber from the local exchange to a
DSL access multiplexer (DSLAM) located in a street cabinet
close to a cluster of customers. A high-speed copper pair
technology, such as very-high-speed DSL, is used from the
cabinet to the customer premises. In areas where copper and
fiber are not available or feasible, wireless can provide Inter-
net access. Technologies for this include WiMAX, High
Speed Packet Access (HSPA), and Universal Mobile
Telecommunications System (UMTS). For wireless access a
wireless modem, located in the customer premises, communi-
cates with a local wireless base station, which, in turn, is con-
nected to the central office.
The local exchanges (or central offices) in a city are con-
nected to each other and to other cities via the metro/edge
network. This network also provides connection points for
Internet service providers (ISPs). The metro and edge net-
work serves as the interface between the access and core net-
works. The metro and edge network includes edge Ethernet
switches, broadband network gateway (BNG), and provider
edge routers. Edge Ethernet switches concentrate traffic from
a large number of access nodes uplink to two or more BNG
routers. The edge switch connects to two or more BNG
routers to provide redundancy. The BNG routers perform
access rate control, authentication, and security services, and
connect to multiple provider edge routers to increase reliabili-
ty. The provider edge routers connect to the core of the net-
work.
The core network comprises a small number of large
routers in major population centers. These core routers per-
form all the necessary routing and also serve as the gateway to
neighboring core nodes. The core routers of any one network
are often highly meshed, but have only a few links to the net-
works of other providers. High-capacity wavelength-division
multiplexed (WDM) fiber links interconnect these routers and
connect to networks of other operators.
We initially focus on the power consumption of the con-
sumer Internet, excluding enterprise networks, data centers,
and content distribution, returning to these later. We do not
address here the energy consumption of equipment within the
home network. The home network can take many forms,
ranging from a passive cable linking a PC to a modem,
through to a multimedia gateway with wired and wireless con-
nections to video, voice, and computing appliances, potentially
also including other networking hardware. A study of such
networks and their energy consumption is beyond the scope of
this article.
Figure 1. A high-level network structure with various options for the access network. Also shown are the metro/edge and core parts of the
public Internet, and some examples of network data centers and storage networks required to provide web-based services such as IPTV,
content distribution, and cloud computing. Power consumption in data centers and content distribution services are not included in the
model, and are considered later.
Cabinet
Cabinet
ONU
DSLAM
DSL
PON
FTTN
PtP
Wireless
Access network
DSLAM
OLT
OLT
Switch
OXC
Core router
Core network
Metro/edge network
BNG
Ethernet
switch
Edge
routers
BNG
IPTV network
Data center
Storage
Storage
Server
Server
Splitter
HINTON LAYOUT 3/2/11 12:01 PM Page 7
IEEE Network • March/April 2011
8
Estimating Power Consumption
To estimate the power consumption of the Internet’s infras-
tructure, an access bit rate (in bits per second) is selected.
Knowing this access rate and the access technology being used
(asynchronous DSL [ADSL], PON, wireless, etc.), and the
network design rules, one can calculate the capacity that must
be handled by the telecommunications equipment in the
access, metro, and core networks. For example, if the access
network is a PON (Fig. 1), the design rules may allocate 32
households to be connected to each OLT card port located in
the local exchange. Assuming the access rate per user is 10
Mb/s, the total capacity that must be handled by the OLT port
is 320 Mb/s. As the access rate increases, so does the capacity
that must be handled by the OLT.
The central office (local exchange) houses many OLT
cards, which are connected to an Ethernet switch in the
metro/edge network, as depicted in Fig. 1. This switch will
have a maximum capacity, and the number of OLT cards it
can accommodate determines the number of switches required
to deal with this capacity. This procedure is repeated to esti-
mate the total traffic within the edge and core networks as
well as the amount of equipment required across the whole
network. Using representative equipment for each part of the
network and employing the manufacturers’ specifications for
that equipment, one can calculate the numbers of devices
required to satisfy the total capacity generated by the chosen
access rate. Knowing the power consumption specifications of
the equipment provides the information required to calculate
the power consumption of the various parts of the network.
Although the results in the plots below are smooth, this is
an artifact of the approximations required to make analysis
tractable. The model is based on evaluating equipment with
adequate capacity to cope with the total demand (plus over-
provisioning for redundancy and growth). However, most net-
work equipment has a relatively flat load vs. power profile;
thus, deploying the next set of equipment to cope with an
increase in traffic causes a step increase in power consump-
tion. The actual power consumption would show a step-wise
form close to the smooth lines in these figures. The results
presented correspond to averaging out these deployments
over the whole network, thereby producing the smoothed
traces.
Principal Contributors to Overall Power
Consumption of the Internet
Due to space limitations in this article, we now focus on a
high-level view of the contributors to the power consumption
of the Internet. To do this we identify a number of key con-
tributors.
Network Equipment
The physical network equipment in the network is the major
contributor to the power consumption of Internet infrastruc-
ture. This includes equipment in the access, metro, and core
networks.
• Three technologies dominate the access network. These are
fiber (for PON and P2P), copper (for ADSL, VDSL, and
hybrid fiber coax [HFC]/cable modem) and wireless. The
aggregation of traffic, via statistical multiplexing, from end
users is an important function of the access network.
The metro network includes providing a gateway into the
metro and core networks. Local traffic requires routing
around city central and suburban areas. The rest is routed
into the core network.
The core network involves core routers and an inter-
city/international communications system that transport
Internet traffic between the core routers.
Many Internet services provided to access users require
exchanging information between the end users and service
providers’ points of connection to the Internet (often called
a point of presence or POP). The transport of this data is
“backhaul” and mainly uses wireless or Ethernet transport.
Network equipment must be powered and cooled. This
includes the provision of DC power to the racks that house
the equipment and the provision of an uninterruptible
power supply (UPS) that ensures continuity of power to the
network equipment.
Capacity Planning
Telecommunications network owners need to allow for traffic
peaks, future growth, and the protection/restoration of ser-
vices. This requires some overbuilding of the network which,
in turn, increases the power consumption of the network. Sec-
tions of the metro/edge and core networks can be 100 percent
or more overbuilt depending on the network design policies of
the owners.
Services/Cloud Facilities
A significant amount of Internet traffic arises from a wide
range of web-based services and resources available to end
users via the Internet. Examples of these include cloud ser-
vices, content delivery, and storage as a service. The data cen-
ters that provide these services require significant amounts of
equipment and power to function. For example, content ser-
vices require servers that store the data/content and regulate
access to it. Other services require servers hosting, processing,
and searching for data. The machines that provide these ser-
vices are usually networked via a local or wide area network.
These networks also consume energy.
Demographics
The Internet is an intra- and international network. The phys-
ical distance between population centers has a direct impact
on the power consumption of the network. Another important
demographic factor is the density of the premises in the popu-
lation centers. Widely spread premises will require more
power to connect to the local exchange.
Service Scenarios
Power consumption is strongly influenced by the type of ser-
vice being provided. The network will provide the following
types of services:
Shared services: Quasi-real-time or non-real-time shared
services such as email, web browsing, and video or audio
download, for which short delays are acceptable. These ser-
vices can be oversubscribed in that many users may share
the bandwidth provided without noticing any degradation in
speed.
• Dedicated services requiring quality of service (QoS): These
include services such as telephony (voice over IP, VoIP),
Internet TV, conferencing, and virtual classrooms. These
services cannot be oversubscribed. Dedicated capacity for
each service must be provided through the access and back-
haul networks to the server that is providing the content or
service.
• Real-time services delivered to multiple users via multicast.
Such services might include broadcast video, near-video-on-
demand, and Internet radio. One copy of each requested
service is streamed to a switch near the requesting cus-
tomer(s) and replicated to all requesting customers con-
nected to that switch.
HINTON LAYOUT 3/2/11 12:01 PM Page 8
Service Management
All networks and the services they provide must be monitored
and managed to ensure they are operating to expectation.
These functions add to network power consumption because
they require specialized systems and equipment to be inserted
into the network.
The focus of this article is on network equipment, not on
equipment and networks located within the home. Therefore
the analysis does not go beyond the DSL modem/ONU/cable
modem/wireless home network gateway or modem.
It is clear that the power consumption of Internet infras-
tructure is influenced by many factors. These factors often
interplay with each other. For example, content distribution
centers may communicate with each other and their cus-
tomers via the public Internet or a private network; depending
on demographic factors, this may be over very long distances.
These alternatives can have a significant impact on the power
consumption of these services.
Where the Power Goes
We have used the analytical methods described above to
develop a picture of power consumption in Internet infras-
tructure and to gauge which parts of the network consume the
most power. The details of this work are given in [2, 9–11]. In
the following sections we present an overview of some of the
key findings of our work. We start with the power consump-
tion of access networks. The details of the analysis are in [9],
and some key results are shown in Figs. 2 and 3, which pre-
sent the power consumed per user as a function of the access
rate provided to the user.
We characterize the access rate available to each customer
by the access rate advertised and sold to customers by ISPs.
However, the metro/edge and core networks are designed by
network operators to provide some lower worst-case minimum
transmission rate to every customer, taking advantage of the
bursty nature of customer Internet traffic. The ratio of the
advertised access rate to this minimum per-user rate is
referred to as the oversubscription rate. Although the over-
subscription rate applied by network providers is typically
much higher for wireless access networks than for wired
access networks, to facilitate a fair comparison we model the
same across all access networks. Note that as the use of the
consumer Internet for streaming real-time services increases,
high oversubscription ratios will become unsustainable. The
results plotted below are based on an oversubscription ratio of
25.
Figure 2 shows that wireless networks such as WiMAX and
third-generation (3G)/UMTS consume significantly more
power per user than fiber-based access for all but the lowest
of access rates. High-speed wireless access is becoming
increasingly popular because it provides mobility and ease of
access to the Internet. However, unless the energy efficiency
of wireless access is improved, its growing popularity may be
unsustainable.
Figure 2 shows that fiber-based access networks are the
most energy-efficient technologies when high rates are
IEEE Network • March/April 2011
9
Figure 2. Power consumption per user for several access network
technologies for a range of access rates. Wireless-based
(WiMAX, 3G/UMTS) access networks are the most power
demanding and fiber based networks the least [9]. Also indicat-
ed is the approximate year corresponding to the given access
rate assuming 40 percent per annum traffic growth.
Access rate (Mb/s)
Year
2010 technology
WiMAX
FTTN
HFC
PtP
PON
DSL
UMTS
10
1
10
0
5
0
Power per user (W)
10
15
20
25
30
2012
2014
2016
2010
10
2
2019
2021
2023
10
3
2026
Figure 3. Power consumption of Internet infrastructure with PON access. The plot includes power consumption of WDM links, core
routers, metro and edge network. Plot a) is based on 2010 technology. Plot b) assumes an annual energy efficiency improvement of 10
percent for equipment in the metro/edge and core networks not including data centers and content distribution networks [2]. Both plots
include the approximate year corresponding to the given access rate assuming 40 percent per annum traffic growth.
Access rate, A
P
(Mb/s)
100
2019
1
10
0
Power per customer (W)
20
30
40
50
10
Total (using PON)
Total (using PON)
Metro and Edge
Metro and Edge
Access (PON)
Access (PON)
Core
Core
WDM
WDM
2010 technology
Year
2012
2010
2014
2016
2021
400
2023
Access rate, A
P
(Mb/s)
100
2019
1
5
0
Power per customer (W)
10
15
20
(a)
(b)
10
2010 technology
Year
2012
2010
2014
2016
2021
400
2023
HINTON LAYOUT 3/2/11 12:01 PM Page 9
IEEE Network • March/April 2011
10
required. Figure 3 is a plot of the key contributors to power
consumption (per customer) of the Internet as a function of
the access rate for fiber-based access. The plot is based on
2010 commercially available technology. Over recent years the
annual increase in access data rate has been about 40 percent
per annum. The calculations used to derive Fig. 3 have includ-
ed factors to account for power supply, cooling, demograph-
ics, service scenarios, capacity planning, and service
management contributions that appear in the above list.
Cloud and content distribution services are discussed below.
Figure 3 shows that for low access rates, Internet power
consumption is dominated by the access equipment (i.e., the
equipment used to connect the home to its local exchange); in
particular, the ONU located in the home. As access rates
increase, the core network power consumption increases and
will ultimately surpass access power consumption. Whereas the
power consumption of a home ONU or gateway is indepen-
dent of its access speed, as access rates increase, the volume of
traffic in the core must increase. This, in turn, requires a sig-
nificant increase in the amount of routing equipment and con-
sequential power consumption to such an extent that the core
routers dominate power consumption at high access rates. The
power consumption growth shown in Fig. 3a assumes 2010
technology in the metro/edge and core networks at all access
rates. This plot shows that without ongoing technology
improvements, the power consumption of the Internet’s infras-
tructure will grow exponentially toward unsustainable levels
due to the demands on the core routers. In reality, there will
be improvements in energy efficiency during the time it takes
for networks to evolve to higher access speeds. Figure 3b
shows the power trends assuming a 10 percent annual improve-
ment in energy efficiency of the metro/edge and core network
equipment. This is a realistic improvement rate for networks in
which the latest generation of equipment is deployed to
accommodate increasing demand [2, 7].
Figure 3 shows that the metro/edge network as well as the
optical communications systems that connect between the
core equipment do not dominate power consumption. The
metro/edge equipment does not have to deal with the volume
of traffic that occurs in the core. The WDM optical communi-
cations systems that connect the routers are relatively energy
efficient in that they can transport substantial capacity at low
power.
Because the core routers will dominate power consumption
at high data rates, we now turn our attention to these routers.
The relative power consumption of subsystems within a core
router is shown in Fig. 4 [12, 13]. A fully loaded core router
consumes approximately 10 nJ/b when it processes IP packets
[2, 13]. The forwarding engine, power supply, and cooling
within the router contribute around 65 percent to its total
power consumption [12].
Improving Energy Efficiency of the Internet
From Fig. 3 it is clear that the two main areas requiring atten-
tion in the context of overall power consumption are the
access networks (in particular the home terminal equipment)
and the core network routers. The challenge of addressing
home terminal equipment has been addressed by the Euro-
pean Union (EU), which has published power consumption
guidelines for this equipment. This voluntary code of conduct
is designed to improve the energy efficiency of all broadband
home equipment sold within the EU [14]. This forms part of
the strategies developed in the EU code of conduct.
Three effective strategies to improving equipment energy
efficiency are:
• Require equipment to reduce its power consumption when
not in use. This low-power state is often referred to as a
“sleep” or “idle” state and can be implemented by shutting
down those parts of the device that are not needed when
the equipment is not communicating. The entire device
cannot be turned off because it will lose contact with the
Internet. A small amount of power must be used to ensure
that the Internet is aware the device is available and is able
to awaken the device when required. Because modern elec-
tronics can operate at very high speeds, even very short
(much less than 1 s) sleep states can be very effective in
reducing power consumption [15].
• Reduce the processing rate of a device when its work load is
low. Many devices can operate over a range of bit rates.
Electronic circuits consume less power when operating at a
lower speed. Thus, when the traffic load on a device is low,
power consumption can be reduced by lowering the speed
at which the device operates. This is often referred to as
rate adaptation [15].
These approaches are being applied to make the Ethernet
protocol more energy efficient and can be applied across
many parts of the Internet. [16]
Improving the energy efficiency of core routers. This will
require either improving the signal processing technology
within the router or changing the function of the router.
Also, strategies for dimensioning the core network to
improve energy efficiency will become increasingly impor-
tant in the future as the core network starts to dominate
power consumption. These strategies have been the subject
of significant research over recent years.
• Deploy the most energy-efficient access network technology
available. The dominance of access network equipment in
today’s network is a clear focus for improving the energy
efficiency of the Internet.
One proposal has been to replace the electronic circuitry
within a router with photonic circuits. This approach is moti-
vated by the expectation that photonic switching technologies
can operate at far higher speeds than electronics. Current
trends indicate that the maximum processing speed attainable
by electronics in the next few years will be about 100 Gb/s,
while photonics holds the promise of attaining speeds over 10
Tb/s. It has been proposed by some researchers that many
electronic routers could be replaced with far fewer photonic
machines, thereby reducing overall power consumption.
Unfortunately, power consumption trends to date for the key
photonic signal processing technologies do not support this
Figure 4. A Pareto analysis breakdown of power consumed by a
core router. The abbreviations are: PS&C: power supply and
cooling. FE: forwarding engine. SF: switching fabric. CP: con-
trol plane. I/O: input/output cards. B: buffers.
PS&C
5
Percent consumption
0
10
15
20
25
30
35
40
FE SF
Router power consumption
CP I/O B
HINTON LAYOUT 3/2/11 12:01 PM Page 10
IEEE Network • March/April 2011
11
scenario. Today complementary metal oxide semiconductor
(CMOS) is about five orders of magnitude less energy con-
suming than the photonic technologies [17]. Furthermore,
while CMOS has shown a trend of continually decreasing
power consumption, the photonic technologies are showing
very little improvement [18]. The net effect of this is that,
whenever intensive signal processing or computation is
required, electronics is the most energy-efficient technology
available.
Another option is to re-architect networks to reduce the
traffic processed in the IP routers. In this approach the net-
work would be redesigned so that a large proportion of Inter-
net traffic bypasses routers in the core network [12, 19, 20].
As Internet traffic travels between its source and destination,
on average it is processed by about 14 routers. These routers
are not directly connected. Rather, they communicate via
optical communication systems that use the synchronous digi-
tal hierarchy/synchronous optical network (SDH/SONET)
protocol. The physical connections are based on WDM in
which many independent optical channels propagate through
fibers deployed around the globe. IP routers can be consid-
ered as sitting at the top level of a multilayer stack of equip-
ment, as depicted in Fig. 5. Processing traffic at the IP level
(Fig. 5a) typically requires about 10 nJ/b. Processing at the
SDH/SONET layer (Fig. 5b) requires around 1–3 nJ/b and in
the WDM layer (Fig. 5c) less than 1 nJ/b [2,19]. Therefore
using SDH/SONET and WDM to bypass the routers reduces
the size and power consumption of the routers in the core of
the network because much of the traffic is processed at the
more energy-efficient SDH/SONET and/or WDM layers [19].
Content Distribution and Data Centers
We now consider content distribution networks with a focus
on video distribution. The provision of video and TV content
via the Internet (e.g., IPTV) is a key driver of Internet growth.
As these services grow the power consumption of the equip-
ment required also increases. Adopting the same network-
model-based approach, the energy required to download
content from a data center can be calculated. A content distri-
bution network can be connected via the Internet or a private
network. (Both options are shown in Fig. 1.) The difference is
that content distribution via the Internet results in the content
traveling through several routers, each adding to the power
consumption of the distribution. Using a private network
avoids the routers, but requires a privately owned (and paid
for) network to connect content directly to the local distribu-
tion point.
IPTV, like most content, is typically stored on hard disks in
a data center which has its own internal network. When the
content is requested it must be retrieved from the
appropriate disk within the data center, and trans-
mitted from the data center to the metro Ethernet
edge switch and down to the TV set-top box (STB)
in the home. To calculate the power consumption
of this process, a file size is chosen (e.g., a 2 hour
standard definition movie is about 1.8 Gbytes) and
the number of request per hour for that movie.
Given this and using typical equipment power con-
sumption figures, the network equipment required
to provide the movie to the STB is determined, and
the power consumption for the download is calcu-
lated. For a given file size, the energy per download
depends on the number of downloads per hour. The
results for a 1.8 Gbyte file (2 hour standard defini-
tion movie) are shown in Fig. 6. In this example the
movie is replicated in 20 data centers spread across
a service area.
Looking at Fig. 6, we see that the power consumed by stor-
age of the content (on hard disks) is constant and independent
of the number of downloads per hour; it is set by the power
consumption of spinning the disks. The power consumed by the
servers that respond to customer requests for content and
extract the content from the disks, and the power required to
transport the content from the data center to the customer
depend on the number of requests for the content. This means
the energy per download being dominated by the storage (disk)
power when there are only a few downloads per hour. If the
content is popular, resulting in many downloads per hour, the
energy per download is dominated by servers and the transport
network because the storage power is shared amongst many
customer requests.
Therefore, popular content should be stored closer to the
user, meaning there will be multiple copies of popular content
geographically spread across the network. This also reduces
the number of routers the content must pass through to reach
the user. For less popular content, fewer copies should be
stored in centralized sites. The most energy-efficient solution
is a compromise between the number/location of the stored
copies and transport to the user’s home. The precise details of
the traces in Fig. 6 and the optimal deployment of the video
copies depend on the popularity of the content.
Figure 5. Traffic flow (grey dashed line) in a node with SDH/SONET and
WDM layers: a) all traffic is passed up through the lower layers and pro-
cessed by the IP router; b) traffic is processed by the SDH/SONET switch,
bypassing the IP router; c) Traffic is switched by the optical cross-connect,
bypassing both the SDH/SONET and IP layers. Lower layers are progres-
sively more energy efficient.
IP
a)
SDH/
SONET
WDM
fiber
link
WDM
fiber
link
OXC
IP
b)
SDH/
SONET
WDM
fiber
link
WDM
fiber
link
OXC
IP
c)
SDH/
SONET
WDM
fiber
link
WDM
fiber
link
OXC
Figure 6. Power consumption per download for a standard definition
2 hour video that has 20 copies replicated in data centers [10].
Downloads per hour, D
Storage
Video servers
Transmission
Total
10
-1
10
-2
10
1
10
0
Power per movie, DE
dl
(W)
10
2
10
3
10
0
10
1
10
2
HINTON LAYOUT 3/2/11 12:01 PM Page 11
IEEE Network • March/April 2011
12
Conclusions
The importance of the Internet and ICT is continually increas-
ing both in terms of economic growth and as a source of
greenhouse gas production. Therefore, to manage the power
consumption of the Internet, it is important to understand
where the energy is consumed in the Internet’s infrastructure.
This article has described one approach to attaining this
understanding and used it to identify those parts of the Inter-
net that dominate its power consumption. This information
can then be used to make the Internet more energy efficient.
As the world becomes more energy constrained, humankind
will need to develop and refine strategies for improving the
energy efficiency of the Internet. A “common rule of thumb”
is the “80/20 rule,” which states that 20 percent of causes pro-
duce 80 percent of effects. In this article the strategies to
address this 20 percent have been identified.
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Biographies
KERRY HINTON (k.hinton@ee.unimelb.edu.au) received an Honors B.E. in 1978,
an Honors B.Sc. in 1980, and an M.Sc. in mathematical sciences in 1982, all
from the University of Adelaide. He was awarded a Ph.D. in theoretical physics
from the University of Newcastle Upon Tyne, United Kingdom, and a DipIR from
Newcastle Upon Tyne Polytechnic in 1984. In 1984 he joined Telstra Research
Laboratories, Victoria, Australia, and worked on analytical and numerical mod-
eling of optical systems and components. His work has focused optical communi-
cations devices, architectures, monitoring, and physical layer issues for intelligent
all-optical networks. He was also a laser safety expert within Telstra. In 2006 he
joined the ARC Special Centre for Ultra-Broadband Information Networks at the
University of Melbourne, Australia, where he is undertaking research into the
energy efficiency of the Internet and optical communications technologies.
J
AYANT BALIGA (jbaliga@ee.unimelb.edu.au) received a B.Sc. degree in computer
science and a B.E. degree in electrical and electronic engineering (with first class
honors) in 2007 from the University of Melbourne, Australia. He is currently
working toward a Ph.D. degree in electrical engineering at the same university.
His research interests include optical network architectures and wireless commu-
nications.
R
OBERT W. A. AYRE (r.arye@ee.unimelb.edu.au) received his B.Sc. degree in
electronic engineering from George Washington University, Washington, DC, in
1967, and B.E. and M.Eng.Sc. degrees from Monash University, Melbourne,
Australia, in 1970 and 1972, respectively. In 1972 he joined the Research Lab-
oratories of Telstra Corporation, working in a number of roles primarily in the
areas of optical transmission for core and access networks, and in broadband
networking. In 2007 he joined the ARC Special Centre for Ultra- Broadband
Networks (CUBIN) at the University of Melbourne, continuing work on network-
ing and high-speed optical technologies.
R
ODNEY S. TUCKER [M’76, SM’81, F’89] (r.tucker@ee.unimelb.edu.au) is a Laure-
ate professor at the University of Melbourne and research director of the ARC
Special Research Centre for Ultra-Broadband Information Networks. He has held
positions at the University of Queensland, the University of California, Berkeley,
Cornell University, Plessey Research, AT&T Bell Laboratories, Hewlett Packard
Laboratories, and Agilent Technologies. He is a Fellow of the Australian Acade-
my of Science and the Australian Academy of Technological Sciences and Engi-
neering. He received his B.E. and Ph.D. degrees from the University of
Melbourne in 1969 and 1975, respectively. He was awarded the Institution of
Engineers Australia Sargent Medal in 1995 for contributions to electrical engi-
neering, and was named IEEE Lasers and Electro-Optics Society Distinguished
Lecturer for 1995–1996. In 1997 he was awarded the Australia Prize for his
contributions to telecommunications, and in 2007 he was awarded the IEEE
Lasers and Electro-Optics Society Aron Kressel Award.
M
ICHAEL Z. FENG (mzfeng@ee.unimelb.edu.au) received his Bachelor of Engineer-
ing degree (with first class honors) in electrical and electronic engineering in
2008 from the University of Auckland, New Zealand. He is currently a Ph.D. stu-
dent at the Centre for Ultra-Broadband Information Networks (CUBIN), University
of Melbourne. His research interests include optical communications technologies
and energy efficiency of optical networks.
HINTON LAYOUT 3/2/11 12:01 PM Page 12
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Article
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