Modeling the Total Energy Consumption of Mobile Network Services and Applications

Abstract

Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.

Full text

energies
Article
Modeling the Total Energy Consumption of Mobile
Network Services and Applications
Ming Yan 1,* , Chien Aun Chan 2, * , AndréF. Gygax 2,3 , Jinyao Yan 1, Leith Campbell 4,
Ampalavanapillai Nirmalathas 4and Christopher Leckie 5
1Faculty of Science and Technology, Communication University of China, Beijing 100024, China;
jyan@cuc.edu.cn
2Networked Society Institute, University of Melbourne, Melbourne, VIC 3010, Australia;
agygax@unimelb.edu.au
3
Department of Finance, Faculty of Business and Economics, University of Melbourne, Melbourne, VIC 3010,
Australia
4Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010,
Australia; leith.campbell@unimelb.edu.au (L.C.); nirmalat@unimelb.edu.au (A.N.)
5School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia;
caleckie@unimelb.edu.au
*
Correspondence: yanm@cuc.edu.cn (M.Y.); chienac@unimelb.edu.au (C.A.C.); Tel.: +86-10-6578-3417 (M.Y.)
Received: 28 November 2018; Accepted: 3 January 2019; Published: 7 January 2019


Abstract:
Reducing the energy consumption of Internet services requires knowledge about the
specific traffic and energy consumption characteristics, as well as the associated end-to-end
topology and the energy consumption of each network segment. Here, we propose a shift from
segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering
with activity-based accounting principles. We use the model to assess a range of the most popular
instant messaging and video play applications to emerging augmented reality and virtual reality
applications. We demonstrate how measurements can be conducted and used in service-specific
end-to-end energy consumption assessments. Since the energy consumption is dependent on user
behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root
causes of service-specific energy consumption. Our main findings show that smartphones are the
main energy consumers for web browsing and instant messaging applications, whereas the LTE
wireless network is the main consumer for heavy data applications such as video play, video chat and
virtual reality applications. By using small cell offloading and mobile edge caching, our results show
that the energy consumption of popular and emerging applications could potentially be reduced by
over 80%.
Keywords:
mobile service; energy consumption; energy model; end-to-end communication networks;
service-specific
1. Introduction
The explosive demand for mobile data has driven a massive deployment of mobile base stations
(BSs), higher bandwidth wireline core networks and larger data centers. As a consequence, the overall
energy consumption of both smartphones and the end-to-end communication network has rapidly
increased. For example, the battery capacity of iPhones has increased from 1880 mAh (iPhone 6) to
2716 mAh for the iPhone X [
1
], which could indicate higher power requirements for smartphone
applications. In addition, the total number of 4G BSs of China Mobile has increased to 1.51 million
in 2016 with an annual network energy consumption of around 20,000 GWh, which equates to a 16%
Energies 2019,12, 184; doi:10.3390/en12010184 www.mdpi.com/journal/energies

Energies 2019,12, 184 2 of 18
increase since 2014 [
2
]. Furthermore, the data centers of Akamai consumed 233,090 MWh electricity in
2016, which is an increase of around 50% since 2012 [3].
The increasing number of smartphone users together with the emergence of data-heavy mobile
applications such as high-definition video play, virtual reality (VR) and augmented reality (AR) are
driving the growth in mobile data traffic. These applications typically require very high network
bandwidth and smartphone computing resources [
4
–
6
]. Moreover, the instant messaging (IM)
applications, such as WeChat, Twitter, etc., attract a huge number of users because they offer a variety
of mobile services including text/picture/voice messages, audio/video chat, moments, etc., and also
consume a lot of network resources. Existing research to reduce the overall energy consumption
of the Internet, including communication networks, cloud data centers and mobile devices, has
focused on segment-specific energy consumption modeling and assessment of the end-to-end delivery
of mobile applications, such as power models for smartphones, BSs, and edge and core networks.
For example, various power models have been developed for estimating the energy consumption
of different smartphone components such as 3G/4G, WiFi, central processing unit (CPU), liquid
crystal display (LCD) and global positioning system (GPS). The researches in [
7
–
10
] show that the
energy consumption of smartphones is influenced by different traffic characteristics and signaling
patterns of mobile applications. Various power models of a long-term evolution (LTE) BS proposed
in [
11
–
15
] try to assess energy consumption of mobile applications by separating their total energy
consumption into data and signaling energy components. On the other hand, the authors in [
16
]
performed a comprehensive review and updated to the radio network energy performance evaluation
method proposed by the European EARTH project [
17
]. The paper provides a new baseline network
power consumption based on population density, which is crucial for energy-efficient 5G deployment.
The topology of a network between an end-user and a cloud data center and the corresponding energy
modeling of such networks is discussed in [
18
–
20
]. Research has shown that a large data center
can consume as much energy as a small city [
21
–
23
]. However, since mobile data traffic consumes
energy in every network segment of the end-to-end link, a better understanding of the service-specific
end-to-end energy consumption is critical for application developers, mobile operators and data center
providers so that they can jointly improve the overall energy efficiency of mobile services.
Furthermore, although existing energy efficiency models are suited for current network
architectures, there is an emerging trend towards using dynamic network architectures for next
generation networks such as 5G based on software defined networking, small cell offloading, network
function virtualization and mobile edge caching and computing [
24
–
27
]. For example, using mobile
edge caching and computing technology, user applications and services could be migrated between
distributed servers located anywhere in the network depending on network conditions, service
usage patterns and users’ mobility in order to improve the performance of service delivery [
28
–
30
].
Alternatively, depending on the service type, certain data intensive applications such as video could
be offloaded from macro cells to small cells in the 5G network to improve user experience and reduce
the load on macro cells [
31
–
33
]. Therefore, segment-specific energy efficiency models are not sufficient
because services could be activated and migrated within the network. Furthermore, by using different
emerging technologies such as small-cell offloading and mobile-edge caching, the energy impacts of
individual segments of the end-to-end network could change significantly, as shown in this paper.
Therefore, we require a service-specific end-to-end energy consumption model in order to understand
and reduce the overall energy consumption and carbon emissions of next generation networks and
services. Our contributions in this paper are as follows:
(1)
We present a comprehensive end-to-end energy consumption model for 15 major mobile services
by taking into account the important factors from each network segment from cloud to core
network, mobile network and end-user devices;
(2) We then demonstrate how key parameters can be measured for service-specific end-to-end energy
assessments and the inter-dependency between key factors in different network segments;

Energies 2019,12, 184 3 of 18
(3)
To address usage pattern heterogeneity of mobile users, we conduct a sensitivity analysis for
key mobile applications and we show that the difference in energy profiles of mobile services
is mainly due to the service type, service data traffic, duration of the service and the type of
end-to-end network topology;
(4)
Finally, we evaluate how two emerging network technologies, small cell offloading and mobile
edge caching, could be used to reduce the overall energy consumption of mobile services.
We begin our investigation by introducing three typical end-to-end network topologies for mobile
services to assess service-specific end-to-end energy consumption. The first step is to assess the
smartphone energy consumption for different mobile services. Here, we have identified a research
gap in understanding the smartphone energy profile for different mobile applications. Therefore,
we conduct experiments in assessing the smartphone energy profiles for 15 mobile services running
on seven popular mobile applications. Our results show that service-specific energy consumption of
a smartphone’s 4G connections is mainly dependent on the 4G signaling duration, while the energy
consumption of a smartphone’s CPU is mainly dependent on the type of service. Video play, map
navigation, video chat and VR are among the top applications that heavily consume the smartphone’s
CPU energy. Our main contribution in this paper is to present the total service-specific end-to-end
energy consumption with the energy breakdown for each network segment. Smartphones are the main
energy consumer for web browsing and IM applications, whereas the LTE wireless network is the main
consumer for heavy data applications such as video play, video chat and VR applications. Our results
show that by using small cell offloading and mobile edge caching, the overall energy consumption of
applications such as video play and virtual reality could potentially be reduced by over 80%.
2. Related Work
To uncover the impact of mobile and internet services on network energy consumption, the energy
consumption models based on mobile data traffic have attracted the attention of a large number of
researchers. Most existing researches focus on the single segment of the entire end-to-end network,
e.g., end-user devices such as smartphone, wireless access networks, wireline core networks and data
centers. In this section, we analyze in more detail the related work in modeling the energy consumption
of different segments of the end-to-end network.
Mobile services and applications consume smartphone energy during their executing processes.
The energy consumption of an application is highly dependent on the degree of interactions among
different smartphone components during application execution process. Smartphone components
such as 3G/4G, WiFi, CPU, LCD, Bluetooth, and GPS are the most energy consuming components [
7
].
In [
8
], the authors proposed an experimental framework for measuring the Android smartphone
power consumption of web pages, including specific components on the page, such as cascade style
sheets (CSS), Javascript, images, and plug-in objects. The hardware experimental environment was
built to measure the energy consumed by setting up the 3G connection with the base station and
the energy consumed by transmitting various payload sizes. Results demonstrated that the energy
consumption of 3G upload is usually greater than 3G download with same data size due to power
consumption of transmitter to transmit packets to the base station. In [
9
], a simple model for the
radio resource control (RRC) state of a smartphone was developed to reveal the impact of traffic
characteristics on the power consumption of the smartphone. Results showed that periodic patterns
of RRC connection may cause increased power consumption and signaling overload. To optimize
the smartphone energy consumption caused by frequent RRC connection, the authors in [
10
] have
modelled the performance-aware hybrid energy optimization problem of mobile devices in mobile
cellular networks.
In our previous work [
11
,
12
], the energy assessment models of wireless access network such as 4G
LTE BS have been investigated. Based on real network and service measurements, the proposed energy
model allocated a proportion of the base station power consumption to each mobile service based on
the data traffic and signaling traffic generated by the service. The main difference between signaling

Energies 2019,12, 184 4 of 18
duration and data duration is that a radio resource control (RRC) connection needs to be established
to allocate mobile radio resources before any data transmission can begin [
11
,
12
]. Upon completion
of data transmission, the RRC connection needs to be released. The entire time from establishing to
releasing the RRC connection is defined as the signaling duration and often is longer than the data
duration for small data IM applications [
11
,
12
]. Using WeChat as an over-the-top (OTT) IM application
example, results showed that WeChat consumes more BS energy than conventional mobile services
due to the network signaling energy overhead. In [
13
], the authors developed power models for
different type base stations (e.g., macro and micro) and divided the total power consumption into two
parts, the static power consumption which is consumed in an empty base station and the dynamic
power consumption depending on the traffic load situation. Besides the data traffic, excessive signaling
overhead which is generated by newer applications such as Facebook and Twitter also consumed
a large amount of energy consumption of LTE networks [
14
]. In [
15
], the authors found that using
microcell BSs in LTE network can dramatically reduce the power consumption of the network. Results
showed that up to a 31% cost reduction could be obtained when using microcell BSs, without the
deterioration of the quality of service.
In [
18
], the authors developed a power consumption model for interactive cloud applications
that reveals the influence of the applications on the power consumption of the network elements in
a wireline network between the end-users and the cloud data center. For network equipment such
as Ethernet switch, broadband network gateways, edge routers, and core routers in the wireline core
network, power consumption was considered as a function of the traffic load, i.e., bits per second
of data traffic, and was modelled using energy per bit as the metric. In [
19
], the authors introduced
an energy model to compute the total energy consumption of content delivery networks which is
based on a hierarchical network architecture. The authors in [
20
] also developed a power model that
permits quantifying the energy efficiency of core network equipment at the granularity of per-packet
processing, and per-byte store and forward packet handling operations.
The increasing demand for storage and computation has driven the deployment and update of
large data centers. Research has shown that a large data center can consume as much energy as a small
city [
21
]. To save energy consumption of data centers and reduce the emission of greenhouse gases,
the authors in [
21
] proposed energy efficiency and low carbon enabler green IT framework for these
large and complex data centers, and the authors in [
22
] outlined the major challenges in performing
real-time energy efficient management of the distributed resources available at both mobile devices
and the remote data centers. Moreover, in [
23
], the authors developed an analytic framework for
modeling total power consumption of a data center and presented parametric power models of data
center components.
Despite major energy efficiency research efforts have been dedicated to individual network
segment, to the best of the authors’ knowledge, the end-to-end energy consumption model of mobile
services including all network components from the end-user devices to the wireless access network,
wireline core network and data center, has never been investigated in existing researches. In this
paper, we try to develop a service-specific end-to-end energy consumption model based on the traffic
characteristics and usage patterns of deferent mobile services. This end-to-end model can help reveal
the energy impacts of individual segments of the end-to-end network.
3. Service-Specific End-To-End Energy Consumption Model
In Figure 1, we categorize major mobile services into three typical end-to-end network topologies.
Topology (a), user-to-data center (U2DC), shows the end-to-end communication link between a
smartphone and a data center via the 4G LTE access network and the wireline core network [
18
] for
applications such as web browsing, file download/upload, map navigation, video on demand, VR
and AR. Here, we analyze the metro, edge and core networks collectively as the wireline core network
for comparison with other network segments. Topology (b), user-to-user via data center (U2UvDC),
shows the typical end-to-end communication link for some of the currently most-downloaded social

Energies 2019,12, 184 5 of 18
networking applications for services such as text, picture and voice messaging. Here, messages are sent
from the sender’s smartphone and stored at the data center before being forwarded to the recipient’s
smartphone. Topology (c), user-to-user direct (U2U), shows the end-to-end communication link for
mobile applications that adopt the peer-to-peer protocol to provide voice, video chats and voice
conference calls, which require the establishment of connections between the users before transferring
data in real time.
Energies 2019, 12, x FOR PEER REVIEW 5 of 18
voice, video chats and voice conference calls, which require the establishment of connections between
the users before transferring data in real time.
Figure 1. Three service and application-specific end-to-end topologies: a) communications between
the user and the data center via 4G LTE access and wireline core networks; b) communications
between two users via the data center; c) direct communications between two users via the 4G LTE
access and the wireline core networks.
Figure 2 demonstrates the modeling of service-specific end-to-end energy consumption. The
end-to-end service energy consumption model includes four sub-models: (1) smartphone, (2) BS, (3)
wireline core network and (4) data center. Each of the four sub-models will be described in detail in
the following subsections, and the models will be integrated into an end-to-end service energy
consumption model.
4G LTE
Access
Network
Data
Center
Metro &
Edge
Network
Core
Network
Smartphone
(a) Topology user-to-data center (U2DC)
4G LTE
Access
Network
Data
Center
Metro &
Edge
Network
Core
Network
Smartphone
Core
Network
Metro &
Edge
Network
4G LTE
Access
Network
Smartphone
(b) Topology user-to-user via data center (U2UvDC)
4G LTE
Access
Network
Metro &
Edge
Network
Core
Network
Smartphone
Metro &
Edge
Network
4G LTE
Access
Network
Smartphone
(c) Topology user-to-user direct (U2U)
Wireline core network
Figure 1.
Three service and application-specific end-to-end topologies: (
a
) communications between
the user and the data center via 4G LTE access and wireline core networks; (
b
) communications between
two users via the data center; (
c
) direct communications between two users via the 4G LTE access and
the wireline core networks.
Figure 2demonstrates the modeling of service-specific end-to-end energy consumption.
The end-to-end service energy consumption model includes four sub-models: (1) smartphone, (2) BS,
(3) wireline core network and (4) data center. Each of the four sub-models will be described in detail
in the following subsections, and the models will be integrated into an end-to-end service energy
consumption model.
3.1. Smartphone Energy Consumption Model
This paper mainly investigates the end-to-end energy consumption of mobile services in 4G
environment, so we only consider the energy consumption of 4G modules and ignore the WiFi
module. In addition, in order to simplify our model, this paper does not consider the smartphone
energy consumption caused by modules such as LCD, GPS, etc. In our energy consumption model,
real measurements of the smartphone are needed to determine the energy consumption in terms
of the 4G connection and CPU, because different mobile services usually have different data and
signaling traffic requirements as well as diverse durations of service. Six major parameters need to
be measured: data traffic
TS_data
(bps), signaling traffic
TS_sig
(bps), data duration
Ddata
(s), signaling
duration
Dsig
(s), CPU power consumption
PS_CPU
(W) and 4G connection power consumption
PS_4G
(W). However, limited existing research has focused on evaluating these parameters for different
mobile applications, which is crucial for the construction of the end-to-end service energy models.
A smartphone’s service-specific energy consumption,
Esmartphone
(Joule), is the sum of the integrals of
the CPU and 4G power consumption over the duration of use, as indicated by 1
(in Figure 2).

Energies 2019,12, 184 6 of 18
Esmartphone =ZDsi g
0PS_CPU (t)dt +ZDs ig
0PS_4G(t)dt, (1)
Measuring the power consumption of smartphones requires the use of external power
measurement tools or a self-metering power measurement methodology with different
granularities [7,8]. The measurement procedures are discussed in the next section.
Energies 2019, 12, x FOR PEER REVIEW 6 of 18
Figure 2. Service-specific end-to-end energy assessment methodology.
3.1. Smartphone Energy Consumption Model
This paper mainly investigates the end-to-end energy consumption of mobile services in 4G
environment, so we only consider the energy consumption of 4G modules and ignore the WiFi
module. In addition, in order to simplify our model, this paper does not consider the smartphone
energy consumption caused by modules such as LCD, GPS, etc. In our energy consumption model,
real measurements of the smartphone are needed to determine the energy consumption in terms of
the 4G connection and CPU, because different mobile services usually have different data and
signaling traffic requirements as well as diverse durations of service. Six major parameters need to
be measured: data traffic  (bps), signaling traffic  (bps), data duration  (s),
signaling duration  (s), CPU power consumption  (W) and 4G connection power
consumption  (W). However, limited existing research has focused on evaluating these
parameters for different mobile applications, which is crucial for the construction of the end-to-end
service energy models. A smartphone’s service-specific energy consumption,  (Joule), is
the sum of the integrals of the CPU and 4G power consumption over the duration of use, as indicated
by ① (in Figure 2).
 

 

,
(1)
Measuring the power consumption of smartphones requires the use of external power
measurement tools or a self-metering power measurement methodology with different granularities
[7,8]. The measurement procedures are discussed in the next section.
3.2. BS Energy Consumption Model
In our previous works [11,12], the BS power consumption is measured as a function of resource
load (i.e., physical resource block (PRB) utilization). The measured data (bps) and signaling traffic
(resource elements) of a mobile service need to be converted into PRBs to determine the service power
consumption in terms of data and signaling. Then the BS’s service-specific energy consumption, 
(Joule), can be determined by summing the integrals of the data and signaling power consumption
over the duration of use, as indicated by ② (in Figure 2):
Measure power
consumption of 4G
connection, 
Determine smartphone energy consumption of
mobile service
( 



)
Base station power
consumption as a
function of resource
load[11,12]
Determine base station energy consumption of
mobile service
( 



)
Determine the power
consumption of data
traffic ()[11,12]
Determine the power
consumption of
signaling traffic
() [11, 12]
Wireline network equipment
power consumption as a
function of bits-per-second
(Joules/bit) [16]
Data center power
consumption as a
function of bits-per-
second (Joules/bit) [3]
Measure data
duration (D)
Measure signaling
duration ()
Measure power
consumption of
CPU, 
Smartphone measurements for mobile service
Measure data
traffic ()
Measure signaling
traffic ()
Determine wireline energy
consumption of mobile service
()
Determine data center energy
consumption of mobile service
()
End-to-end energy consumption of
mobile service
        
1
2
3
4
5
Figure 2. Service-specific end-to-end energy assessment methodology.
3.2. BS Energy Consumption Model
In our previous works [
11
,
12
], the BS power consumption is measured as a function of resource
load (i.e., physical resource block (PRB) utilization). The measured data (bps) and signaling traffic
(resource elements) of a mobile service need to be converted into PRBs to determine the service power
consumption in terms of data and signaling. Then the BS’s service-specific energy consumption,
EBS
(Joule), can be determined by summing the integrals of the data and signaling power consumption
over the duration of use, as indicated by 2
(in Figure 2):
EBS =ZDdat a
0PBS_data (t)dt +ZDsi g
0PBS_sig(t)dt, (2)
where
PBS_data
and
PBS_sig
are the BS’s power consumption of data traffic and signaling traffic generated
by mobile service, respectively:
PBS_data (t)=(ηd,RRU +ηd,BBU)Td(t), (3)
PBS_sig(t)=(ηs,RRU +ηs,BBU )Rs(t), (4)
where
ηd,RRU
and
ηd,BBU
are the power (in watts) consumed by one bit per second of data rate in the
radio remote unit (RRU) and baseband unit (BBU) respectively at time
t
,
Td(t)
is the data rate (in
bits per second) of the mobile service,
ηs,RRU
and
ηs,BBU
are the energy (in Joules) consumed by one

Energies 2019,12, 184 7 of 18
resource element (RE) in component RRU and BBU respectively, and
Rs(t)
is the average signaling
REs per second consumed by the mobile service.
Importantly, the BS’s power consumption differs for diverse types of BSs (e.g., macro, micro, pico
and femto). In addition, different cell sites have different average throughput-to-PRB utilization ratios
even for the same type of BS, so the BS energy consumption to deliver the same mobile service will
vary between different BSs [
11
]. Finally, the service-specific signaling traffic is measured in terms of
the number of RE in 4G LTE and its value varies over a small range, i.e., between 120 to 190 RE/s with
an average of 144 RE/s [11], later used in the energy consumption assessments.
3.3. Wireline Core Network Energy Consumption Model
Energy per bit (Joules/bit) is a common metric used in determining the energy efficiency of
the wireline core network [
18
]. Usually, the metro and edge networks consist of Ethernet switches,
broadband network gateways and edge routers. Edge routers are the gateway to the core network,
which consists of many large core routers. The energy per bit of these routers and switches can be
calculated from their maximum capacity, maximum power and idle power [
18
]. For example, a CRS-3
core router has a maximum capacity of 4480 Gbps with a maximum power of 12.3 kW and an idle
power of 11.07 kW; this yields an energy per bit of ~8.5 nJ/bit. In addition, the average number
of network hops (i.e., routers and switches) along the path from the smartphone to the data center
has been investigated in [
18
]. Then
3

calculates the wireline core network service-specific energy
consumption,
Ewireline
, of a mobile service as the product of the measured data traffic generated by a
mobile service and the energy per bit of the wireline core network:
Ewireline =NcEc+NeEe+Ebng +Esw ×ZDdat a
0TS_data dt, (5)
where
Nc
and
Ne
are the number of core and edge routers in the wireline core network,
Ec
,
Ee
,
Ebng
,
and
Esw
denote respectively the energy per bit of the core router, edge router, broadband network
gateway (BNG), and Ethernet switches.
3.4. Data Center Energy Consumption Model
Usually, a data center comprises of many thousands of servers and can consume as much energy
as a small city. The energy consumption of a data center includes not only the electricity consumed by
these servers, but also the energy consumed by cooling facilities or dissipated in conversions within
the uninterruptible power supplies (UPS) and power distribution unit (PDU) systems [
21
]. Therefore,
it is difficult to directly assess the energy consumption of a data center caused by a mobile application.
To assess the impact of data traffic on the energy consumption of a data center, Akamai Technologies
Inc., a major cloud platform provider, adopts the intensity metric of electricity (kWh) per unit of
network traffic (Gbps) to assess the energy performance of its data centers [
3
]. This energy metric of a
data center, MDC, can be calculated using the following equation:
MDC =EDC_annual /Tannual, (6)
In the above equation:
•EDC_annual
(kWh) is the annual electricity consumption of a data center. For example, the annual
electricity consumption of Akamai’s data centers was about 233 ×106kWh in 2016 [3].
•Tannual
(bit) is the annual data throughput of a data center, and can be calculated with the average
daily web traffic of a data center which exceeded 30 Terabits per second for Akamai [3].
By converting the annual energy consumption of Akamai to power consumption and then
dividing by the maximum network traffic, the energy per bit (Joules/bit) of Akamai’s data centers can
be calculated. As in the calculation for the wireline core network energy consumption, i.e., multiplying

Energies 2019,12, 184 8 of 18
the measured data traffic generated by a mobile service with the energy per bit of the data center,
we can determine the data center energy consumption of a mobile service,
EDC
(Joule), as indicated by
4
(in Figure 2):
EDC =3.6 ×106×MDC ×ρ×ZDda ta
0TS_data dt, (7)
where
ρ
is the ratio of average Internet traffic to busy-hour Internet traffic, and can be assumed around
0.3 according to Cisco [34].
Finally, the total service-specific end-to-end energy consumption,
Etotal
(Joule), is the sum of the
energy consumption of each segment used in the end-to-end communication link:
Etotal =Esmartphone +EBS +Ewireline +EDC, (8)
Note that, for different service types, the end-to-end network components may be different, as
shown in Figure 1. Hence, the total service-specific end-to-end energy consumption will sum together
the energy consumption of different network segments depending on the end-to-end topology.
4. Service-Specific Smartphone Energy and Traffic Measurements
We demonstrate the process to acquire the measurements for the six major parameters of mobile
services shown in Figure 2: data traffic
TS_data
(bps), signaling traffic
TS_sig
(bps), data duration
Ddata
(s), signaling duration
Dsig
(s), CPU power consumption
PS_CPU
(W) and 4G connection
power consumption
PS_4G
(W). These parameters are crucial in developing the end-to-end energy
consumption model since service energy consumption of smartphones represents a major portion,
yet existing research [
7
–
15
] has limited insights into this aspect. Therefore, we develop test cases
for 15 different mobile services from seven mobile applications, as shown in Table 1. These mobile
applications are grouped into (i) conventional applications (web browsing, map navigation, file upload
and download); (ii) emerging applications (AR and VR, which generate a large amount of data traffic
and require more computational resources from the smartphones); and (iii) popular applications (video
play and IM). The video data traffic generated by video-on-demand applications, such as Youku, iQiyi
and YouTube, currently dominate total mobile Internet traffic. On the other hand, IM applications
such as Wechat, QQ and WhatsApp are among the most downloaded applications in the world. These
IM applications provide various communication services such as text, voice, and picture messaging,
as well as video and audio chats.
Packet Capture (an Android-based application that can capture communication packets of mobile
services) is used to collect the uplink and downlink data traffic when testing a service. The test
information for each mobile service is shown in Table 1. A smartphone’s energy consumption and the
signaling duration are measured using PowerTutor, an Android-based application that displays the
power consumption of major smartphone system components, such as the CPU, network interface,
display, and GPS receiver, for different applications running simultaneously on the smartphone.
However, PowerTutor has not been updated since October 2011 so we use the built-in power and 4G
traffic management function of the Android system for cross validation. In our experiments, we used
Huawei KIW-CL00 (Smartphone A in Figures 3and 4) and Samsung SM-A7100 (Smartphone B in
Figures 3and 4) Android smartphones.

Energies 2019,12, 184 9 of 18
Table 1. Test results for the 15 mobile services under investigation.
No. Services Test Information Average 4G UL
Traffic (kBytes)
Average 4G DL
Traffic (kBytes)
Average 4G
Energy
Consumption (J)
Average CPU
Energy
Consumption (J)
Average
Signaling
Duration (s)
Network
Topology
1 Web browsing Built-in browser, open
m.sohu.com 358.4 593.92 37.02 9.4 70
U2DC
2 Navigation Baidu Map, Place search
and route planning 184.32 440.32 34.2 15.75 55
3 Cloud upload Baidu Cloud, upload a
picture (size = 2.5 MB) 2580.48 92.16 28.72 2.46 41
4Cloud
download
Baidu Cloud, download a
picture (size = 2.5 MB) 102.4 2641.92 32.86 1.98 50
5 Video play
Youku, open a video
online (length = 60 s,
size = 5 MB)
194.56 5089.28 51.4 13.8 91
6 AR
Let’s go, display the store
info in the screen 20.48 163.84 25.93 7.88 43
7 VR video
Orange VR, open a video
(length = 65 s,
size = 58 MB)
389.12 61440 61.5 47.1 113
8Send text
message
Wechat, type & send a
16-Chinese character
text message
0.57 0.51 10.63 0.56 35
U2UvDC
9Receive text
message
Wechat, receive a
16-Chinese character
text message
0.41 0.47 7.09 0.27 15
10 Send voice
message
Wechat, send a voice
message (length = 10 s) 18.37 2.63 13.89 1.08 27
11 Receive voice
message
Wechat, receive a voice
message (length = 10 s) 3 26 11.37 0.52 23
12 Send picture
message
Wechat, send a picture
(size = 2.67 MB) 2810 132 13.24 6.82 26
13 Receive picture
message
Wechat, receive a picture
(size = 2.67 MB) 43 2793 9.67 1.21 21
14 Audio chat Wechat, audio chat 60 s 358.4 307.2 44 6.6 80 U2U
15 Video chat Wechat, video chat 60 s 4321.28 5514.24 52.075 18.825 97

Energies 2019,12, 184 10 of 18
Energies 2019, 12, x FOR PEER REVIEW 9 of 18
Figure 3. Smartphone’s 4G power consumption and signaling duration for 15 different types of
mobile services. Note that the services are sorted by 4G signaling duration and average measurements
for 2 types of smartphones are plotted to demonstrate the consistency of the measurements.
Table 1. Test results for the 15 mobile services under investigation.
No.
Services
Test
Information
Average
4G UL
Traffic
(kBytes)
Average
4G DL
Traffic
(kBytes)
Average 4G
Energy
Consumption
(J)
Average CPU
Energy
Consumption
(J)
Average
Signaling
Duration
(s)
Network
Topology
1
Web
browsing
Built-in
browser, open
m.sohu.com
358.4
593.92
37.02
9.4
70
U2DC
2
Navigation
Baidu Map,
Place search
and route
planning
184.32
440.32
34.2
15.75
55
3
Cloud
upload
Baidu Cloud,
upload a
picture (size =
2.5 MB)
2580.48
92.16
28.72
2.46
41
4
Cloud
download
Baidu Cloud,
download a
picture (size =
2.5 MB)
102.4
2641.92
32.86
1.98
50
5
Video play
Youku, open a
video online
(length = 60 s,
size = 5 MB)
194.56
5089.28
51.4
13.8
91
6
AR
Let’s go,
display the
store info in the
screen
20.48
163.84
25.93
7.88
43
7
VR video
Orange VR,
open a video
(length = 65 s,
size = 58 MB)
389.12
61440
61.5
47.1
113
8
Send text
message
Wechat, type &
send a 16-
Chinese
character text
message
0.57
0.51
10.63
0.56
35
U2UvDC
Average smartphone 4G
power consumption (W)
Average 4G signalling duration (s)
0
20
40
60
80
100
120
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
12345678910 11 12 13 14 15
Smartphone A Smartphone B
Figure 3.
Smartphone’s 4G power consumption and signaling duration for 15 different types of mobile
services. Note that the services are sorted by 4G signaling duration and average measurements for 2
types of smartphones are plotted to demonstrate the consistency of the measurements.
Energies 2019, 12, x FOR PEER REVIEW 11 of 18
Figure 4. Smartphone’s CPU power and energy consumption for 15 different types of mobile services.
Note that the services are sorted by CPU energy consumption and average measurements for two
types of smartphones are plotted to demonstrate the consistency of the measurements.
Figure 4 shows the average smartphone CPU power and energy consumption for all services.
Again, measurements for smartphones A and B are plotted to indicate the robustness of the
measurements. We observed only small variations in the measurements. The dashed line represents
the exponential trend of the average smartphone’s CPU service-specific power consumption for
smartphone A. Combining the observations from Figure 4 and the measurements in Table 1, we
found that the CPU energy consumption of smartphones does not correlate strongly with the data
traffic of mobile services. However, the CPU power consumption of the tested smartphones is
strongly dependent on the type of application running on them. For example, as shown in Figure 4,
IM-type services generally require very little CPU power consumption, while navigation, video chat
and VR require relatively high CPU power consumption compared to other mobile services.
5. Total Service-Specific End-to-End Energy Consumption
Table 1 presents our key contribution in assessing the total service-specific end-to-end energy
consumption by using the methodology shown in Figure 2. It should be noted that different BS cell
sites have different power parameters that could affect the BS energy consumption of mobile services.
Here, we assume that a macro BS cell site is used in the service energy consumption assessment [12].
Figure 5a shows the baseline end-to-end energy consumption for 7 typical mobile services grouped
into 3 different network topologies with different user behaviors. In the baseline, the BS cell site is a
macro cell, while the smartphone energy consumption is based on Smartphone A’s measurements.
Furthermore, different users are expected to have heterogenous service usage profiles. Our
measurements so far have only considered the typical usage scenarios for evaluating service-specific
energy consumption profiles. Here, we expanded the measurements to cover a wide range of usage
scenarios for selected services and applications via sensitivity analysis:
 Web browsing: we accessed the website sina.cn with time ranges from 60 s to 500 s to keep
refreshing page content.
 Youku video: we viewed different videos with sizes ranging from 7.9 MBytes to 26 MBytes.
 VR: we accessed different videos with sizes ranging from 50 MBytes to 177 MBytes.
Average smartphone CPU
power consumption (W)
Average smartphone CPU energy consumption (J)
0
10
20
30
40
50
60
0.0
0.1
0.2
0.3
0.4
0.5
012345678910 11 12 13 14 15 16
Smartphone A
Smartphone B
×
Figure 4.
Smartphone’s CPU power and energy consumption for 15 different types of mobile services.
Note that the services are sorted by CPU energy consumption and average measurements for two types
of smartphones are plotted to demonstrate the consistency of the measurements.
The same testing procedure for each mobile service was repeated five times at different times of the
day and on different days. We found small variations in terms of the service data and signaling traffic,
the power consumption of the 4G connection and CPU, and the 4G signaling duration. Comparing the
measurements between two smartphones, we also found small variations in terms of the measurements
of power consumption, as well as data and signaling traffic. The values plotted in Figures 3and 4are
the average values recorded from five separate measurements.

Energies 2019,12, 184 11 of 18
Next, our key findings follow. Figure 3shows the average power consumption of the 4G
connection and the average 4G signaling duration for different mobile services. The dashed line
represents the average power consumption of 4G in Smartphone A, while the red dots represent
the signaling durations for all 15 mobile services. The bar chart in Figure 3shows that the power
consumption of 4G connections does not vary much for different types of services although the
signaling duration for different types of services can vary. Moreover, as shown in Table 1, the average
4G energy consumption and average signaling duration vary proportionally. These findings suggest
that the 4G energy consumption of smartphones depends mainly on the signaling duration of the
mobile service and is close to independent of the data traffic. Furthermore, the two smartphones’
measurement variations were small.
Figure 4shows the average smartphone CPU power and energy consumption for all services.
Again, measurements for smartphones A and B are plotted to indicate the robustness of the
measurements. We observed only small variations in the measurements. The dashed line represents
the exponential trend of the average smartphone’s CPU service-specific power consumption for
smartphone A. Combining the observations from Figure 4and the measurements in Table 1, we found
that the CPU energy consumption of smartphones does not correlate strongly with the data traffic
of mobile services. However, the CPU power consumption of the tested smartphones is strongly
dependent on the type of application running on them. For example, as shown in Figure 4, IM-type
services generally require very little CPU power consumption, while navigation, video chat and VR
require relatively high CPU power consumption compared to other mobile services.
5. Total Service-Specific End-To-End Energy Consumption
Table 1presents our key contribution in assessing the total service-specific end-to-end energy
consumption by using the methodology shown in Figure 2. It should be noted that different BS cell
sites have different power parameters that could affect the BS energy consumption of mobile services.
Here, we assume that a macro BS cell site is used in the service energy consumption assessment [
12
].
Figure 5a shows the baseline end-to-end energy consumption for 7 typical mobile services grouped
into 3 different network topologies with different user behaviors. In the baseline, the BS cell site is a
macro cell, while the smartphone energy consumption is based on Smartphone A’s measurements.
Furthermore, different users are expected to have heterogenous service usage profiles. Our
measurements so far have only considered the typical usage scenarios for evaluating service-specific
energy consumption profiles. Here, we expanded the measurements to cover a wide range of usage
scenarios for selected services and applications via sensitivity analysis:
•
Web browsing: we accessed the website sina.cn with time ranges from 60 s to 500 s to keep
refreshing page content.
•Youku video: we viewed different videos with sizes ranging from 7.9 MBytes to 26 MBytes.
•VR: we accessed different videos with sizes ranging from 50 MBytes to 177 MBytes.
•IM voice message: we sent and received voice messages ranging from 10 s to 60 s.
•Audio chat: we tested voice calls from 60 s to 1200 s.
•Video chat: we tested video chats from 60 s to 1200 s.

Energies 2019,12, 184 12 of 18
Energies 2019, 12, x FOR PEER REVIEW 12 of 18
 IM voice message: we sent and received voice messages ranging from 10 s to 60 s.
 Audio chat: we tested voice calls from 60 s to 1200 s.
 Video chat: we tested video chats from 60 s to 1200 s.
The service-specific end-to-end energy consumption ranges (minimum to the maximum) are
shown in Figure 5a. For example, the end-to-end energy consumption for video chats ranges from
366 Joules to 4580 Joules when the connection duration increases from 1 min to 20 min, with an
average value around 1620 Joules.
Figure 5. Baseline end-to-end energy consumption of typical mobile services. Note that a macro BS is
used in the baseline energy consumption assessments. a) End-to-end energy consumption under
different user behaviors; b) average energy breakdown of baseline. The error bars show the minimum
and maximum usages of the services.
(1) Topology U2DC: Under this topology, covering web browsing, video play and VR, data are
transferred between the smartphone and the cloud server. Therefore, the end-to-end energy
consumption of such a service consists of the energy consumed by the smartphone (i.e., 4G and
CPU), the 4G LTE access network, the wireline core network, and the data center. Figure 5a
shows that the average end-to-end energy consumption of Topology U2DC services could vary
significantly from 470.1 Joules (web browsing) to 2470.5 Joules (VR) in the baseline. From the
average energy consumption breakdown shown in Figure 5b, we observe that for high energy
consumption services (i.e., VR) the LTE wireless network tends to dominate, while for services
with low energy consumption services (i.e., web browsing) the smartphone dominates.
(2) Topology U2UvDC: This topology includes IM voice and picture messaging services. Their
commonality is that data is transmitted to the cloud server before being forwarded to the
intended recipient’s smartphone. Therefore, the end-to-end energy consumption consists of (1)
the energy consumed by sending the message from the sender to the cloud server and (2) the
transmission of the message from the cloud server to the receiver. Figure 5a shows that voice
messages are typically short, resulting in a relatively low service-specific end-to-end energy
consumption compared to picture messages. For IM picture messages, because the size of an
470.1 522.1
2470.5
42.4 121.7
605.0
1620.6
0
1000
2000
3000
4000
5000
Web Browsing Video Play VR IM Voice msg. IM picture
msg.
Audio chat 2
users
Video chat
Energy consumption (J)
End to end energy consumption (baseline)
Topology U2DC Topology U2UvDC
Web browsing Video Play VR IM voice msg.
(30s)
IM picture msg. Audio chat 2
users
Video chat
Smartphone LTE wireless Wireline core network Data center
Topology U2U
(a)
(b)
68.8%
25.6%
1.1% 4.4%
4.3%
82.1%
2.7%
10.8%
94.3%
5.2%
0.2%
0.4%
21.3%
68.5%
3.4%6.8%
82.3%
16.9%
0.8%
26.8%
69.9%
3.3%
30.9%
59.2%
2.0% 7.9%
Figure 5.
Baseline end-to-end energy consumption of typical mobile services. Note that a macro BS
is used in the baseline energy consumption assessments. (
a
) End-to-end energy consumption under
different user behaviors; (
b
) average energy breakdown of baseline. The error bars show the minimum
and maximum usages of the services.
The service-specific end-to-end energy consumption ranges (minimum to the maximum) are
shown in Figure 5a. For example, the end-to-end energy consumption for video chats ranges from 366
Joules to 4580 Joules when the connection duration increases from 1 min to 20 min, with an average
value around 1620 Joules.
(1)
Topology U2DC: Under this topology, covering web browsing, video play and VR, data are
transferred between the smartphone and the cloud server. Therefore, the end-to-end energy
consumption of such a service consists of the energy consumed by the smartphone (i.e., 4G and
CPU), the 4G LTE access network, the wireline core network, and the data center. Figure 5a
shows that the average end-to-end energy consumption of Topology U2DC services could vary
significantly from 470.1 Joules (web browsing) to 2470.5 Joules (VR) in the baseline. From the
average energy consumption breakdown shown in Figure 5b, we observe that for high energy
consumption services (i.e., VR) the LTE wireless network tends to dominate, while for services
with low energy consumption services (i.e., web browsing) the smartphone dominates.
(2)
Topology U2UvDC: This topology includes IM voice and picture messaging services. Their
commonality is that data is transmitted to the cloud server before being forwarded to the intended
recipient’s smartphone. Therefore, the end-to-end energy consumption consists of (1) the energy
consumed by sending the message from the sender to the cloud server and (2) the transmission
of the message from the cloud server to the receiver. Figure 5a shows that voice messages are
typically short, resulting in a relatively low service-specific end-to-end energy consumption
compared to picture messages. For IM picture messages, because the size of an image can be
large (ranging from 0.2 MB to 8 MB for the images used in our tests), the energy consumed in the
4G LTE access network can be higher than in the other network segments. In addition, sending

Energies 2019,12, 184 13 of 18
and receiving small messages triggers frequent network reconnections, which generate a large
amount of signaling traffic [
11
]. Therefore, the 4G connection requires more than 90% of the
smartphone’s energy use for IM text and voice messages.
(3)
Topology U2U: This topology includes voice, audio chat and video chat services. Their
commonality is that the users are connected directly by the P2P-like protocol so that the data is
transferred directly between smartphones. The end-to-end service energy consumption increases
as the usage increases. The total service-specific end-to-end energy consumption consists of the
energy consumed by smartphones and the corresponding LTE wireless network and the wireline
core network. As depicted in Figure 5, the average end-to-end energy consumption of video
chat is around 1620 Joules, with the LTE network accounting for more than 69% of the total
service energy consumption, followed by the energy consumed by the smartphones (26.8%). For
audio chat services, the average end-to-end energy consumption is around 605 Joules with the
smartphone accounting for more than 82% of the total service energy consumption. This is due
to the audio chat generating less data traffic than video chat with the same connection duration.
6. Opportunities for Reducing the Energy Consumption of Mobile Services
In this section, we analyze the root cause of the energy consumption in each segment and then
suggest strategies to reduce the total network energy consumption of mobile applications.
6.1. Offloading Heavy Mobile Services from Macro Cells to Smaller Cells
To establish the proportions of energy consumptions for different segments of the network,
we calculated the energy consumption proportion of each network segment using the four sub-models
introduced in Section 3and the measurements in Table 1. The calculation results are shown in Figure 5b.
Figure 5b shows that the smartphone and LTE wireless network are responsible for the majority of
energy consumed by all typical mobile services. The high energy consumption of a smartphone
is mainly due to the power consumption of the 4G module in the device during the active stage
for the entire network connection from RRC connection establishment to data transmission and
RRC connection release. Furthermore, the transmission power of a smartphone is related to the
distance between itself and its serving BS. When a smartphone is close to the serving BS, it can reduce
its transmission power to decrease energy consumption. Related researches show that the energy
consumption of a 4G smartphone module can be potentially reduced by approximately 30 to 50%
when offloading from a macro to a small cell (e.g., pico and femto) [
35
,
36
]. Furthermore, the authors
of [
37
] propose a multi-objective optimization framework to maximize the energy efficiency of small
cell offloading while guaranteeing the quality of service under various network traffic loads.
In contrast, the 4G LTE data energy consumption is significant for those services that generate a
large amount of data traffic, especially for video applications such as video play, VR video and video
chat. Figure 5shows that the energy consumed by the wireless access network represents 59% to 82%
of the total end-to-end energy for various video applications. For these applications one of the most
effective ways to reduce the energy consumption of the 4G access network is to offload the heavy data
services from macro to small cells, in spite of the fact that femto-cell offloading might cause network
signaling overhead due to heterogeneous user mobility [38,39].
We have evaluated the service-specific end-to-end energy consumption using our energy models
to quantify the energy savings by offloading mobile services from a macro to a femto cell. The error bars
in Figure 6a show the minimum and maximum values from usage behavior. Figure 6a shows that the
service-specific end-to-end energy consumption could potentially be reduced by 30% to 73% compared
to the baseline (i.e., service energy consumption using macro cells). In addition, the proportions of the
LTE wireless network energy consumption for all mobile services decrease when comparing Figure 6b
with Figure 5b. Offloading is particularly effective for heavy data services. For example, for the
VR application the average total energy consumption decreases from 2470.5 to 603.3 Joules and the
proportion of LTE wireless network energy consumption decreases from 82.1% to 11%.

Energies 2019,12, 184 14 of 18
Energies 2019, 12, x FOR PEER REVIEW 14 of 18
example, for the VR application the average total energy consumption decreases from 2470.5 to 603.3
Joules and the proportion of LTE wireless network energy consumption decreases from 82.1% to 11%.
Figure 6. End-to-end energy consumption of typical mobile services under different test scenarios: (1)
using offloading from macro cell to femto cell; and (2) using both small cell offloading and mobile
edge caching. (a) End-to-end energy consumption under different user behaviors; (b) average energy
breakdown when offloading; (c) average energy breakdown when offloading & edge caching. The
error bars show the minimum and maximum usages of the services.
Challenges: Although our results show that the end-to-end energy consumption of mobile
services could be significantly reduced by using small cell offloading, the deployment of a large
number of small cells could potentially increase both the capital expenditure and operational
expenditure of mobile operators. Furthermore, the energy consumed by a large number of small cells
could also increase the total network energy consumption. Therefore, the deployment of small cells
should consider the dynamic traffic demand patterns of mobile users. In this context, big data
analytics could be used to analyze the dynamic traffic patterns of different parts of the mobile access
networks to determine the ideal spots for small cell deployment. Furthermore, by understanding the
user behavior and the diurnal cycle of network conditions, small cells could be switched off during
low peak hours to save energy.
6.2. Multi-Access Edge Computing and Caching
Interestingly, Figure 6b shows that once heavy data services (particularly for topology U2DC
services) are offloaded from macro to small cells the energy consumption proportions of the wireline
core network and data center increase substantially to 35.4% and 44.8% of the total energy
149.2 162.2
603.3
20.5 37.6
264.7
497.2
81.0 90.6 152.8
0
300
600
900
1200
1500
Web Browsing Video Play VR IM Voice msg. IM picture
msg.
Audio chat 2
users
Video chat
Energy consumption (J)
Offloading Offloading & Edge caching
Topology U2DC Topology U2UvDC Topology U2U
(a)
79.4%
2.3%
8.1%
10.2%
48.9
%
6.1
%
19.9%
25.1%
8.9
%11.0%
35.4%
44.8%
97.6%
0.4%
1.2%
0.8%
34.2%
9.2%
34.6%
21.9%
92.9%
1.5% 5.6%
50.2%
10.6
%
39.2
%
96.1%
2.8%
0.5%
0.7%
85.2%
10.7%
1.8% 2.4%
36.9%
45.5%
7.5%
10.2%
Web browsing Video Play VR IM voice msg.
(30s)
IM picture msg. Audio chat 2
users
Video chat
(b) Offloading
(c) Offloading & Edge caching
Smartphone LTE wireless
Wireline core network Data center
Edge data caching
Figure 6.
End-to-end energy consumption of typical mobile services under different test scenarios:
(1) using offloading from macro cell to femto cell; and (2) using both small cell offloading and mobile
edge caching. (a) End-to-end energy consumption under different user behaviors; (b) average energy
breakdown when offloading; (
c
) average energy breakdown when offloading & edge caching. The error
bars show the minimum and maximum usages of the services.
Challenges: Although our results show that the end-to-end energy consumption of mobile services
could be significantly reduced by using small cell offloading, the deployment of a large number of
small cells could potentially increase both the capital expenditure and operational expenditure of
mobile operators. Furthermore, the energy consumed by a large number of small cells could also
increase the total network energy consumption. Therefore, the deployment of small cells should
consider the dynamic traffic demand patterns of mobile users. In this context, big data analytics could
be used to analyze the dynamic traffic patterns of different parts of the mobile access networks to
determine the ideal spots for small cell deployment. Furthermore, by understanding the user behavior
and the diurnal cycle of network conditions, small cells could be switched off during low peak hours
to save energy.
6.2. Multi-Access Edge Computing and Caching
Interestingly, Figure 6b shows that once heavy data services (particularly for topology U2DC
services) are offloaded from macro to small cells the energy consumption proportions of the wireline
core network and data center increase substantially to 35.4% and 44.8% of the total energy consumption,

Energies 2019,12, 184 15 of 18
respectively. Therefore, how to reduce the service energy consumption of these network segments
becomes an important question.
One possible solution is to use multi-access edge computing and caching (MEC). This emerging
technology aims to deploy a large number of distributed caches at the edge of the mobile network in
close proximity to users to reduce the service latency and mobile backhaul load [
40
,
41
]. By using MEC,
the necessity of transmitting data from the cloud through the wireline core network can potentially
be eliminated by deploying energy-efficient edge caching devices at the LTE mobile edge. Mobile
edge caching is mainly effective for Topology U2DC services because these services need to access
the content stored in the data server. Based on the energy modeling for a cloud data center, we model
the MEC’s energy consumption as a function of the power consumption of edge caching equipment,
cache size, content refresh frequency, and the number of users sharing the cache. We assume that
power-efficient high-speed solid-state storage is used for edge caching, with power consumption
ωca
of 6.25
×
10
−12
watt/bit [
42
]. The average caching duration, the data throughput of the service
and the average number of users who share the content for the duration of caching are multiplied to
obtain the MEC’s energy consumption of a mobile service. Here, we assume that the average caching
duration is one day and the average number of users is 30 [
43
]. We have evaluated the end-to-end
energy consumption of Topology U2DC services to quantify the energy savings by using the MEC
network architecture.
Figure 6a shows the total energy consumption of Topology U2DC services using femto cells and
edge caching. It should be highlighted that the edge caching technology is not suitable for topologies
U2UvDC and U2U because the transmission links will not be shortened in these network topologies
despite the introduction of small cells and edge caching techniques. Therefore, only the energy
consumption of services using Topology U2DC are presented in Figure 6when using femto cells and
edge caching. The bar chart shows that the total service-specific end-to-end energy consumption can be
further decreased. The error bars show the values from the minimum and maximum usage behavior.
For example, the total energy consumption of the VR application decreases by approximately 94%
from 2470.5 to 152.8 Joules by using both MEC and femto cells. Furthermore, Figure 6c shows that
the energy consumption proportions of the wireline core network become negligible and the energy
consumption proportion of the data source has decreased significantly by replacing the cloud data
center with edge caching.
Challenges: The deployment of a large number of distributed MEC servers at the edge of the
mobile access networks could potentially increase the overall network energy consumption. Similar
to the deployment challenges of small cells, mobile operators could make use of big data analytics
in determining the ideal locations for the deployment of MEC servers. However, managing a large
number of MEC servers could be challenging for mobile operators since it involves the management
of caching and computing resources. Therefore, mobile operators require predictive analytics to
understand and forecast local user behaviors and the usage patterns of local applications so that
resource orchestration in MEC networks could be optimized in real-time.
7. Conclusions
The ongoing increase in the energy consumption of all network segments of mobile applications
and services has become a major concern for all stakeholders, including the smartphone manufacturers,
mobile application developers, mobile operators, cloud providers and consumers. However, without a
better understanding of service- and application-specific end-to-end energy consumption, effective
energy-efficient techniques cannot be developed, because different services exhibit different total
energy consumption and have different proportions of energy consumption for each network segment.
Therefore, we have developed a service-specific end-to-end energy consumption model to assess the
total energy consumption based on 3 different network topologies. We developed test cases for 15
services from 7 mobile applications to assess the service-specific end-to-end total energy consumption.
We also conducted sensitivity analysis by assessing the service energy consumption for different

Energies 2019,12, 184 16 of 18
user behaviors. For current network topologies, we found that the smartphone and the LTE wireless
network are responsible for most of the total service energy consumption. By understanding the root
causes of service-specific energy consumption in each network segment, we were able to evaluate
two potential energy-efficient solutions for future network topologies: (i) offloading mobile services
from macro to smaller cells; and (ii) mobile edge caching. Our results show that these two solutions
combined could potentially reduce the service energy consumption by up to 81% and 83% for video
play and VR, respectively. Finally, we presented some future challenges and research work for effective
deployment and management of small cells and MEC networks.
Author Contributions:
M.Y., C.A.C. and A.F.G. conceived of and designed the proposed scheme. M.Y., C.A.C.
and A.F.G. conceived, designed and performed the experiments. M.Y., C.A.C., J.Y. and L.C. analyzed the data.
A.N. and C.L. supervised the whole project. M.Y. and C.A.C. wrote the manuscript. A.F.G., L.C., A.N. and C.L.
reviewed the manuscript. All authors read and approved the final manuscript.
Funding:
This research was funded in part by the Fundamental Research Funds for the Central Universities of
China, grant number 3132018XNG1816 and 2018CUCTJ078.
Acknowledgments:
The authors would like to thank the staff of the School of Communication and Information
Engineering, Faculty of Science and Technology, Communication University of China (CUC), for their support
during this study.
Conflicts of Interest: The authors declare no conflict of interest.
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