Application-Based QoS Evaluation of Heterogeneous Networks

David C. Wyld et al. (Eds) : NETCOM, NCS, WiMoNe, CSEIT, SPM - 2015
pp. 161–172, 2015. © CS & IT-CSCP 2015 DOI : 10.5121/csit.2015.51613
APPLICATION-BASED QOS EVALUATION
OF HETEROGENEOUS NETWORKS
Farnaz Farid, Seyed Shahrestani and Chun Ruan
School of Computing, Engineering and Mathematics,
Western Sydney University, Sydney, Australia
farnaz.farid@westernsydney.edu.au,
s.shahrestani@westernsydney.edu.au,
c.ruan@westernsydney.edu.au
ABSTRACT
Heterogeneous wireless networks expand the network capacity and coverage by leveraging the
network architecture and resources in a dynamic fashion. However, the presence of different
communication technologies makes the Quality of Service (QoS) evaluation, management, and
monitoring of these networks very challenging. Each communication technology has its own
characteristics while the applications that utilize them have their specific QoS requirements.
Although, the communication technologies have different performance assessment parameters,
the applications using these radio access networks have the same QoS requirements. As a
result, it would be easier to evaluate the QoS of the access networks and the overall network
configuration depending on the performance of applications running on them. Using such
application-based QoS evaluation approach, the heterogeneous nature of the underlying
networks and the diversity of their traffic can be adequately taken into account. In this paper,
we propose an application-based QoS evaluation approach for heterogeneous networks.
Through simulation studies, we show that this assessment approach facilitates better QoS
management and monitoring of heterogeneous network configurations.
KEYWORDS
QoS; QoS metric; Dynamic weight; Unified QoS Metric; application weight; weight
1. INTRODUCTION
The advancement and proliferation of modern wireless and cellular technologies have changed
the way people work and communicate. By 2018, the data traffic over mobile networks is
expected to reach 15.9 exabytes per month, with 69 percent of that consisting of video. There will
be over 10 billion mobile-connected devices by 2018, which will exceed the world’s expected
population at that time [1]. To deal with this growing number of devices and the massive
increases in traffic, the networks are moving towards an all-heterogeneous architecture. Any
heterogeneous network constitutes of different communication technologies. These technologies
have distinct bandwidths, coverage area, and operating frequencies. Their QoS characteristics,
such as delay, throughput, and packet loss, as well as usage and implementation costs also differ
from each other. As a result, the adaptation of heterogeneous network-based architecture for the
provision of different applications especially multimedia applications faces significant

162 Computer Science & Information Technology (CS & IT)
challenges. Among these challenges, QoS-related issues such as the effective QoS evaluation,
management, and monitoring still top the list [2].
Managing QoS for video or voice applications over heterogeneous networks is a challenging task.
A research from Nemertes shows that the companies invest a significant amount of their budget
to manage VoIP applications over these network architectures. For small enterprises, the annual
costs range from $25,000, and for global enterprises this cost is around $2 million [3]. Therefore,
the enterprises need to dedicate a lot of their effort to ensure service quality at every level of the
network. System downtime is another challenge for businesses, which could often happen due to
poor network management and monitoring. According to Gartner research, the hourly cost of
system downtime for large enterprises was $42,000, with a typical business on average,
experiencing 87 hours of downtime per year [4]. As a result, the QoS of any service-based
network should be monitored, managed, and evaluated on an ongoing basis.
In this paper, we introduce some unified metric measurement functions that can help with
assessing the application-based performance of heterogeneous networks. By taking the relevant
performance-related parameters into account, these functions quantify the underlying network
and the application-related QoS with a numerical value. The proposed approach considers the
effects of the QoS-related parameters, the available network-based applications, and the available
Radio Access Networks (RANs) to characterize the network performance with a set of three
integrated QoS metrics. The first metric denotes the performance of each possible application in
the network. The second one is related to the performance of each of the radio access networks
present in the network. The third one characterize the QoS level of the entire network
configuration. The core of this method is considering the effects of different application and radio
access networks on the QoS of heterogeneous networks.
The rest of the paper is organized as follows: Section 2 discusses the background and motivations
of this work, Section 3 illustrates the concept of unified QoS metric. Section 4 presents the
application weight calculations in detail. The impact and the significance of the applications for
QoS analysis are then discussed in Section 5. The last section gives the conclusions and proposes
the future works.
2. RELATED WORK AND MOTIVATIONS
QoS evaluation in heterogeneous networks has been an active area of research [5, 6]. Most of the
existing research focuses on the partial QoS evaluation of a heterogeneous network by deriving
the performance level of a single access network and a single application present within that
environment. Also, different studies have come up with various performance metrics for QoS
evaluation of these networks. The conventional methods do not consider the performance of all
the applications running on a network. For example, if there are voice and video conferencing
running over a UMTS network, these methods do not include the performance analysis of these
applications to quantify the overall network QoS. Furthermore, there is no unified metric to
quantify the QoS of a network, which considers the performance of all the access networks present
in it. For example, in a heterogeneous environment, there are three access networks, such as
UMTS, WiMAX, and LTE. At present, no unified metric can represent the performance of this
network configuration using the QoS-related parameters of these access networks.

Computer Science & Information Technology (CS & IT) 163
Multi-criteria decision-making (MCDM) or Multi-Attribute Decision Making (MADM)
algorithms have been widely employed in the area of the heterogeneous networks from vertical
handover perspectives [7, 8]. The most common criteria, which are considered during this
ranking process, are service, network, and user related [9]. These can include received signal
strength, type of the service, minimum bandwidth, delay, throughput, packet loss, bit error rate,
cost, transmit power, traffic load, battery status of the mobile unit, and the user’s preferences. To
facilitate the combining of these attributes into a single value, based on their relative importance,
a weight is assigned to each attribute.
The weights for QoS-related parameters have both subjective and objective elements in it [10].
The network attributes, for example, the importance of received signal strength and bandwidth
are objective in nature. Application related attributes such as delay, packet loss, and jitter show
some objectivity. However, some studies have already revealed their potential subjective natures.
For example, a study conducted in Tanzania shows that the users give moderate importance to
end-to-end delay over packet loss [11]. The study by ETSI reveals that the users give strong
importance to end-to-end delay over packet loss [12]. Therefore, the importance of application-
related performance parameters can vary based on changing contexts, for example, between home
and industrial environments or urban and rural areas. The significance of applications can vary
depending on the context as well. For example, an application related to the education services
can have higher importance compared to one that provides some entertainment services.
Moreover, the absence or presence of an application will affect the weights of others in the
network.
For weight assignment, the available literature on QoS evaluation in network selection has mostly
used the Analytic Hierarchy Process (AHP) method, which is primarily developed by Saaty [7, 8,
13]. Some studies have also assigned fixed weights to these parameters based on their importance
to service performance [14]. Both AHP and fixed weight methods are unable to handle the
subjective and ambiguous factors related to weight determination such as context-based
significance. In this study, Fuzzy Analytical Hierarchy Process (FAHP) with the extent analysis
method is applied to bring the context-based information into the picture. This method is capable
of handling ambiguity in any particular subject. It is also possible to assign the weights
dynamically to the relevant parameters by using this method.
3. THE UNIFIED QOS METRIC
The quality of service on any network or application is usually evaluated through a set of specific
metrics. For example, to assess the performance of any voice application, the delay, jitter and
packet loss are measured and compared with the acceptable values of these parameters. Similarly,
the QoS of any network is evaluated through parameters such as delay, packet loss, throughput,
and available bandwidth. The presence of different types of communication technologies and
applications in a heterogeneous network makes its QoS assessment method a challenging task. To
deal with such challenges, this paper introduces unified metric measurement functions.
The QoS evaluation approach proposed in this work considers any heterogeneous network as a
set of three layers; these are the application layer, the radio access network layer and the network
configuration layer. Each of these layers uses a function to quantify a unified QoS metric, which
flows to the next layer and derive the combined metric of that layer. Figure 1 shows the flowchart
of the proposed approach. In the application layer, a function is defined to derive the QoS of each

application through a unified metric. This function combines the values of several application-
related performance metrics. As such, the QoS of a network-based application is treated as a
function of QoS-related parameters. This can be expressed as:
Figure 1. The concept of Unified QoS Metric
( )1 2, ,...,A pQoSAM f QP QP QP= (1)
where A denotes a network-based application, and QP refers to the QoS-related parameters. Then
in the radio access network layer or RAN layer, the QoS of each access network, which are
present in the network, is evaluated. This evaluation is conducted based on the performances of
the active applications in those access networks. Hence, the QoS of an access network is viewed
as a function of the application QoS metrics. It can be expressed as:
( )( )1,2,....,i mR AQoSRM f QoSAM
=
= (2)
where R denotes any radio access network, and i refers to the number of active applications
present on a network. Finally, to evaluate the QoS of the overall network configuration another
function is defined, which uses the radio access network metrics as its input. This can be
expressed as:
( )( )1,2,....,j nRQoSCM f QoSRM
=
= (3)
where j refers to the number of radio access networks present on a network.

4. THE APPLICATION WEIGHT CALCULATION
The weights of applications are considered during the QoS evaluation of radio access networks.
The radio access network metric can be used to label a network as a particular service-oriented
network such as education or health by integrating the application weights. For instance, if the
QoS metric of a radio access network that is mainly used for health services, is always good, then
that network can be taken as a suitable health service-oriented one for future use.
Table 1. Example of Application Weight Calculation
The importance of the applications is subject to change depending on the requirements of
particular networks. The service operators can update these criteria according to their particular
circumstances. The criteria for this study have been formulated using the studies relevant to
distance education-based service models [15]. For instance, in these service models,
Videoconferencing (VC) bears more significance compared to voice-based applications. On the
other hand, in a more general sense, VC may be less significant than voice-based applications as
the latter is more easily amiable to the users. Therefore, the criteria to decide the applications
weights in this regard can be the number of users using the application and the purpose and the
context of that application usage.
Table 1 shows the example of two networks, 1N and 2N , which have applications 1A and 2A with
different number of users. The application weights are expressed as 1
1
N
Aw , 2
1
N
Aw , 1
1
N
Bw , and 2
2
N
Bw . In
this paper, the weights of these applications are defined based on two criteria, the importance of
the service, to which it belongs, and the number of users using that application. Other evaluation
rules can be integrated based on individual needs. In the network 1N , it is considered that 1
1
N
Aw >
1
1
N
Bw . This is determined depending on the fact that in the network 1N , the application 1A is used
by more users than the application 2A as the application 1A is used for educational services,
whereas, the application 2A is used for entertainment services. Therefore, when the QoS metric
in the network 1N is calculated considering these application weights, the QoS metric value
reflects the significance of the service the application provides. As a result, if the QoS value is
good for the application 2A and poor for the application 1A , the outcome of the QoS level of the
Networks Considered Parameters Weights
1N
Application Service
Number of
Users 1A 2A
1A Education 20
1
1
N
A
w
1
1
N
B
w
2A Entertain-
ment
18
2N
1A Health 10
2
1
N
A
w
2
2
N
B
w
2A Education 6

network will be poor. That is because, the majority of the users in that network experience poor
performance for a valuable service. If, in any case, the entertainment service application has more
users, the result will also be same as the education service is set to have higher significance than
the entertainment service. These findings can change based on specific network requirements.
On the other hand, the Network 2N supports both education and health services. As more users
are using the health services compared to the education services, the application weight of 1A is
greater than 2A , 2
1
N
Aw > 2
2
N
Bw . If the QoS value of the network 2N is good, then it can be
categorized as a health service-oriented network. Therefore, the configurations of 2N can be
recommended for any network that aims to deploy network-based health services in the future.
Service operators can input these criteria to change the weights dynamically for any network.
The weight calculation involves two steps. At first, the alternatives, criteria, and the fuzzy
judgement matrix are defined. Then in the second step, the actual weight is calculated based on
those measures. FAHP-based calculations include: establishing a set of alternatives
{ }1 2, ,........, mX x x x= , a set of goal or evaluation criteria { }1 2, ,........, nG g g g= , a fuzzy judgement
matrix (FJM), with elements ‫ݎ‬పఫ෥ that represents the relative importance of each pair of criteria i
and j, and a weighting vector ( )1 2, ,......, nw w w w= . Both steps involve the concept of Triangular
Fuzzy Number (TFN) and fuzzy addition and multiplication operations. To derive the FJM for
the first step the importance scale presented in Table 2 is used. It shows the TFN ( ), ,t t tK l m ut =
where t=1, 2,…, 9, and ݈௧, ‫ݑ‬௧ and ݉௧ are the lower, upper and the middle value of the fuzzy
number ‫ܭ‬௧ respectively. Table 3 shows the pair-wise comparison matrix for VC, voice, and VS
applications formed based on the cited studies. The importance scale of Table 1 is used for the
comparisons. If one of the applications is absent from the network, these pair-wise comparison
matrices are subject to change.
For the second step of FAHP, different methods are proposed. The most prominent one is
Chang’s extent analysis method [16]. This method is chosen as it provides easy and flexible
options for the weight calculation. The steps of the extent analysis method are as follows:
At first, the sums of the each row of the defined fuzzy comparison matrix are calculated. Then the
normalization of the row sums is conducted using fuzzy multiplication to obtain fuzzy synthetic
analysis. Therefore, in the fuzzy comparison matrix, the fuzzy synthetic analysis of criteria ‫ܩ‬௜ of
alternative ܺ௠ is calculated as:
‫ܦ‬ீ೔
௑೘
= ∑ ‫ݎ‬పఫ෥௡
௝ୀଵ ⨂ ൣ∑ ∑ ‫ݎ‬పఫ෥௡
௝ୀଵ
௡
௜ୀଵ ൧
ିଵ
= ൬
∑ ௟೔ೕ
೙
ೕసభ
∑ ∑ ௨೔ೕ
೙
ೕసభ
೙
೔సభ
,
∑ ௠೔ೕ
೙
ೕసభ
∑ ∑ ௠೔ೕ
೙
ೕసభ
೙
೔సభ
,
∑ ௨೔ೕ
೙
ೕసభ
∑ ∑ ௟೔ೕ
೙
ೕసభ
೙
೔సభ
൰ (4)
where , {1,2,3............. }i j n= and n is the number of criteria. In step 2, in order to rank the criteria
against each alternative, the degree of possibility of two fuzzy numbers is applied. Therefore,
2 1
2 2 2 1 1 1( , , ) ( , , )X Xm m
G G
D Dl m u l m u≥ is computed by the following equation:

Table 2. A FAHP-based Pair-wise Comparison Importance Scale
Fuzzy Numbers Definition Triangular Fuzzy
Number
1 1 1 1( , , )k l m u Equal importance (1,1,1)
2 2 2 2( , , )k l m u Intermediate values (1/2,3/4,1)
3 3 3 3( , , )k l m u Moderate importance (2/3,1,3/2)
( )4 4 4 4, ,k l m u Intermediate values (1,3/2,2)
( )5 5 5 5, ,k l m u Strong importance (3/2,2,5/2)
( )7 7 7 7, ,k l m u Very strong importance (5/2,3,7/2)
( )9 9 9 9, ,k l m u Extreme importance (7/2,4,9/2)
( ) ( ) ( )2 1
21
sup min ,m m
Xmm
G
X X
XG G DDG
V D D x yµ µ
  
≥ =   
  
(5)
It can be also expressed as:
( )
( )
2 1
2 1
1 2
1 2
2
2 2
2 2 1 1
1
0
( ) ( )
m m m m
X m
G
X XX X
G G G G
if m m
if l l
D
l u otherwise
m u m l
V D D hgt D D
dµ


≥
= = ≥
 −
 − − −
 ≥ = ∩ 
 
(6)
and
( )
( )
1 2
1 2
1 2
2 1
1
2 1
1 1 2 2
1
0
( ) ( )
m m m m
Xm
G
X XX X
G G G G
if m m
if l u
D
l u otherwise
m u m l
V D D hgt D D
dµ


≥
= = ≥
 −
 − − −
 ≥ = ∩ 
 
(7)
where d is the ordinate to validate if the highest intersection point D is between
2
X m
GD
µ and
1
X m
GD
µ . Both the values of ( )2 1
m mX X
G GV D D≥ and 1 2
m mX X
G GV D D 
 
 
≥ are required to compare
2
X m
GD
µ
and
1
X m
GD
µ . For large numbers of criteria, the degree of possibility is applied as:
( ) ( ) ( ) ( )1 2 3 1 2 1 3 1
, ,.............., and and.....m m m m m m m m m m
n n
X X X X X X X X X X
G G G G G G G G G GV D D D D V D D D D D D ≥ = ≥ ≥ ≥ 
( )1
min m m
n
X X
G GV d d= ≥ (8)

Table 3. Pair-wise Comparison Matrix for different Applications
Applica
-tions
Criteria VC Voice VS
VC Purpose of
Usage
(1, 1, 1) (3/2,2,5/2) (1.09,
1.5,
2)
(2/3,1,
3/2)
(0.84,
1.25,
0.75)Number
of Users
(1, 1, 1) (2/3,1,3/2) (1,3/2,2)
Voice Purpose of
Usage
(2/5,1/2,2/3) (0.54,
0.75,
1.09)
(1, 1, 1) (2/3,1,
3/2)
(1.59,
2,
2.5)Number
of Users
(2/3,1,3/2) (1, 1, 1) (5/2,3,
7/2)
VS Purpose of
Usage
(2/3, 1,3/2) (0.59,
0.84,
1.25)
(2/3,1,3/2) (0.48,
0.67,
0.95)
(1, 1, 1)
Number
of Users
(1/2,2/3,1) (2/7,1/3,2/5) (1, 1, 1)
Assume that ( )m
n
X
Gd C′ = ( )1
min m m
n
X X
G GV d d≥
In step 3, the weight vector w for each alternative is calculated. This is obtained as:
( ) ( ) ( )( )1 2
' , ,...........,m m m
n
T
X X X
m G G Gd C d C d C′ ′ ′=w (9)
In step 4, the normalized weight vector is calculated for each alternative as:
( ) ( ) ( )( )1 2
, ,....................,m m m
n
TX X X
m G G Gd C d C d C=w
( ) ( ) ( )1 2
, ,........,
1 1 1
mm m
n
m m m
n n n
XX X
GG G
X X X
G G G
d Cd C d C
n n n
C C C
j j j
=
∑ ∑ ∑
= = =
 
 
 
 
 
 
 
(10)
The weight vector of the considered applications is calculated as:
( ) ( )'
VC,Voice,VS 1, 0.94, 0.56A =w
The normalization weight vector is as follows:
( ) ( )VC Voice VS, , 0.4,0.38,0.224A w w w= =w
5. IMPACT OF APPLICATION IMPORTANCE
This section evaluates the impacts of dynamic application weights on the unified QoS metrics.
The performance of a few network-based service models have been analyzed using the fixed

weight-based method in our previous work [17]. In this work, a detailed analysis is conducted to
present the effects of dynamic weights on the performance of the same service models. The
weights are calculated for each application according to the changing circumstances of the
network. These weights are entered as inputs to derive the network QoS metric.
In the previous work, the voice application is set to have a higher importance than the VS
application and the weights have been fixed as 0.6 and 0.4. In this work, those weights are set to
change based on the pair-wise comparison matrices presented in Table 3. Figure 2 shows the QoS
analysis of the scenario with twelve voice calls and one VS session on the network. The figure
clearly indicates that when the voice and VS applications have equal importance, the access
network has a good QoS level (e.g. 0.81). It shows an average QoS level (e.g. 0.62) for voice and
a good QoS level (e.g. 1) for VS. When the importance level of voice application has been
changed from having equal to extreme importance over VS application, the access network QoS
comes down to an average value of 0.63. Although, the performance of the VS application is
good, because of having a lower importance, it has less effect on the network QoS level. On the
other hand, the voice application, being extremely important, has a greater impact on the network
QoS level.
Figure 2. The Effect of Application Importance on Network QoS
Figure 3 shows a similar type of analysis with the altered importance of voice and VS
applications. When the VS application has extreme importance, the network QoS improves due to
the impact of application weights on the network performance.

Figure 3. The Effect of Application Importance on the Network Performance
Figure 4. Network Performance Analysis with changing Application Importance
Figure 4 illustrates the QoS in a voice-based network for different number of calls when the
importance of the application changes. When the VC application has extreme and moderate
importance over voice application respectively, the network shows a poor QoS level. The reason
is that the VC application with ten and twenty voice calls on the network experience a poor
quality. On the other hand, the network takes an average QoS level with ten and twenty voice
calls when the voice application has moderate and extreme importance over VC application
respectively.

6. CONCLUSIONS
In this paper, an application-based QoS analysis method has been proposed and evaluated. In
assessing the overall QoS level of a heterogeneous network, the levels of importance of
applications are included as weights. The key contributions of this work include the proposing of
a methodical approach for calculating and applying these weights. Extensive simulation studies,
utilizing these weights for QoS assessment of various heterogeneous configurations supporting a
variety of applications, have also been carried out. These studies demonstrate how the inclusion
of the application importance weights for QoS evaluations, can assist in a systemic choosing of a
fitting network configuration. In our future works, we intend to include several other factors that
can influence the QoS provisions of a heterogeneous network supporting real-time applications.
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distance education,” Proc. Local Computer Networks Workshops (LCN Workshops), 2013 IEEE
38th Conference on, 2013, pp. 17-23.
AUTHORS
Farnaz Farid is pursuing her PhD degree in Information Technology and Communications at the Western
Sydney University. Prior to that she has worked in China as a web application developer and web business
SME at IBM. Her research interests include wireless and cellular networking, web engineering, and
technology for development.
Seyed Shahrestani completed his PhD degree in Electrical and Information Engineering at the University of
Sydney. He joined Western Sydney University in 1999, where he is currently a Senior Lecturer. He is also
the head of the Networking, Security and Cloud Research (NSCR) group at Western Sydney University.
Chun Ruan received her PhD degree in Computer Science in 2003 from the University of Western Sydney.
Currently she is a lecturer in the School of Computing, Engineering and Mathematics at Western Sydney
University. Prior to that, she worked as an associate professor, lecturer and associate lecturer at the
Department of Computer Science, Wuhan University, China.

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