IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016

55

Mass-User Satisfaction for NFV-Based Application Specific
Network
Kazunori Ueda†*, Makoto Iwata†, Ken-ichi Baba††, and Shinji Shimojo††††††

School of Information, Kochi University of Technology, Kochi, 782-8502, Japan
Department of Information and Communications Engineering, Kogakuin University, Tokyo, 163-8677, Japan†††
Cybermedia Center, Osaka University, Osaka, 567-0047, Japan

Summary
By virtue of advanced virtualization approaches such as network
function virtualization (NFV) or software defined network (SDN),
it has been recently possible to provide valuable network services
dedicated to specific applications or users. In order to support
those value-added services suitably, the network must be
operated and managed according to not conventional per-flowbased QoS policy but service-oriented policy. This paper
discusses typical application specific networks in terms ofservice-oriented metrics and then proposes mass-user satisfaction
as one of application-oriented metrics. The proposed mass-user
satisfaction can be transparently translated into physical resource
management and can be optimized suitably according to serviceoriented policy specific to applications or users. Thisadvantageous feature is revealed through simulation results of atypical network configuration.

Key words:
Network function virtualization, Application specific network,
Mass-user satisfaction, Virtual network management

1. Introduction
Recently, the more the communication devices, network
applications, and user needs diversify, the more difficult it
is to implement a network function or functionality bycoupling it to a physical device or network tightly.
Therefore, various virtualization approaches have been
investigated, e.g., network function virtualization (NFV)
and software defined network (SDN).
The NFV approach makes it possible to build, deploy,and operate network functions (NFs) virtually bydecupling software from hardware, i.e., physical devices[1], [2]. Thus, network service providers can easily
introduce new NFs without deploying additional physicaldevices. In order to introduce virtual network functions(VNFs) on an actual network, it is necessary to adopt an
additional management mechanism such as thefunctionality provided by NFV Management and Network
Orchestrator (NFV MANO). NFV MANO can also
support various resource allocation functions to provide
end-to-end services on the network [3]-[5].

Manuscript received September 5, 2016
Manuscript revised September 20, 2016

Fig. 1. An overview of NFV-based application specific network

SDN approaches [6], [7] enable us to virtualize thephysical networks and to operate multiple virtual networks,
called slices. There have been a lot of proposals to manage
multiple slices on physical networks efficiently [8], [9].
Since SDN can harmonize with VNFs or an NFV MANO,
every slice in SDN can be operated and managed flexibly
by decoupling software from hardware, i.e., both physicaldevices and networks. Hence, it is prompt and flexible toprovide valuable network services or applications to end
users even if networking and computing resources
environments are constrained.
In such an environment, network resources and costs
should be utilized to improve valuableness orcompetitiveness of the network services as much as
possible. That is, such virtualization approaches make iteasy to provide application specific network services
promptly. In this sense, conventional per-flow-based QoS
policy is no longer crucial rather than application-oriented
or provider-oriented management policy. In order to
conduct the management of an application specificnetwork, mass-user satisfaction is significant as a simple
application-oriented metric for the network provider. In
this paper, the NFV-based application specific network is
defined as a network in which a network application issuitably classified and its classified packets are transferred
according to the mass-user satisfaction, as shown in Fig. 1.
This paper discusses typical application specificnetworks in terms of service-oriented metrics and thenproposes the mass-user satisfaction as one of applicationoriented metrics. The proposed mass-user satisfaction canbe transparently translated into physical resource
management and can be optimized suitably according to
the service-oriented policy specific to applications or users.

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IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016

This advantageous feature is revealed through simulation
results of the typical network configuration in the paper.

2. Application Specific Networks
One of the most beneficial features in the NFV-based
application specific networks is to build and operatevarious networks flexibly, even in temporal, comparedwith the conventional non-virtualized networks. Even if
the requirements of the network alter eventually, the NFVbased network can be modified and operated adaptively. In
addition, since the building effort and cost of the nonvirtualized networks are generally higher than those of the
NFV-based networks, the NFV-based networks can bebuilt and operated with a cost-effective smaller scale.
This section discusses the following typical application
specific networks and their characteristics.
i. Emergency network, e.g., disaster, terrorism-related
incident.
ii. Massive event network, e.g., public viewing,
exhibition.
iii. Service provider network, e.g., a mobile virtual
network operator (MVNO), an application service
provider (ASP).
In case of an emergency such as catastrophic disaster,
there might be not enough network resource to meetvarious requirements of a large number of users. If aphysical infrastructure is suddenly damaged, the users
must share an access network such as a satellite internet
access with heavier resource constraint. Even if thephysical infrastructure has no damage, a specific network
point such as an evacuation place will be temporally
crowded with the user traffic.
In the management strategy for such networks, network
resource should be preferentially allocated to users whocollect essential information about present situations ofutilities and distributed food. The users also want to keepin contact with a family, relatives, friends, and colleagues
and might use some real-time network applications such as
IP telephony. If the emergent situation changes with thetime, the importance of the network applications mightchange along with users’ requirements. In this case, themanagement strategy of the network must be adaptively
applied according to the resource constraints with VNFs.
The massive-event network is a temporal network which
has limited resources for the Internet connection. In
addition, the network traffic depends on a time schedule ofthe event and the usage trends of time-dependent contents
or applications. A flexible network function such as VNF
is useful in the resource constrained network. Examples ofsuch networks are temporal networks specialized for a
public viewing, technical exhibitions, and technicalconferences.

In such networks, movies requiring high bandwidth aresometimes provided for attendees. For example, there are
replay movies of impressive scenes in a public viewing
and commercial movies of new products in a technicalexhibition or conference. Many attendees may watch amovie at the same time once it is announced that the movie
is available. This causes bursty traffic. Basically, capacityof a network constructed for an event is limited and traffic
congestion occurs in such situation. Furthermore,satisfaction of the attendee depends on availability of realtime network application such as video streaming. Thosefeatures imply that the real-time application should bemanaged as not an essential one but a prior one.
Service provider network can be flexibly constructed byadopting the virtualization approaches such as NFV. A
typical example network is a wireless access network of an
MVNO, which is constructed virtually on a mobile accessinfrastructure of a mobile network operator (MNO). The
MVNO provides an inexpensive internet connection
service to customers. By managing network applications
efficiently, the MVNO can reduce a usage cost of both amobile access infrastructure and a transit network. As aresult, the MVNO can provide moderate internet
connection service as well as improve competitiveness by
reducing the cost.
Due to the expensive extra charge of fixed rate service, acustomer wants to offload his/her traffic onto a freewireless network. When the free wireless network is notavailable, the customer may not want to acquire a network
resource more than necessary because the extra amount ofpackets beyond the upper limit results in expensive extra
charge or low bandwidth regulation. Thus, the reputationof the MVNO is evaluated by total satisfaction of thecustomers, so that the management should be strategic andmoderate on manageable network applications along withbusiness model of the company.
Typical application specific networks discussed above
require a management strategy according to physicalresource constraints and characteristics of the networks.
Under a resource limited environment, there may be abottleneck that causes quality degradation of the network.
In order to mitigate the bottleneck, a part of the network
involving the bottleneck must be managed and operated
appropriately, i.e., priority control and bandwidth
allocation are performed because the managed network iscongested due to overconcentration of flows of network
applications. Based on the discussions in this section, theapplications deployed in the NFV-based application
specific network can be classified as summarized inTable 1. There are (a) essential application which must be
accepted mandatorily, (b) manageable application whose
priority is scaled along with the characteristics andresource constraints of the network, and (c) others that canbe regarded as background traffic.

IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016

Table 1: Management strategy of network applications under resource
constraints.
resource constraint
heavy <----> lightessential application
mandatory
manageable application marginal <----> priorothersmarginal

A simple scenario is to introduce a service-oriented
operation and management strategy only when the
manageable application is marginal under heavy constraint,
or when the manageable application is prior to others
under light resource constraint.

3. Mass-User Satisfaction
In an application specific network, network applications
are classified into three classes and managed by VNFs.
Although a metric is required to conduct the managementof network applications, not only conventional metrics
used in per-flow-based QoS or quality of experience(QoE) management [10]-[18] but also a perspective metricincluding an application-oriented or provider-oriented
metric must be considered.
This paper focuses on a mass of users and introduces
mass-user satisfaction as a simple application-oriented
metric according to the mass-user satisfaction in the NFVbased application specific network. Each network
application flow is suitably classified and controlled.
Here, a single user satisfaction is introduced to translate
physical QoS metrics into the application-oriented metric.
In this paper, the user satisfaction related to the bit rate is
calculated by Equation 1 derived from the Waver-Fechner
Law as shown in Fig. 2. The Waver-Fechner Law can beused for measurement of QoE [19] because it describes therelationship between actual intensity of physical stimulus
and perception. When U represents a set of users who use
a network application, for any user i ∈ U, the single user
satisfaction S(bi ) is formulated as follows:log(𝑏𝑏𝑖𝑖 /𝑏𝑏𝑀𝑀𝑀𝑀𝑀𝑀 )𝑆𝑆(𝑏𝑏𝑖𝑖 ) =(1)log(𝑏𝑏𝑀𝑀𝑀𝑀𝑀𝑀 /𝑏𝑏𝑀𝑀𝑀𝑀𝑀𝑀 )Satisfaction (Intensity)where bi represents an average bit rate of the network
application which the user i uses. bMAX and bMIN are a
maximum and minimum bit rate of the network
application respectively. bMAX is set to an upper boundary
estimated under the service and network configurations.
bMIN is set to guarantee the practical use of the network
application with the lowest quality.
1.0

0.8
0.6
0.4
0.2
0b

MIN

Bit rate (Perception)

bMAX

Fig. 2. Single user satisfaction based on bit rate.

57

Mass-user satisfaction SM , an extension of the single user
satisfaction, is formulated by the sum of single user
satisfactions as follows:(2)
𝑆𝑆𝑀𝑀 = ∑𝑖𝑖∈𝑈𝑈 𝑤𝑤𝑖𝑖 𝑆𝑆(𝑏𝑏𝑖𝑖 )where wi is a weight of the user i, which is set to 0 or 1.
The mass-user satisfaction of all users can be calculated byassigning all weights to 1. When focusing on specific users,
weights of selected users are set to 1 and those of otherusers to 0 as follows.
1(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢)
𝑤𝑤𝑖𝑖 = �(3)0(𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒)

Normalized mass-user satisfaction SNM can be obtained byaveraging the SM by the number of users |U| as follows.
1
𝑆𝑆𝑁𝑁𝑁𝑁 = |𝑈𝑈| ∑𝑖𝑖∈𝑈𝑈 𝑤𝑤𝑖𝑖 𝑆𝑆(𝑏𝑏𝑖𝑖 )(4)

In this paper, the weight wi is set 0 or 1 for the mass-user
satisfaction. Although the weight can be set to a real
number for more flexible metrics, this effectiveness will be
investigated in future study.

4. Resource Management Guidelines
Network resource management based on the mass-user
satisfaction will be conducted along with individual
operation and management strategy of each network. For
the optimal management, it is necessary to study various
applications and networks and then to select a better
control scheme according to each situation. In this section,
useful resource management guidelines are discussed byreporting the simulation results of a typical NFV-based
network configuration with some applications.
Resource management becomes effective only when the
network involves some resource bottlenecks. The simplest
situation is represented as a bottleneck link between two
application specific networks. Our simulated network
shown in Fig. 3 consists of the network NA and NB,
where one VNF node configured on a physical node of the
network NA and the network NA is managed based on the
proposed mass-user satisfaction. Each link capacity is
assumed 1 Gbps because 1 Gbps is an affordable capacity
used for wired access link or wireless backbone.
Admission control, priority control, and bandwidth control
are performed as network management. In the admission
control, the number of flows is regulated according to a
network operation policy of each network. In other words,
each flow may establish its connection as a result of theadmission control. Priority and bandwidth controls are
performed according to classes of flows.

IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016

1Gbps

1Gbps
1Gbps

..........

..........

End userEnd user

1Gbps

1Gbps

End user
Application specific
Application specificnetwork NB
network NA: Physical network node
: VNF node on physical network node

End user

Fig. 3. Simulated network configuration.

In the simulations, all flows of network applications areclassified into three classes listed in Table 1. Flows ofessential application in the network are categorized as thehighest priority class A, e.g., broadcast application forpresent availability of utilities and distributed food. Flowsof manageable application are classified into class B, e.g.,real-time video streaming application. Flows of otherapplications are classified into the lowest priority class C,e.g., web surfing. As numerical examples, simulation
parameters for each application class are shown in Table 2.
As for protocol of transport layer, TCP is used for the class
A and C and UDP for the class B. The bit rate of the class
B is set to 6 Mbps. The number of flows is set to 100 for
the class A, from 100 to 300 for the class B, and 50 or 200
for the class C. Each flow passes through the bottleneck
link between two networks. Each flow is initiated at a
random timing within 1 second and it is terminated by 65
seconds in simulation time.
The Network Simulator ver. 2 (NS-2) [20] was adopted asa network simulator in order to trace connections andqueuing. Priority and bandwidth controls are performed
with a component of class based queueing (CBQ). CBQ isapplied to a bottleneck link for the outgoing flows of the
network NA. When priority control works, priorities 1, 2,
and 3 are assigned to the class A, B, and C, respectively.
Fractions of link bandwidth assigned to the class A, B, andC are 0.3, 0.6, and 0.1, respectively.
The formulas of the single user satisfaction and the massuser satisfaction introduced in Section 3 are adopted,
where one user corresponds to only one flow. bMAX for theclass A and C are set to 4.189 Mbps, which was measured
in case of no congestion, and bMAX for the class B is set to
6 Mbps. bMIN for all classes are set to 128 kbps. bMAX forthe class B and bMIN for all classes are set by referring to
the Main tier of level 3 and level 1 of H.265 (High
Efficiency Video Coding: HEVC) [21]. Table 2: Specifications of network simulation.

Protocolclass A(essential)
TCP

Bit rate

Variable

Number of flows
CBQ
Priority100
1class B(manageable)
UDP
fix(6 Mbps)100-300
2

class C(others)
TCP
Variable50, 200
3

Fractionof link
bandwidth
allocation

0.3

0.6

0.1

On simulation results, each single user satisfaction is
calculated from an average bit rate of each flow and themass-user satisfaction is also calculated from specificsingle user satisfactions and their weights with the formulaof the mass-user satisfaction. Simulation results are shownin Fig. 4 to 6, in which x axis is the number of flowsclassified into the class B and y axis is user satisfaction ofeach perspective.
400

Mass-user satisfaction(all classes)

58

350
300
250
200
150
100
50
0
100

class C: 200 flowsclass C: 200 flows (with priorities)class C: 50 flowsclass C: 50 flows (with priorities)
150
200
250
Number of flows (class B)

300

Fig. 4. Mass-user satisfaction (all classes).

Firstly, Fig. 4 shows the mass-user satisfaction focusing on
all application classes. In the figure, triangle and squareplots denote the case of priority control and circle and xmark plots denote the case of no priority control. Circle
and triangle plots represent conditions that the number ofthe class C flows is 200, and x-mark and square plots
represent conditions that the number of the class C flows is
50, respectively. The mass-user satisfaction of circle andx-mark plots is relatively higher than that of triangle andsquare ones. In this case, the normalized mass-user
satisfaction shown in Fig. 5 is not degraded so much when
the number of flows in the class B increases. To increase
the number of flows in the class B implies to cause heavier
resource constraint. Therefore, if it is necessary for the
manageable applications to be assigned marginal resource
under heavy resource constraint, network resources should
not be allocated with class-based traffic management. Thissituation can be associated with early phase of theemergency network as discussed in Section 2.
Otherwise, the network affords to accept more flows of theclass B. In this case, it is necessary to consider user
satisfaction of each class. Fig. 6 shows the normalized
mass-user satisfaction focusing on the class B andboxplots of single user satisfaction of each flow classified
into the class B when whiskers span 95 % of all points.
Outliers of boxplots are omitted because degrees of single
user satisfaction of upper 95 % users are considered astargets of evaluations in the results. In this figure, each

IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016

Normalized mass-user satisfaction(all classes)

normalized mass-user satisfaction decreases as the numberof flows increases. Single user satisfaction represented at
the lower end of the whisker also decreases. From theresults, as resource constraints become lighter, network
resources should be further allocated to the class B inorder to improve the total satisfaction of all users as wellas the lower bound of upper 95 % users’ satisfactions.
1.0
0.8
0.6
0.4
0.2
0
100

class C: 50 flowsclass C: 50 flows (with priorities)class C: 200 flowsclass C: 200 flows (with priorities)
150
200
250
Number of flows (class B)

300

Normalized mass-user satisfaction
and distribution of single user
satisfaction (class B)

Fig. 5. Normalized mass-user satisfaction (all classes).
1.0
0.8
0.6
0.4
0.2
Normalized mass-user satisfaction
0
100

150
200
250
Number of flows (class B)

300

Fig. 6. Normalized mass-user satisfaction and distribution of single user
satisfaction (class B).

This situation can be applied to the MVNO network
discussed in Section 2. Moreover, when resource
constraint is light and resource allocation to manageable
application is requested to be prior to others, network
resources should be allocated to manageable applications
as much as possible by using class-based traffic
management. This improves minimum satisfaction of theapplications such as time-dependent video streaming in themassive event.
These resource management guidelines are derived fromthe simulation results of the typical NFV-based network
configuration. The significant point is to clarify the
management purpose of the NFV-based application
specific network and then to maintain a well balance
between the mass-user satisfaction and the normalized
mass-user satisfaction in the network as well as a range oftarget users of applications.

59

5. Conclusion
This paper discussed management characteristics of NFVbased application specific networks and then proposed themass-user satisfaction as one of service-oriented metrics
for them. Network resource management guidelines basedon the mass-user satisfaction were presented throughanalyzing simulation results of a typical application
specific network.
In future extensions of this research, we plan toenhance the proposed mass-user satisfaction byintroducing sophisticated integration of user satisfaction
with various QoE metrics. We also plan to investigate
novel VNF or SDN modules operating with resource
management guidelines of multiple slices.

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Kazunori Ueda received the B.E.
degree in Engineering from Osaka
Prefecture University in 1998 and the
M.E. degree from Osaka University in
2000, respectively. He has been a
Lecturer, Kochi University of Technology,
Kochi, Japan, since 2004. His researchinterests
includecomputercommunication
networks,
wireless
communication networks, and peer-to-peer communication
systems. He is a member of IEEE, IEICE, and IPSJ.
Makoto Iwata
received B.E. and
M.E. degrees in Electronic Engineeringfrom Osaka University, Osaka, Japan, in
1986 and 1988, respectively. He received
a Dr. Eng. degree in Information Systems
Engineering from Osaka University in
1997. Now he is a professor of School ofInformation, Kochi University ofTechnology. He was a visiting professor
of Research Institute of Electrical Communication, Tohoku
University from 2006 to 2009. His research interests are software,architectures, and LSI circuit of novel massively parallel
processing systems. He is a member of IEEE, IEICE, and IPSJ.

Ken-ichi Baba
received the B.E.,
M.E. and D.E. degrees in Information andComputer
Sciencesfrom
Osaka
University, Japan, in 1990, 1992, and1995, respectively. He became a Research
Associate at Osaka University in 1992, anAssistant Professor at the Kochi
University of Technology in 1997, and an
Associate Professor at Osaka University
in 1998. He has been a Professor with the Department ofInformation and Communications Engineering at Kogakuin
University, Tokyo, Japan, since 2014. He has been the Directorof Center for Information Science at Kogakuin University since2015. His research interests include broadband communication
networks, computer communication networks, and photonic
network systems. He is a member of IEEE and IEICE.

Shinji Shimojo
received the M.E. and
Ph.D. degrees from Osaka University in1983 and 1986, respectively. He was an
Assistant Professor with the Department
of Information and Computer Sciences,
Faculty of Engineering Science at Osaka
University from 1986 to 1989, and a
Lecturer with the Large-Size Computer
Center from 1989 to 1991, and an
Associate Professor from 1991 to 1998. During this period, healso worked for a year as a Visiting Researcher at the Universityof California, Irvine. He has been a Professor with the
Cybermedia Center (then the Large-Size Computer Center) at
Osaka University since 1998, and has been the director of theCenter from 2005 to 2008 and from 2015. He was an executive
researcher at National Institute of Information andCommunications Technology and a director of Service Platform
Architecture Research Center (SPARC), JGN2Plus, and Network
Testbed Research and Development Promotion Center. His
current research interests include a wide variety of multimedia
applications, peer-to-peer communication networks, ubiquitous
network systems, and Grid technologies. He was awarded the
Osaka Science Prize in 2005. He is a member of IEEE, IEICE
fellow, and IPSJ fellow.