FLEXIBLE VIRTUAL ROUTING FUNCTION DEPLOYMENT IN NFV-BASED NETWORK WITH MINIMUM TOTAL NETWORK COST

International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.5, September 2016
DOI: 10.5121/ijcnc.2016.8502 15
FLEXIBLE VIRTUAL ROUTING FUNCTION
DEPLOYMENT IN NFV-BASED NETWORK
WITH MINIMUM TOTAL NETWORK COST
Shin-ichiKuribayashi
Department of Computer and Information Science, Seikei University, Japan
ABSTRACT
In a conventional network, most network devices, such as routers, are dedicated devices that do not
have much variation in capacity. In recent years, a new concept of Network Functions
Virtualisation (NFV) has come into use. The intention is to implement a variety of network functions
with software on general-purpose servers and this allows the network operator to select any
capabilities and locations of network functions without any physical constraints.
This paper focuses on the deployment of NFV-based routing functions which are one of critical
virtual network functions, and present the algorithm of virtual routing function allocation that
minimize the total network cost. In addition, this paper presents the useful allocation policy of
virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy
takes the ratio of the cost of a routing function to that of a circuit and traffic distribution in the
network into consideration. Furthermore, this paper shows that there are cases where the use of
NFV-based routing functions makes it possible to reduce the total network cost dramatically, in
comparison to a conventional network, in which it is not economically viable to distribute small-
capacity routing functions.
KEYWORDS
Virtual routing function, NFV, resource allocation, minimum total network cost.
1. INTRODUCTION
In recent years, a new concept of Network Functions Virtualization (NFV) [1]-[3] has
been introduced. The main idea of NFV is the decoupling of physical network equipment
from the functions that run on them. By virtualizing and consolidating network functions
traditionally implemented in dedicated hardware and using cloud technologies, network
operators expect to achieve greater agility and accelerate new service deployments while
driving down both operational and capital costs.
The NFV implements a variety of virtual network functions with software on general-
purpose servers, enabling the network operator to select any capabilities and locations of

16
network functions without any physical constraints. In addition, since NFV makes it easy
to change functions and their locations and capacities dynamically to suit the usage
condition, an economical network can be constructed flexibly. Therefore, the algorithm to
determine the place where network functions are located and how much capacities of
network functions flexibly are required is essential for economical network design.
The rest of this paper is organized as follows. Section 2 explains related works. Section 3
presents points of NFV-based routing function allocation. Section 4 proposes the
algorithm of virtual routing function allocation with minimum total network cost. Section
5 evaluates the proposed algorithm using a ladder-shaped network model, which
simulates the shape of Japan, and identifies the useful function allocation policy. Section
6 presents the conclusions. This paper is an extension of the study presented in [15].
Especially, this paper tries to minimize the total network cost, instead of the total power
consumption.
2. RELATED WORK
Reference [2] summarizes the main differences between the conventional networks and
NFV-based networks as follows:
- Decoupling software from hardware
- Flexible network function deployment
- Dynamic operation
The above features of NFV enable the network operator to select any capacities and
locations of network functions without any physical constraints. Therefore, the efficient
algorithm to determine the place where network functions are located and how much
capacities of network functions are required is essential for economical network design.
The issue of placing network functions would be related to virtual network embedding
(VNE) [4], [5], which is used to allocate physical resources for virtual nodes/links to
construct virtual network. Through dynamic mapping of virtual resources onto physical
hardware, the benefit gained from existing hardware can be maximized. Reference [6]
proposes a virtual network function (VNF) chaining placement that combines location-
routing and VNE problems, solving first the placement and then the chaining. Reference
[7] focuses on a hybrid scenario where services are provided by dedicated physical
hardware and virtualized service instances, and formulates the placement problem as an
Integer Linear Program (ILP) with an objective of allocating a service chain onto the
physical network minimizing the number of servers used. Reference [8] investigates the
influence of virtualizing the S-GW and P-GW functions on the network load and network
delay. It showed differences in performance when the functions were either fully
virtualized and when their data and control planes were separated. The authors showed
that four data centers are required to support a full virtualized gateways deployment for an
exemplary gateway deployment in a US network. Reference [9] formulates the vDPI
(virtual Deep Packet Inspection) placement as a cost minimizing problem, capturing the

17
different constraints the operator is facing, and solves it as an integer linear program
(ILP).
References [10]-[12] propose the joint multiple resource allocation method in a cloud
computing environment that consists of multiple data centers with processing ability and
bandwidth to access them, and demonstrate by simulation evaluations that the proposed
methods can reduce the total resource much, compared with the conventional allocation
method. The proposed resource allocation model is related to the online bin packing
problem with composite bins, in which two bins (processing ability bin and bandwidth
bin) are consolidated.
As for dynamic allocation, Reference [13] evaluates the online virtual function mapping
and scheduling problem and proposes a set of greedy algorithms. Reference [14] proposes
the automatic placement of the virtual nodes and the allocation of network services on
them, supported by a monitoring system that collects and reports on the behavior of the
resources.
Most of these studies mainly try to embed the virtual network functions to physical
resources economically and would be useful for the short-term design of the network, to
use the existing resources efficiently. It is also required to expand the network or to add
new network functions in the future.As for long-term design of the network, this paper
tries to determine the optimal capacity and location of virtual network functions without
any physical constrains. This should be necessary for the network operators to design their
network economically. This paper also tries to show that the use of NFV-based routing
functions makes it possible to reduce the total network cost dramatically, in comparison to
a conventional network.
3. POINTS OF NFV-BASED ROUTING FUNCTION ALLOCATION
In general, routing functions would be allocated either in a centralized approach or in a
distributed approach, as illustrated in Figure 1. There are three areas (#A, #B, #C) and a
circle, ○, indicates virtual routing functions deployed at each area in Figure 1.
Ifa distributed approach is adopted, virtual routing functions are deployed in each area and
intra-area traffic does not need to go through routing functions outside the area concerned.
This results in less cost of a circuit than in a centralized approach. In addition, packet
transfer delay is shorter, thereby enhancing the quality of service. On the other hand,
virtual routing functions aredeployed only in Area #A in a centralized approach, resulting
in less cost of virtual routing functions than in a distributed approach. In a conventional
network, the centralized approach would be an economically viable option because
capacity variations of routers are limited. Particularly, small capacity routers are not
available.In NFV-based network, both centralized and distributed approaches could be
considered as routing functions of any capacities can be allocated at any location
economically. It is necessary to take these factors into consideration in considering the
routing function allocation in an NFV-based network.

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4. ALGORITHM OF VIRTUAL ROUTING FUNCTION ALLOCATION
WITH MINIMUM TOTAL NETWORK COST
4.1. TWO POSSIBLE ALGORITHMS
<Algorithm 1> Initially, allocate a routing function to every area. Then, select the area
with the minimum number of packets sent or received per unit time (referred to as
“integration-origin area”). If integrating the routing function of the selected area with that
of another area (referred to as “integration-destination area”) reduces the total network
cost, integrate them. Repeat this until the total network cost can no longer be reduced or
there is no integration-origin area left.
<Algorithm 2> Initially, allocate a routing function only to the area where the number of
packets sent or received per unit time is the largest. Then, select the area where the number
of packets sent or received per unit time is the second largest. If making the routing
function of the first area handle the routing of the second area reduces the total network
cost, do so. Repeat this until the total network cost can no longer be reduced or there is no
area which can be handled by the routing function of another area.
In this paper, we adopt Algorithm 1 and clarify the detailed allocation algorithm.
Area #B Area #C
：Virtual routing functions ：Terminal or Server
<Centralized> <Distributed>
Router
Area #AArea #A
Area #B Area #C
Router
Router Router
Figure 1. Two main approaches for virtual routing function allocation

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4.2. DETAILED PROCESSING FLOW OF VIRTUAL ROUTING FUNCTION
ALLOCATION
4.2.1. OVERVIEW OF PROCESSING FLOW
The processing flow for determining whether to integrate two areas is described below by
referring to Figure 2.
Step 1. Selection of integration-origin area
Select an area with the least volume of packets sent or received per unit time, so as to
minimize the amount of detour traffic (Area #3 in Figure 2).
Step 2. Selection of integration-destination area
Select all area as integration-destination candidates except integration origin area.
Step 3. Integration
If the total network cost after the integration is smaller than before the integration, the routing
function of the integration-origin area is integrated into that of the integration-destination area
in which the integration results in the minimum total network cost. For example, if
PB1<PB2<PAin Figure 2, the routing function is integrated into the one in Area #1. Note that PA,
PB1 and PB2are total network cost of topology A, B1 and B2 respectively.If the total network
cost after the integration is not smaller than that before the integration, no integration is made.

20
4.2.2 Allocation Policy For Selecting Integration-Source Area In Step 1
Unless the volumes of traffic flows in the two directions between two areas are greatly
unbalanced, integration-origin area candidates are selected in descending order of the distance
from the center of the network, in Step 2. For example, in the ladder-shaped model shown in
Figure 3 (which simulates the shape of Japan and consists of 10 areas), it is desirable to reduce
the total circuit length by integrating areas into area group G3 (which contains Areas #5 and
#6), which is close to the center of the network so that the total network cost can be reduced.
Areas #1, #2, #9, and #10 included in group G1, which is the farthest from the center of the
network, are first selected one by one as integration-origin area candidates. Whether to
integrate them is determined using the algorithm described in Section 4.2.1. When all the
areas in G1 have been examined, Areas #3, #4, #7, and #8 in G2 are selected as integration-
origin area candidates in Figure 3.

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5. EVALUATION OF THE PROPOSED ROUTING FUNCTION
ALLOCATION
5.1 . EVALUATION CONDITIONS
1) Network structure:
The ladder-shaped network illustrated in Figure 3 is used for evaluation. There are 10 areas
and the distance between two adjacent areas in the horizontal direction is 400 km while that in
the vertical direction is 300 km.
2) Traffic Model
It is supposed that there are 110 simultaneously communicating terminals in each area. If it is
assumed that 20% of all terminals are communicating simultaneously, the total number of
terminals in all areas will be about 5000 and this number is representative of that of small-to-
medium enterprises. The terminals are broken down into 11 sets of 10 terminals, and each set
is communicating with a different area. Since it is desirable to consider applications that
require the largest bandwidths and the largest number of packets, video traffic is assumed. The
bandwidth (one way) required for communication by each terminal is set to 1.8 Mbps, and the
packet length is set to 7200 bits. It requires routing capacity of 250 pps (packets/s) per each
communication (one way).In addition, to evaluate unbalanced traffic distribution, it is
assumed that traffic concentrated on the central area (Area #5 in Figure 3). To evaluate the
effect of traffic concentration on Area #5, it is assumed that the volume of traffic between
Area 5 and any of the other areas is M times (referred to as “peak coefficient”) larger than that
between non-Area #5 areas. If M is one, the traffic distribution is uniform.
3) Route selection between areas
The fixed route is selected between areas, as illustrated in Figure 4. Moreover,it is not
considered to select the route dynamically according to the traffic conditions in the network.

22
4) Zc is defined as the ratio of the cost of a routing function to that of a circuit is critical, as in
(1):
Zc=α/β (1)
where α is the cost of routing functions per pps, and β is the cost of a circuit per Mbps per 10
km. The fixed cost of routing function or circuit bandwidth when there is no traffic is
disregarded in this paper.
5) Evaluation tool
We built an evaluation tool written in the C language.
5.2 . RESULTS AND EVALUATION
Evaluation results of the final routing function allocation following the proposed allocation
algorithm in Section 4.2 are shown in Figures 5 and 6. The size of a circle, ○, in an area
indicates the capacity of the routing function. Figure 5 shows how the final function allocation
changes as Zc is varied and M is fixed at 2. Figure 6 shows how the final function allocation
changes as M is varied and Zc is fixed at 3. The following points are clear from these Figures:
1) The greater Zc (cost ratio) is, the greater the cost of the routing function relative to
that of the circuit and the more advantageous to integrate the two areas concerned.In the
example of Figure 5, routing functions are allocated in all areas when Zc≦0.9, in half of all
areas when Zc≒2.9, and only in one area when Zc>3.5.
2) The greater M (peak coefficient) is, the greater the effect of integrating the routing
function of one area into that of another area is. This is explained in detail with Figure 7. Two
areas, #A and #B are considered in Figure 7. If area #A is integrated into area #B, the circuit
capacity that is newly required for intra-area communication in the first area consumes
additional cost, x1, while cost x21 and x22 previously consumed by the routing function
capacity that had been required to relay traffic to another area becomes zero. The greater M is,
the greater x22 becomes (even when x1 remains the same), resulting in an increase in the
number of cases where integration is advantageous (the right part in Figure 7).
To ascertain the above-mentioned trend, we examined how the ratio of the number of areas
that have routing functions to the total number of areas, δ, changes when Zc and M are varied.
The result is shown in Figure 8. The horizontal axis represents Zc while the vertical axis
represents δ. The minimum value of δ is 0.1 while its maximum value is 1.0. As in Figures 5
and 6, the greater Zc is and the greater M is, the more advantageous the integration is.
3) Figure 5 shows that, when Zc is 0.8, the final function allocation is to distribute a
routing function to every area. However, this allocation would not be viable in the
conventional non-NFV network because a routing function needs to have a capacity that is
higher than a certain level, Q. For example, if Q is twice the capacity of the routing function
allocated in the case of Figure 5 (Zc = 0.9), the total network cost of the conventional network

23
(it is the same topology in the case of Zc> 3.6 in Figure 5) increases by 20%. This takes only
the cost of the routing function capacity and the circuit bandwidth into consideration, and does
not include the fixed cost of servers and circuits, which must be taken into consideration in the
conventional network. Therefore, NFV-based routing function allocation could make it
possible to distribute small-capacity routing functions in a network, resulting in a large
reduction in cost in comparison to the conventional network.
Figure 6. Simulation result 2
<Final topology of virtual routing function allocation (Zc=3)>
Area #1 Area #3 Area #5 Area #7 Area #9
Area #2 Area #4 Area #6 Area #8 Area #9
Peak Area
<M=1>
<M=2>
<M=10>
Centralizedapproach
ismoreattractive
Area #10

24
5. CONCLUSION
This paper has proposed the algorithm of virtual routing function allocation in the NFV
network for minimizing the total network cost. The proposed allocation algorithm has been
evaluated with a ladder-shaped network model, which simulates the shape of Japan. It has
been found that, the greater the cost of a routing function relative to that of a circuit is and the
greater the peak coefficient of traffic between areas is, the smaller the total network cost can
be made by integrating the routing functions of some areas into those of a small number of
areas. Furthermore, this paper has indicated that there are cases where the use of NFV-based
routing functions makes it possible to reduce the total network cost much, compared with a
conventional network, in which it is not economically viable to distribute small-capacity
routing functions.
It is required to study the routing function allocation, which considers 1) fixed cost of routing
functions or circuits where there is no traffic, 2) allowable maximum network delay and 3)
dynamic route selection between area. The allocation of other virtual network functions (ex.
DPI, FW) in addition to virtual routing functions should also be studied.
Area #A
<M is small> <M is large>
Area #B Area #A Area #B
Area #A Area #B Area #A Area #B
W:Traffic volume between Area #A and Area #B M*W:Traffic volume between Area #A and Area #B
Figure 7. Comparison of area integration effect
x21
Cost reduction: X21 - X1
x1
Cost reduction: X22 - X1
（Circuit）（Circuit）
（Routing）
X21 ＜＜＜＜X22
＜＜＜＜
x22
（Routing）
x1

25
ACKNOWLEDGMENT
I would like to thank Mr. K.Hidafor their help with the simulation.
REFERENCES
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Figure 8. Ratio δ vs. value of Zc and M

26
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AUTHOR
Shin-ichiKuribayashi received the B.E., M.E., and D.E. degrees from Tohoku University, Japan, in
1978, 1980, and 1988 respectively. He joined NTT Electrical Communications Labs
in 1980. He has been engaged in the design and development of DDX and ISDN
packet switching, ATM, PHS, and IMT 2000 and IP-VPN systems. He researched
distributed communication systems at Stanford University from December 1988
through December 1989. He participated in international standardization on ATM
signaling and IMT2000 signaling protocols at ITU-T SG11 from 1990 through 2000.
Since April 2004, he has been a Professor in the Department of Computer and
Information Science, Faculty of Science and Technology, Seikei University. His research interests
include optimal resource management, QoS control, traffic control for cloud computing environments
and green network. He is a member of IEEE, IEICE and IPSJ.

FLEXIBLE VIRTUAL ROUTING FUNCTION DEPLOYMENT IN NFV-BASED NETWORK WITH MINIMUM TOTAL NETWORK COST

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