SDN/NFV Architecture for IoT Networks
´
Angel Leonardo Valdivieso Caraguay
1
, Patricia Jeanneth Lude
˜
na-Gonz
´
alez
1
,
Rommel Vicente Torres Tandazo
1
and Lorena Isabel Barona L
´
opez
2
1
Department of Electronics and Computer Science (DCCE), Universidad T
´
ecnica Particular de Loja, Loja, Ecuador
2
Department of Informatics and Computer Science (DICC), Escuela Polit
´
ecnica Nacional, Quito, Ecuador
Keywords:
IoT, NFV, SDN, QoS.
Abstract:
Since the appearance of the Internet of Things concept the research community has been focused on how to
allow an efficient communication and data analysis of millions of devices connected to Internet. However,
the current efforts have not enough in order to cover the IoT requirements and challenges. In recent years,
technologies like SDN, NFV, edge computing, among others, have been pushing not only traditional networks
but also the IoT environments. One of the main concerns is to ensure the quality of the services provided by
IoT devices, for its purpose the decouple of data and control plane, the virtualization of network functions,
advanced data analysis capacity and the ability to share and put closer the services are considered promising
characteristics for this kind of environment.This paper presents an architecture that integrates SDN and NFV
focused on IoT environments and a proof of concept to enhance the quality of services. The experiment
takes advantage of the controller capabilities in order to modify QoE/QoS flows in real time by means the
configuration of SDN-App.
1 INTRODUCTION
The growth of devices connected to Internet has
brought uncertainty and concerns about the real capa-
city of current networks. According the Leading IoT
Gartner report (Gartner, 2017), the number of con-
nected devices will exceed the 20 billions by 2020
and thus the industry and the research community
are introducing novel technologies in order to ensure
the correct and efficiently operation of the network.
Software Defined Network (SDN), Network Function
Virtualization (NFV), Self-organized network (SON),
Mobile Edge Network (MEC), cloud and fog compu-
ting, artificial intelligent, among others, are conside-
red key enabled technologies in this totally networked
world.
The Internet of Things (IoT) could take advantage
from the combination of these technologies in order to
provide a context aware environment (Palattella et al.,
2016),(Li et al., 2015). The main current IoT challen-
ges are related to technology convergence, processing
of huge volume data, network programmability, limi-
ted capacity, energy scarcity, context modeling, rea-
soning, ensuring Quality of Services (QoS), security,
trust and privacy, among others (Sheng et al., 2013),
(Perera et al., 2014). The key idea behind NFV is
to share the network infrastructure between different
physical infrastructures/vendors in order to deploy a
traditional network function as a virtual instance. For
its part, SDN separates the data plane (forwarding
tasks) and control plane (decision and control tasks)
of current network devices, allowing the network pro-
grammability. In the IoT context, the synergy bet-
ween SDN and NFV aims to enhance the control and
deployment of IoT devices or sensors in an efficient
and cost-effective way (Bizanis and Kuipers, 2016),
(Barona L
´
opez et al., 2015).
Furthermore, IoT devices not only have a critical
problem in terms of network, computing and storage
capacity but also scalability, data access and complex
analysis requirements (D
´
ıaz et al., 2016). So, IoT
could take advantage from edge and cloud compu-
ting due their facility to provide unlimited resources
(storage, computing and network) closer to the requi-
red service or where the user is located. These resour-
ces can be quickly deployed with a minimal effort or
interaction of the service administrator, establishing
on demand business model.
IoT environments require the processing, corre-
lation and analysis of raw data acquired by differed
devices such as sensors, but because of their limited
resources, these processes must be done outside. In
Caraguay, Á., Ludeña-González, P., Tandazo, R. and López, L.
SDN/NFV Architecture for IoT Networks.
DOI: 10.5220/0007234804250429
In Proceedings of the 14th International Conference on Web Information Systems and Technologies (WEBIST 2018), pages 425-429
ISBN: 978-989-758-324-7
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
425
this context, advanced analysis capabilities and arti-
ficial intelligent can aid to build knowledge based on
the massive data sending by IoT devices. This ap-
proach lets to offer new kind of applications knowing
as Cloud of Things or Everything as a Service (Ca-
valcante et al., 2016). One of the main issues in this
regard is to ensure the quality of the provisioned servi-
ces. From the Quality of Service point of view, SDN
and NFV play an important role in order to ensure the
service level agreements (SLAs) through their elasti-
city to manage and deploying new network functions
or sensors when the QoS levels are decreasing or if
the user needs additional services. This lets not only
enhance the QoS of the provisioned service but also to
provide a better Quality of Experience (QoE) to end
users. Al the same time, the service and telecommu-
nication providers can be benefited by the reduction
of both capital and operational expenditures (CAPEX
and OPEX).
This paper presents an IoT architecture based on
the combination of SDN and NFV with the objective
to realize proofs of concept related to QoS challenges
in this kind of environments. This document is or-
ganized into four sections, being the first of them the
present introduction. Section 2 describes the propo-
sed SDN/NFV Architecture. Section 3 shows the re-
sults of the flow modification and its influence in the
QoS/QoE levels, in real time. Finally the conclusions
are presented in Section 4.
2 SDN/NFV ARCHITECTURE
The global description of the architecture is described
in Figure 1. The proposed architecture follows and in-
tegrates the design principles promoted by ONF (Ong,
2017) and ETSI (Matias et al., 2015). The model is
composed by well-defined four layers: Infrastructure,
Control and Virtualization, Application and Orches-
tration and Management.
The different layers are composed by sublayers
(Figure 2), as follows:
1. Infrastructure Layer. It includes the hardware and
basic software components needed to forwarding
the traffic. In contrast to traditional network de-
vices, IoT devices also originates their own traffic
provided by the internal sensors. In this context, a
dual or hybrid functionality is proposed. IoT no-
des execute traditional forwarding protocols such
as (AODV (Chakeres and Belding-Royer, 2004),
BCHP (Gondaliya and Kathiriya, 2016), DSDV
(Perkins et al., 2001)), among others, depending
of their standard capabilities. If the nodes have
connection with the IoT controller, the nodes ope-
Figure 1: IoT SDN/NFV Architecture.
rate as a data plane waiting for the configuration
provided by control plane. The southbound API
enables the communication between data and con-
trol plane.
2. Control and Virtualization Layer. It provides the
control of the forwarding data behavior and the
virtualized resources for the instantiation of the
NFV Apps. For this purpose, it is composed by
the control plane and virtualized elements. The
control plane has basic control functions. The
Device Manager registers the available nodes that
has connection with the controller and can receive
control plane messages. The unique identification
of the nodes (e.g. IP, MAC address) and other
identification parameters will be available for the
other modules. Similarly, the Topology Manager
uses the information of the Device Manager and
attempts to map the location and connectivity ca-
pabilities of the devices. The Statistics Manager
listens the communication messages between data
and control plane and infers some basic statistics
information (e.g. bytes sent by node links). Furt-
hermore, the Flow Manager registers the active
data flows in the network.
The administrator also has the capability of in-
clude their own control plane modules for spe-
cial purposes. These applications are known as
SDN Apps. For its part, the virtualized resour-
ces (storage, networking and computing), known
as NFV Apps, are available for their download,
installation or configuration in the virtual infra-
structure. These Apps are considered high level
applications.
3. Application Layer. The different NFV applica-
tions are located on this layer. This layer fol-
lows the virtualization principles applied in com-
puter science, where different user applications
ITSCO 2018 - Special Session on Internet of Things and Smart Communities
426
can be executed in different operating systems
(OS) sharing the same hardware resources. In
this context, the different users can share virtua-
lized resources (storage, network and computing)
to execute their applications in a isolated environ-
ment. Examples of NFV Apps include security
apps, QoE, data analysis, among others.
4. Orchestration and Management. The paradigms
of separate data and control planes, network
function softwarization and resources virtualiza-
tion require the coordination of the different lay-
ers of the infrastructure. For this reason, the Or-
chestration and Management layer operates and
has the possibility of take actions on the other
layers of the infrastructure. It is responsible to
ensure the enough resources for the instantiation
and operation of the VNF Apps and Control Apps.
For this purpose, it is composed by three sublay-
ers: VNF Manager, Orchestrator and Virtual In-
frastructure Manager (VIM). The VIM has a clear
and updated view of the installed infrastructure
and the available virtualized resources (storage,
networking, computing). This information is sent
to the Orchestrator.
For its part, the Orchestrator receives the VIM in-
formation and ensures the good operation and iso-
lation of virtualized elements. Moreover, the Or-
chestrator authorizes the instantiation of Virtual
Network Functions (VNF) only if virtual resour-
ces are available. Then, the VNF Manager orga-
nizes and registers the instantiation of available
VNFs. It also receives the NFV instantiation re-
quests from different customers.
Figure 2: IoT SDN/NFV Architecture.
3 RESULTS
In order to demonstrate the feasibility of the propo-
sed framework, the simulation results are focused on
the behavior of Infrastructure and Control and Vir-
tualization Layer. For this purpose, mininet (Lantz
et al., 2010) emulations were applied. Mininet is the
SDN emulator widely adopted by research commu-
nity. Mininet uses python scripts to emulate custom
network topologies and includes virtual hosts, OF-
enabled switches and virtual links. However, the emu-
lation performance in real time applications is depen-
dent on the underlying host system capacity. In this
context, the use of small scale topologies guarantee
the accuracy results and CPU/memory isolation.
The topology is emulated using a server Acer
Swift 3 (Intel i7, 2.7 Ghz, 8GB) running a virtual ma-
chine with Linux Ubuntu 16.04. The topology (Fi-
gure 3) emulates a common data center with core,
aggregation and edge OF-enabled switches (s1-s7).
The switches are connected to the SDN Floodlight
(Floodlight Controller, ) controller. Floodlight pro-
vides a REST-API to request the main SDN services
required to implement SDN-Apps. The present ex-
periment uses the QoS-App (Wallner and Cannistra,
2013), (Wallnerryan, ). Each edge switch (s4-s7) is
connected with two hosts (h1-h8). The virtual hosts
represent the IoT devices and generate video strea-
ming information. The virtual hosts use a VLC server
to stream the video file “highway cif ”(Telecommuni-
cation Networks Group, ) using RTP/UDP. The video
is encoded in MPEG4 (highway cif.mp4) with a size
file of 4.23 MB and a resolution of 352 x 288. The vi-
deo is composed by 2000 frames and a total duration
of 66 seconds.
Figure 3: Test Topology.
The objective of the experiment is to demonstrate
the controller capability to customize the switches be-
havior without affect the normal operation of the net-
work. The experiment uses two streaming flows. The
first streaming (w/o-QoS) is sent from h1 to h8 and
the second stream (w-QoS) sents the video from h2 to
h7. Both videos are simultaneously sent through the
network. During the streaming, after 45 seconds, the
QoS-App is downloaded and installed in the control-
SDN/NFV Architecture for IoT Networks
427
ler. Then, the QoS-App is automatically configured
to control the bandwidth of the links depending of the
flow. The w-QoS streaming (flow from h2 to h7) is
limited with a maximal datarate of 2Mbps and the se-
cond flow w/o-Qos (h1 to h8) is limited to a maximal
datarate of 0.4 Mbps. Then, the received streams are
saved in different video files.
The received files are processed using the Evalvid
Tool (Department of Telecommunication Systems, ),
(Klaue et al., 2003). For this purpose, the files are
decoded as .yuv files and Evalvid evaluates the Peak
Signal to Noise Ratio (PSNR) and the Structural Si-
milarity Index Metric (SSIM) between both, sent and
received streams. Since several processes are execu-
ted simultaneously in the same VMs (mininet, Flood-
light, VLC, Evalvid), slight variations between test
are depicted depending on the CPU and memory re-
sources. For this reason, the Monte-Carlo method is
used. That is, the scenario is tested 20 times and the
corresponding average is evaluated.
The results of the experiments are depicted in the
Figures 4, 5 and 6. With the purpose of reduce distor-
tions, the trend line with an average of 20 frames is
depicted. Figure 4 shows the PSNR average against
the number of frames. The red line (solid) represents
the w-QoS streaming and the blue line (dotted) re-
presents the w/o-QoS. At the beginning of the expe-
riment, both streaming flows have similar behavior
(best effort). For this reason, the average PSNR is
clearly similar between them. The vertical black line
depicts the point at which the QoS-App is downloa-
ded and configured in the Floodlight controller. As
expected, after the QoS-App configuration, the be-
havior of the switches are automatically modified to
identify the flows and assign different QoS policies.
As a result, the h2-h7 traffic shows better levels of
PSNR compared with h1-h8 flow. In this context, the
average w-QoS is 26,54 dB against 23,73 dB of the
w/o-QoS stream.
Figure 4: Peak Signal to Noise Ratio.
The Figure 4 shows a considerable trough in case
plot. The experiments show that the unexpected ef-
fect is mainly caused by the fast moving scenes in this
time. The network load is increased and the PSNR
decreases.
Figure 5: Structural Similarity Index Metric.
For its part, the Figure 5 outlines the SSIM of
the test flows. The average SSIM of w-QoS is 0,814
against the w/o-QoS streaming with 0,738. Finally,
Figure 6 summarizes the above results with the cal-
culation of MOS (Mean Opinion Score). The MOS
(ITU, 1996) attempts to estimate the grade of accepta-
bility of the user (QoE). The scale is proposed as fol-
lows: (5) excellent, (4) good, (3) fair, (2) poor and (1)
bad. Using this background information, the average
MOS during the streaming is calculated. The average
for w/o- QoS is 2,302 against 2,755 of the w-QoS stre-
aming. The obtained results demonstrates the con-
troller capability to dynamically modify the network
behavior and balance the data flows depending of the
user requirements.
Figure 6: Mean Opinion Score (MOS).
4 CONCLUSIONS
The present work analyzes the advantages that SDN
and NFV paradigms introduce in IoT environments.
Moreover, a SDN/NFV architecture for IoT networks
is proposed. In this context, the controller capabi-
lity to dynamically control the network behavior is
ITSCO 2018 - Special Session on Internet of Things and Smart Communities
428
tested. The mininet based test topology and the vi-
deo streaming analysis demonstrate that the floodlight
controller is capable to modify the QoS/QoE of diffe-
rent flows in real time. Therefore, the challenges for
future research include the balance and orchestration
of virtual resources for IoT environments. Similarly,
the optimization of algorithms for real streaming in
SDN/NFV architectures is challenging.
ACKNOWLEDGMENT
The authors are with the Group of Robust, Sustainable
and Secure Networks (SRSNet), Department of Elec-
tronics and Computer Science (DCCE), Universidad
T
´
ecnica Particular de Loja (UTPL), Loja, Ecuador.
REFERENCES
Barona L
´
opez, L. I., Valdivieso Caraguay,
´
A. L., Villalba,
L. J. G., and L
´
opez, D. (2015). Trends on Virtualisa-
tion with Software Defined Networking and Network
Function Virtualisation. IET Networks, 4(5):255–263.
Bizanis, N. and Kuipers, F. A. (2016). SDN and Virtuali-
zation Solutions for the Internet of Things: A Survey.
IEEE Access, 4:5591–5606.
Cavalcante, E., Pereira, J., Alves, M. P., Maia, P., Moura,
R., Batista, T., Delicato, F. C., and Pires, P. F. (2016).
On the Interplay of Internet of Things and Cloud Com-
puting: A Systematic Mapping Study. Computer
Communications, 89:17–33.
Chakeres, I. D. and Belding-Royer, E. M. (2004). AODV
Routing Protocol Implementation Design. In Distri-
buted Computing Systems Workshops, 2004. Procee-
dings. 24th International Conference on, pages 698–
703. IEEE.
Department of Telecommunication Systems. Evalvid. Avai-
lable at http://www.tkn.tu-berlin.de/menue/research/
evalvid.
D
´
ıaz, M., Mart
´
ın, C., and Rubio, B. (2016). State-of-the-
art, Challenges, and Open Issues in the Integration of
Internet of Things and Cloud Computing. Journal of
Network and Computer Applications, 67:99–117.
Floodlight Controller. Open Source Software for
Building Software-Defined Networks. Availa-
ble at http://www.projectfloodlight.org/floodlight
(2018/04/19).
Gartner (2017). Leading IoT Gartner Report.
Gondaliya, N. and Kathiriya, D. (2016). Map Based DTN
Architecture and an Efficient Routing Protocol in De-
lay Tolerant Networks for Post Disaster Situation. In-
ternational Journal of Computer Science and Infor-
mation Security, 14(8):980.
ITU (1996). Methods for Subjective Determination of
Transmission Quality. Recommendation P.800, Inter-
national Telecommunication Union, Geneva.
Klaue, J., Rathke, B., and Wolisz, A. (2003). Evalvid–A
Framework for Video Transmission and Quality Eva-
luation. In International conference on modelling
techniques and tools for computer performance eva-
luation, pages 255–272. Springer.
Lantz, B., Heller, B., and McKeown, N. (2010). A Network
in a Laptop: Rapid Prototyping for Software-defined
Networks. In Proceedings of the 9th ACM SIGCOMM
Workshop on Hot Topics in Networks, page 19. ACM.
Li, S., Da Xu, L., and Zhao, S. (2015). The Internet of
Things: a Survey. Information Systems Frontiers,
17(2):243–259.
Matias, J., Garay, J., Toledo, N., Unzilla, J., and Jacob, E.
(2015). Toward an SDN-enabled NFV Architecture.
IEEE Communications Magazine, 53(4):187–193.
Ong, L. (2017). ONF SDN Architecture and Standards for
Transport Networks: Control Architecture and Net-
work Modeling I. In Optical Fiber Communication
Conference, pages M2H–1. Optical Society of Ame-
rica.
Palattella, M. R., Dohler, M., Grieco, A., Rizzo, G., Tor-
sner, J., Engel, T., and Ladid, L. (2016). Internet of
Things in the 5G era: Enablers, Architecture, and Bu-
siness Models. IEEE Journal on Selected Areas in
Communications, 34(3):510–527.
Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos,
D. (2014). Context Aware Computing for the Internet
of Things: A Survey. IEEE communications surveys
& tutorials, 16(1):414–454.
Perkins, C. E. et al. (2001). Ad Hoc Networking, volume 1.
Addison-wesley Reading.
Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., and
Leung, K. (2013). A survey on the IETF Protocol
Suite for the Internet of Things: Standards, Challen-
ges, and Opportunities. IEEE Wireless Communicati-
ons, 20(6):91–98.
Telecommunication Networks Group. Evalvid Video File.
Available at http://www2.tkn.tu-berlin.de/research/
evalvid/cif/highway.
Wallner, R. and Cannistra, R. (2013). An SDN Appro-
ach: Quality of Service using Big Switchs Floodlight
Open-source Controller. Proceedings of the Asia-
Pacific Advanced Network, 35:14–19.
Wallnerryan. Floodlight with QoS module. Available
at https://github.com/wallnerryan/floodlight-qos-beta
(2018/04/19).
SDN/NFV Architecture for IoT Networks
429