Conference PaperPDF Available

SENATUS: an experimental SDN/NFV Orchestrator



The fifth generation telecommunication standard (5G) will make use of novel technologies, such as Software Defined Networks (SDN) and Network Function Virtualization (NFV). New models are integrating SDN and NFV in a control plane entity responsible of the Management and Orchestration (MANO) of the whole system. This entity, acting as director of the 5G control plane, is known as Service Orchestrator. This work presents SENATUS, an experimental service orchestrator targeting research and testing environments. SENATUS implements a set of innovations to support the validation of the future network planner modules that will be integrated on the management entities of the 5G architecture. Furthermore, we present two testbed scenarios to show the capability of SENATUS to control the SDN and NFV technologies previously mentioned.
SENATUS: an experimental SDN/NFV
Sebastian Troia(1), Alberto Rodriguez(1), Rodolfo Alvizu(1), Guido Maier(1)
(1) Politecnico di Milano, Dipartimento di Elettronica Informazione e Bioingegneria (DEIB), Italy
AbstractThe fifth generation telecommunication standard
(5G) will make use of novel technologies, such as Software
Defined Networks (SDN) and Network Function Virtualization
(NFV). New models are integrating SDN and NFV in a control
plane entity responsible of the Management and Orchestration
(MANO) of the whole system. This entity, acting as director of
the 5G control plane, is known as Service Orchestrator. This
work presents SENATUS, an experimental service
orchestrator targeting research and testing environments.
SENATUS implements a set of innovations to support the
validation of the future network planner modules that will be
integrated on the management entities of the 5G architecture.
Furthermore, we present two testbed scenarios to show the
capability of SENATUS to control the SDN and NFV
technologies previously mentioned.
KeywordsSoftware Defined Networking, Network Function
Virtualization, OpenFlow, Openstack, Machine Learning
Telecommunication networks have become a critical
infrastructure for any society enabling economic growth and
social prosperity [1]. The demand for data is surging rapidly
every year. In 2021, the global IP traffic will reach 3.3 ZB
(10²¹ bytes!) [2]. Cloud services and Machine-to-Machine
(M2M) applications, capacity hungry applications, and
bandwidth-hungry devices are contributing to the increment
in traffic and data congestion. It is foreseen that the
introduction of new technologies, like 5G, will boost this
growth. This situation is forcing the underlying network
technologies to change, increasing the level of
programmability, control, and flexibility of configuration,
while reducing the overall costs related to network
operations [3]. Contrary to the evolution of previous
generations, 5G will require not only improved networking
solutions but also more sophisticated mechanisms for traffic
differentiation to fulfil stronger end-to-end Quality of
Service (QoS) requirements of numerous different
verticals”, such as automotive, manufacturing, energy,
eHealth, agriculture, etc [4] [5].
This work presents an experimental service orchestrator
(SO) named SENATUS, targeting research environment, as
devised to support testbeds for the development and
validation of network services and network planning
algorithms. It implements functions for managing SDN
networks as well as for deploying network services on NFV
infrastructure. In addition, SENATUS introduces an interface
for horizontal communication that allows interworking with
experimental Machine Learning (ML) modules and
optimization algorithms. SENATUS is destined to provide a
suitable environment for validating service orchestration
functions and modules for network planning tools. The
contribution of this paper can be summarized in four points:
1. Offer service orchestration in an infrastructure
integrating SDN and NFV technologies.
2. Provide means for integrating experimental
modules able to setup network configurations
taking into consideration past and current situation
of the network.
3. Propose testing SDN/NFV environments for
4. Supply a suitable set of metrics and data to perform
performance evaluation.
SENATUS is presented together with two testbed
architectures. The first one has its physical infrastructure
emulated using Mininet and can validate the SDN-based
functionalities, while the second, the main one, has been
deployed using real equipment of BONSAI laboratory at
Politecnico di Milano. The paper is organized as follows:
Section II presents related works. Section III shows the
SENATUS architecture. Section IV introduces two testbed
architectures. Section V presents the validation and
performance results of SENATUS. Finally, a discussion on
open issues and the conclusions are presented in Section VI.
This section will compare SENATUS orchestrator with
other SO in the literature, highlighting similarities and
CORD [6], as one of the first implementations of service
orchestration, introduced some of the basic concepts of the
joint use of SDN and NFV. CORD uses the NFV for fast
and flexible deployment of network services, while SDN
interconnects the equipment and provides end-to-end QoS.
CORD set the foundations for latter architectures, dividing
the service orchestration into what we can consider: a
network manager, an infrastructure manager and a higher
entity (XOS) that takes the role of service Orchestrator.
Similarly, to CORD, SENATUS uses OpenStack to provide
a virtualization layer over the HW, and ONOS gives
network data plane (DP) abstraction.
ADRENALINE [7][8] introduces the hierarchical
approach to multi-domain orchestration that CORD lacked.
It is a testbed designed to be integrated in advanced
scenarios with multiple heterogeneous networks segments,
each one with its own local manager that uses the Control
Orchestration Protocol (COP) on its NBI to communicate
with the multi-domain orchestrators. ADRENALINE is a
powerful environment suitable for testing end-to-end 5G
service orchestration. Statistics and status are pulled from
the NBI of the bottom entities to the SBI of the superior one,
and the actions and new configurations for the network are
pushed from the top. Up to this point, ADRENALINE does
not provide a horizontal communication interface like the
one required by the network planner, that would allow the
introduction of specific purpose modules to enhance the
capabilities of the orchestration system. The lack on this
interface makes it difficult to attach new modules to the
service orchestrators without creating new layers of
abstraction. By contrast, SENATUS introduces some
horizontal functions that allows the delegation of some of
the decision-taking features to external Planner modules,
such as ML routing solution or ML algorithms for VNF
OSM [9] emphasizes multi-domain NFV/SDN
orchestration, being devised to provide a single service
orchestration point for both technologies from where to
encompass the placement and interconnections, trough
virtual overlay networks, of the VNFs forming the NS to be
deployed. Nevertheless, OSM is still an immature project,
and many of its planned features are not implemented yet.
As an example, up to the date, it does not provide specific
control over VNF placement and most of the SDN-related
functions are yet to be integrated. Whereas the SENATUS
orchestrator circumscribes to a less general scenario than
OSM, that is, the control of a single administrative segment;
the SO and the testbeds implemented on this work covert the
full stack from the physical layer to the SO layer and, in
addition, already implement the SDN features and VNF
placement that OSM is still lacking.
ETSO [10] uses specific-purpose modules for VNF
placement and interconnection. It implements the full stack,
and can use several strategies for VNF placement due to
their custom modules. However, despite following the
TOSCA specifications for NS description, it is a self-
contained orchestrator that does not follow the NFV
standard and lacks compatibility to be integrated with
common open solutions and projects.
The limitations reported in this section have been
addressed developing SENATUS.
SENATUS is a service orchestrator designed for research
and validation environments. Its architecture resembles the
structure proposed by the ETSI MANO framework and
takes as reference the modular division used by OSM.
SENATUS orchestrator corresponds to the highest layer of
abstraction in the proposed Control Plane (CP) architecture,
see Fig. 1. The name of SENATUS is borrowed from the
ancient Roman Republic Senate, where administrative
decision, usually taking into account the history, were made
and passed down to the rest of the administrative system. On
this line, we have reproduced this mechanism, combining
the service orchestrator with horizontal modules that have
the task of implementing actions on the underlying network.
The CP architecture in Fig. 1 refers to the full system, which
integrates five different entities:
1. Openstack 1 as Virtual Infrastructure Manager
2. ONOS [11] as SDN controller.
3. SENATUS as the proposed SO.
4. Network Planning Algorithms collects the modules
that provision the network services in the SDN
5. Automatic Algorithms serve as collectors of
historical information needed to run the network
planning algorithms.
Then, the CP is integrated with the Data Plane (DP)
described in Section IV. Internal SENATUS orchestrator
architecture divides its functionalities into two modules:
Service Orchestrator (SO) and the Resource Orchestrator
(RO). SENATUS orchestrator has been entirely
programmed in Python.
A. Resource Orchestrator (RO)
SENATUS uses three sources as metric providers, which
are: Openstack, Gnocchi and ONOS. Openstack has access
to the information status of the system, such as
characteristics of the deployed VNFs. Gnocchi [12] is a
software that provides metric collection and aggregation at
fixed time intervals and allows to define custom metrics
regarding computing network resources. Gnocchi is
especially useful for managing the storage of data coming
from diverse sources, such as the case of distributed
platforms like Openstack. ONOS, as SDN controller, is able
to recover a wide set of metrics from the underlying network
devices and provide them to its native applications and to
external entities using the NBI. The granularity of the
measures (by default 5 seconds) can be set in ONOS
configuration. In contrast to Openstack and Gnocchi, where
data is queried using dedicated API libraries, ONOS
provides the data through REST API. Information from the
REST APIs are transformed by SENATUS RO to JSON
lists or matrices of data for being presented to the rest of the
modules. Some of the information retrieved by the RO is
included on Table 1.
B. Service Orchestrator (SO)
The SO module integrates the high-level control logic
responsible of the Management and Orchestration (MANO).
The set of functions provided by SENATUS SO (some of
them presented in Table 1) often rely on a combination of
data and actions to be taken by Openstack and ONOS. As
1 Weblink:
SENATUS RO abstracts the subjacent layers, the SO
module focus on high level and complex processing
functions not available at lower levels.
C. Network Planning Algorithms
This entity represents one of the main innovations proposed
by this work. The Network Planning Algorithms encloses
the algorithms that are executed to realize the network
services, see Fig.1. This module gives the possibility in the
future to implement different network optimization
algorithms, such as VNF placement and routing
computation. The algorithms defined in this module use the
functions provided by the SO and RO, defined in the Table
1, to carry out their purpose. Network Planning Algorithms
can access a data storage where it is possible to acquire
historical data and therefore be able to train various machine
learning based algorithms. In [13], a machine learning based
algorithm has been implemented exploiting the potential of
SENATUS. The algorithm classifies the traffic matrices in
order to optimize routing in the underlying network
scenarios (see Mininet and BONSAI testbed in section IV).
D. Automatic Algorithms
This entity is used to execute algorithms that collect data
from the network and from servers running VNFs. The
network data acquisition module is responsible for the
continuous collection (every 5 seconds) and processing of
information regarding network traffic exchanged between
nodes and link status over time. This gives the possibility to
the planning algorithms, given the history of the network, to
optimize the distribution of network resources. Furthermore,
it opens the possibility for a network operator to monitor his
network and provide increasingly efficient services.
This section introduces the testbed scenarios that have been
deployed to be used as experimental Data Plane (DP)
working under the CP described. Two dedicated scenarios
have been setup for SENATUS. The first one is devised for
testing the SDN features of the service orchestrator. It does
not have Openstack enabled and only takes advantage of the
SENATUS functions related exclusively to ONOS. The
topology of this first scenario is a virtual and emulated on
Mininet: for this reason, this testbed will be named as
“Mininet testbed” (see Fig. 2). The second environment is a
physical implementation deployed at the Broadband Optical
Networks, Security and Advanced Internet (BONSAI)
laboratory of Politecnico di Milano. This second testbed,
named “BONSAI testbed” (see Fig.2), provides both SDN
and NVF capabilities, taking full use of the SENATUS. We
are dedicating a last subsection to describe the VNFs used
during this work to validate SENATUS.
A. Mininet Testbed
This testbed scenario has been created with a virtual
topology emulated by Mininet [14] and an instance of
ONOS version 1.12.0 Magpie. Mininet is one of the most
widely used network emulators [15]. It can create virtual
networks with operational nodes and allows interaction
among them. One of the key features of Mininet is that it
supports OpenFlow and Software-Defined Network (SDN)
systems, which can be created as easy by running a single
command. Mininet scenario is suitable for simplified testing
of the SDN-related functions developed inside SENATUS.
Using Mininet, the topology replicates 12 switches and 4
virtual hosts. These virtual hosts use the Distributed Internet
Traffic Generator (D-ITG) tool to generate traffic [16]. D-
ITG is a platform capable of producing packet-based traffic,
emulating various stochastic processes for both IDT (Inter
Departure Time) and PS (Packet Size) random variables
(e.g., with Exponential, Uniform, Cauchy, Normal, Pareto,
etc.). D-ITG supports both IPv4 and IPv6 traffic generation
and it is capable to generate traffic at network, transport, and
application layer.
B. BONSAI testbed
BONSAI testbed refers to the physical testing scenario in
BONSAI laboratory at the Politecnico di Milano. This
environment integrates equipment acting as VNF
hypervisors along with a network composed OpenFlow
switches2. OpenStack has been deployed for providing NVF
capabilities, while ONOS is used as SDN controller.
SENATUS is deployed as service orchestrator on the same
computer where Openstack controller is hosted. Two
separate networks are used in BONSAI testbed. Network 1
(red dotted lines in Fig.2) is a traditional IP network used for
the communication exchange between the CP entities.
Network 2 (black lines) is the SDN network composed
by Zodiak FX Openflow switches. A set of
computers, collectively named Compute Nodes, provide
the equipment
2 Weblink:
and the power to run three instances of Openstack compute
nodes, an instance of the Openstack controller with
SENATUS and one with ONOS. A virtual router has been
instantiated through OpenWrt VNF (see section IV.C.3) to
connect both SDN and Management network, providing
external connectivity to internet.
C. VNF deployed
Three VNFs have been used on the validation process of
1) CirrOS. It is a light Linux image typically used for
proof of concepts and testing purposes. This Linux
distribution only requires 13 MB of disk space, 32 MB of
RAM and 1 CPU available to be launched, which makes it
perfect for testing the health of the VIM installation.
2) Ubuntu 16.04. Ubuntu is a free open source Linux
distribution operating system based on Debian. The VNF
image is based on Ubuntu Server, requires around 279 MB
of RAM, 1 CPU.
3) OpenWrt. It is a Linux distribution for embedded
systems, typically used as an open source firmware for
routers. As a proof of concept, we have generated and
configured a OpenWrt VNF with 4 network interfaces and
the necessary software packages to act as a virtual router. It
requires 250 MB of RAM and 1 CPUs from the hypervisor.
In order to validate SENATUS, we have run several
experiments aimed at evaluating:
1. VNF Building time: we measure the time required
to deploy a VNF.
2. VNF migration time: we measure the time for
creating a snapshot image. Then, we calculate the
migration time as (VNF Building time + Snapshot
creation time). We compare the data with results
taken from literature.
3. SENATUS overhead: with this test we estimate the
time taken by SENATUS to perform: a VNF
deployment, a request of VNF status using
Openstack API and a request of VNF metrics using
Gnocchi API. We repeat the same experiments on
the Command Line Interface and compare the
4. ONOS experiments: this test is devised to measure
the performance of SENATUS SDN capabilities on
Mininet testbed.
A. VNF building time
With the first experiment, we measure the building time of a
VNF inside the BONSAI testbed. For this, two of the VNFs
already introduced in IV.C have been used: CirrOS and
Ubuntu 16.04. We use several container flavors, as indicated
by Table 2, to compare the outcoming values. The flavors
are obtained by varying the RAM and the number of core
assigned to the virtual machine container. We observe that
assigning more RAM to the VNF containers the deployment
time improves slowly. This is reasonable, since the
processing of the installation of the VNF image inside the
VM depends on the resources allocated for the VNF.
However, taking into account the minimum size of RAM
required by CirrOS (32 MB) and Ubuntu 16.04 (279 MB),
the impact is minimal, only gaining a second for CirrOS
deployment at the cost of allocating 20 times more RAM.
Flavor VCPU RAM (MB)
VNF instantiation time (s)
Ubuntu 16.04
Deployment of Ubuntu VNF is speed up half a second using
a container four times larger than the minimum size
required. If we compare deployment times for flavors F2
and F6, we notice that allocating an extra CPU does not
have significant impact.
B. Snapshot creation time and migration
Using the results of the VNF building time tests, we aim at
checking the system performance for a theoretical VNF
migration. We perform an offline migration, which involves
two steps: generating the VNF snapshot and deploying the
new VNF on the desired node. In Fig.3, we compared the
migration time using some of the flavors defined in the
previous section with results from literature. These results
include experiments carried out migrating a VNF in a
container equivalent to F4 in two high speed optical
networks at 7 Gbits/s (SAI1) and 3.3 Gbits/s (SAI2)
respectively [17]. We point out that, in contrast to the SAI1
and SAI2 networks, BONSAI testbed provides a maximum
speed of only 100 Mbits/s. Another comparison is taken
from [18] (RBD). Here a 1 Gbits/s switch is used.
Nonetheless, the specifications of the hosts used for the
experiment far exceed the ones from BONSAI testbed.
Finally, a tighter value is derived from [19] (Toronto) for
migration between enterprise clouds of a VNF of Ubuntu
12.04 of 243.6 MB using a flavor comparable to F4.
As we can see in the Fig.3, the time to complete the VNF
migration, as we can infer from the results provided by the
literature, greatly affects the performance of a network
service. Despite the differences in throughput between the
BONSAI testbed and the architectures proposed by the
literature, such as the ones used on SAI1, SAI2 and Toronto,
the results are comparable.
C. SENATUS overhead
Here we present the results from the experiments comparing
the overhead introduced by SENATUS against the use of a
script that exploits the Command Line Interface (CLI),
which is the most common way of using Openstack without
a service orchestrator. Fig 4 shows the time required for the
communications for each API: Openstack and Gnocchi. On
Openstack, we have taken as reference the creation time of a
VNF. Using the flavor F2 and CirrOS VNF of previous
subsection, we measure the time between summoning the
VNF creation and the instant when the VNF start running.
On the second experiment, we measure the time to recover
information from Openstack, specifically, a request of the
status of a VNF. Finally, we check the time required to
recover a set of metrics from Gnocchi. The results above
show that SENATUS is remarkably faster that the usage of
the CLI for summoning actions and collect information.
This is possible due to the direct usage of the Openstack and
Gnocchi libraries, in contrast to the CLI methodology that
must spend some time to convert the terminal input to API
D. ONOS experiment
On this last set of experiments, we present some results
from the SDN-related features of SENATUS. Experiment
have been performed on the Mininet testbed.
ONOS (ms)
OpenDayLight (ms) Ryu (ms)
1.52 1.5 0.3
In addition, as done with VNF migration experiment, we
introduce some results from literature [19]. In particular, in
Table 3 we compare the values of flow installation with
another two commonly used SDN controllers: OpenDaylight
[21] and Ryu3. It is observed that installing a flow from
SENATUS using ONOS has similar performance than the
provided by OpenDaylight, and both of them are much
slower than Ryu. ONOS and OpenDaylight provides a full
network operating system and run and support native
network applications, while Ryu is based on a lighter
approach as a python framework for SDN application
development: thus, a faster response on flow installation
was expected.
In this work, we show a new service orchestrator called
SENATUS. It provides service orchestration for network
segments deploying Openstack as infrastructure manager
and ONOS as SDN controller. SENATUS innovates with
the provisioning of external modules that accomplishes the
task of providing support for decision-taking algorithms for
network planning. Furthermore, SENATUS in conjunction
with ONOS and Openstack implements a rich variety of
metrics that are available for collecting measurements of
different performance of the system. In order to provide the
3 Weblink:
full research ecosystem, SENATUS is presented together
with two testbeds that have been deployed and configured as
part of the architecture being studied.
The work leading to these results has been supported by the
European Community under grant agreement no. 761727
Metro-Haul project.
5GPPP Architecture Working Group. Vision on software networks
and 5g. January 2017.
Cisco Visual networking Index. Forecast and methodology, 2016-
2021, white paper. San Jose, CA, USA, 1, 2016.
5G PPP Architecture Working Group et al. View on 5g architecture.
White Paper, July, 2016.
5GPPP Architecture Working Group. 5g empowering vertical
industries. February 2016.
5GPPP Architecture Working Group. 5g ppp use cases and
performance evaluation models. 2017.
L. Peterson, et al. Central office re-architected as a data center. IEEE
Communications Magazine, 54(10):96101, October 2016.
R. Vilalta, et al. Multi-tenant transport networks with sdn/nfv. In 2015
European Conference on Optical Communication (ECOC), pages 1
Sept 2015.
R. Muñoz, et al. Sdn orchestration and virtualization of heterogeneous
multi-domain and multilayer
transport networks: The strauss
In 2015 IEEE International Black Sea Conference on
Communications and Networking (BlackSeaCom), pages 142146
May 2015.
A Israel, et al. Osm release three-a technical overview. White paper,
ETSI, 2017.
M. Mechtri, et al. Etso: End-to-end sfc orchestration framework. In
2017 IFIP/IEEE Symposium on Integrated Network and Service
Management (IM), pages 903904, May 2017.
P. Berde et al., “ONOS: towards an open, distributed SDN OS”, ACM
HotSDN pp. 1-6 (2014).
S. Troia, et al. "Machine-Learning-Assisted Routing in SDN-based
Optical Networks". Accepted in Eur
opean Conference on Optical
Communication (ECOC), Rome, Italy September 2018.
A. Khalid, et al: A network animator for visualizing real-time packet
flows in mininet.
In Innovations in Clouds, Internet and Networks
(ICIN), 2017 20th Conference on, pages 229231. IEEE, 2017.
A. Botta, et al, "A tool for the generation of realistic network
workload for emerging networking scenarios", Computer Networks
(Elsevier), 2012, Volume 56, Issue 15, pp 3531-3547.
M. I. Biswas, et al. An analysis of live migration in openstack using
high speed optical network.
In 2016 SAI Computing Conference
(SAI), pages 12671272, July 2016.
W. Ding, et al. Construction and performance analysis of unified
storage cloud platform based on openstack with ceph rbd.
In 2018
IEEE 3rd International Conference on Cloud Computing and Big Data
Analysis (ICCCBDA), pages 135141. IEEE, 2018.
D. Kargatzis, et al. Virtual machine migration in heterogeneous
clouds: from openstack to vmware.
In 2017 IEEE 38th Sarnoff
Symposium, pages 16, Sept 2017.
C. Metter, et al. Investigating the impact of network topology on the
processing times of sdn controllers.
In Integrated Network
Management (IM), 2015 IFIP/IEEE International Symposium on
pages 12141219. IEEE, 2015.
M. Jan, et al. "Opendaylight: Towards a model-driven sdn controller
architecture", World of Wireless, Mobile and Multimedia Networks
(WoWMoM), (2014).
... Cheng et al. [31] 2020 × × × × Mekikis et al. [110] 2020 × × × Sharma et al. [111] 2020 Gokhale et al. [112] 2020 × × × Na et al. [113] 2020 × × × × × Zhani and ElBakoury [114] 2020 × × Meshram and Patil [115] 2020 × × X. Wei et al. [116] 2019 × × × × × I. Budhiraja et al. [117] 2019 × × × × Vora et al. [118] 2019 × × × × Ge et al. [68] 2019 × × × × Aggarwal and Kumar [119] 2019 × × Fanibhare et al. [120] 2019 × × × Maier and Ebrahimzadeh [121] 2019 × × × Arshad et al. [122] 2019 × × × Jinke Ren et al. [123] 2019 × × × × × Kim et al. [6] 2018 × × × Grasso and Schembra [49] 2018 × × × × Alextian [124] 2018 × × × × J. Cabrera et al. [125] 2018 × Dmitry et al. [126] 2018 × × × × Y. Xiao et al. [127] 2018 × × × × × Ateya e al. [128] 2018 × × S. Troia et al. [129] 2018 × × × × C. Grasso et al. [49] 2018 × × × × × Li et al. [130] 2018 × × × × M. Gharbaoui et al. [131] 2018 × × × Popovski et al. [47] 2018 × × × Chatras et al. [132] 2017 × × × × Yi-Wei Ma et al. [133] 2017 × × × T. Theodorouan et al. [134] 2017 × × × × × K. Wang et al. [135] 2017 × × × × × Ateya et al. [136] 2017 × × × × Feng et al. [137] 2017 × × × × Pilz et al. [138] 2016 × × × Athmiya et al. [139] 2016 × × × × × Intharawijitr et al. [140] 2016 × × × × Simsek et al. [141] 2016 Maier et al. [8] 2016 Tong et al. [142] 2016 × × × × N. Truong et al. [143] 2015 × × × P. Iovanna et al. [144] 2015 × × × × D. Szabo et al. [145] 2015 × × × F. Bonomi et al. [146] 2011 × × × × × Cuervo et al. [147] 2010 × × × × × Furthermore, at the wireless edge, 5th-generation mobile communication systems are essential to support the TI. A research project has been carried out at the Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) [61] along with the telecommunication industry Next-Generation Mobile Networks (NGMN) alliance [148] regarding the early evaluation of 5G scenarios. ...
... On similar lines, Sebastian Troia et al. [129] presented their work on the SDN/NFV Orchestrator, a new service orchestrator called SENATUS. It facilitates a service orchestration for network segments deploying Openstack as infrastructure manager and Open Network Operating System (ONOS) as SDN controller. ...
Full-text available
The Tactile Internet (TI) is an emerging area of research involving 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the Internet between tactile ends, with audio-visual data as feedback. This emerging TI technology is viewed as the next evolutionary step for the Internet of Things (IoT) and is expected to bring about a massive change in Healthcare 4.0, Industry 4.0 and autonomous vehicles to resolve complicated issues in modern society. This vision of TI makes a dream into a reality. This article aims to provide a comprehensive survey of TI, focussing on design architecture, key application areas, potential enabling technologies, current issues, and challenges to realise it. To illustrate the novelty of our work, we present a brainstorming mind-map of all the topics discussed in this article. We emphasise the design aspects of the TI and discuss the three main sections of the TI, i.e., master, network, and slave sections, with a focus on the proposed application-centric design architecture. With the help of the proposed illustrative diagrams of use cases, we discuss and tabulate the possible applications of the TI with a 5G framework and its requirements. Then, we extensively address the currently identified issues and challenges with promising potential enablers of the TI. Moreover, a comprehensive review focussing on related articles on enabling technologies is explored, including Fifth Generation (5G), Software-Defined Networking (SDN), Network Function Virtualisation (NFV), Cloud/Edge/Fog Computing, Multiple Access, and Network Coding. Finally, we conclude the survey with several research issues that are open for further investigation. Thus, the survey provides insights into the TI that can help network researchers and engineers to contribute further towards developing the next-generation Internet.
... Nowadays many SDN controllers, e.g., Onos, Ryu, OpenDayLight, can obtain a logical view of the network topology. In our testbed Ryu is chosen as SDN controller technology due to its usability and a lighter approach as a python framework for SDN application development: thus, a faster response on flow installation was expected, as confirmed in previous work [60]. In addition, since it is developed in Python, it has many predictors and machine learning libraries readily available. ...
The demand of low latency applications has fostered interest in edge computing, a recent paradigm in which data is processed locally, at the edge of the network. The challenge of delivering services with low-latency and high bandwidth requirements has seen the flourishing of Software-Defined Networking (SDN) solutions that utilize ad-hoc data-driven statistical learning solutions to dynamically steer edge computing resources. In this paper, we propose RoPE, an architecture that adapts the routing strategy of the underlying edge network based on future available bandwidth. The bandwidth prediction method is a policy that we adjust dynamically based on the required time-to-solution and on the available data. An SDN controller keeps track of past link loads and takes a new route if the current path is predicted to be congested. We tested RoPE on different use case applications comparing different well-known prediction policies. Our evaluation results demonstrate that our adaptive solution outperforms other ad-hoc routing solutions and edge-based applications, in turn, benefit from adaptive routing, as long as the prediction is accurate and easy to obtain.
Empowered by sufficient robotics and haptic hardware at the edges of a communication network system, Tactile Internet (TI) is the upcoming transformation that will empower the control of IoT in real-time and facilitate a true paradigm shift in making a dream into a reality. However, one of the most crucial challenges in realising TI is to achieve round trip time (RTT) of 1ms or less. This RTT includes transmission, queuing, processing (operator’s end), and acknowledgement times (controlled end). Another challenge is to achieve ultra-high reliability for establishing haptic communications for TI. This paper proposes a novel fog-based traffic flow framework employing Software-Defined Networking (SDN) and Fog Computing (FC) to address these challenges. An efficient traffic flow algorithm is developed to effectively manage complex and critical traffic in the network to reduce extra processing and waiting times at each level of clouds. The SDN and FC approaches provide an effective solution to route the traffic efficiently in the system. Hence, the traffic flow paths from the master to slave sections are reduced, and consequently, RTT is also reduced. The performance of the proposed system is evaluated by an iFogSim simulator in terms of throughput, RTT, energy consumption, and reliability. The simulation results obtained show that the proposed system outperforms the existing edge, cloud, and cellular networks.
Full-text available
With the advent of 5G technology and an ever-increasing traffic demand, today Communication Service Providers (CSPs) experience a progressive congestion of their networks. The operational complexity, the use of manual configuration, the static nature of current technologies together with fast-changing traffic profiles lead to: inefficient network utilization, over-provisioning of resources and very high Capital Expenditures (CapEx) and Operational Expenses (OpEx). This situation is forcing the CSPs to change their underlying network technologies, and have started to look at new technological solutions that increase the level of programmability, control, and flexibility of configuration, while reducing the overall costs related to network operations. Software Define Networking (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) are accepted as effective solutions to reduce CapEx and OpEx and to boost network innovation. This chapter summarizes the content of my Ph.D. thesis, by presenting new ML-based approaches in order to efficiently optimize resources in 5G metro-core SDN/NFV networks. The main goal is to provide the modern CSP with intelligent and dynamic network optimization tools in order to address the requirements of increasing traffic demand and 5G technology.
Network Function Virtualization enables network slicing as a novel paradigm for service provisioning. With network slicing, Virtual Network Functions (VNFs) can be instantiated at different locations of the infrastructure, choosing their optimal placement based on parameters such as the requirements of the service or the resources available. One limitation of state-of-the-art technology for network slicing is the inability to re-evaluate orchestration decisions once the slice has been deployed, in case of changing service demands or network conditions. In this paper, we present ACHO, a novel software framework that enables seamless re-orchestration of VNFs of any kind, including RAN and Core. With ACHO, VNFs and resources can be easily re-assigned to match, e.g., varying user demands or changes in the nodes’ load. ACHO uses lightweight mechanisms, such as splitting the engine of a VNF from the data it requires to perform its operation, in such a way that, when re-allocating a VNF, only the data is moved (a new engine is instantiated in the new location). We demonstrate the use of ACHO in a small scale testbed, showing that (i) the proposed re-orchestration is feasible, (ii) it results much faster than existing alternatives (especially for relocation), and (iii) the framework can be readily applied to existing VNFs after minimal changes to their implementation.
Conference Paper
Full-text available
We demonstrate how to dynamically place Virtual Network Functions over a software defined optical network integrating IT computing and real IP over WDM resources, thus allowing exchange of real traffic.
Conference Paper
Full-text available
We demonstrate a Machine-Learning-based routing module for software-defined networks. By training with the optimal routing solutions of historical traffic traces, the module can classify traffic matrices to provide real-time routing decisions.
Conference Paper
Full-text available
Adopting a Cloud solution means binding to a specific platform and vendor, with proprietary protocols, standards and tools so usually running into a vendor lock-in. The fear of vendor lock-in is often cited as a major impediment to Cloud service adoption. In this work, we focus on the Virtual Machine (VM) migration between homogeneous and heterogeneous Cloud platforms. We focused on the technical parameters that are essential to be tuned, depending on the various types of virtualization engines used by the Cloud environments. The key difference is the configuration requirements mainly related with image formats and used hypervisors. To demonstrate heterogeneous VM migration we develop a tool that works for OpenStack and VMWare platforms. The experimental analysis demonstrates the effectiveness of our solution with regards to the HTTP response times, especially between the heterogeneous Cloud platforms. The tool achieves approximately two milliseconds response time for HTTP requests to Cloud APIs, excluding the time required to download and upload image files.
Conference Paper
In this demonstration we present MiniNAM, a utility that provides real-time animation of networks created by the Mininet emulator. Mininet is one of the most well-known network emulators in research and academia. Although Mininet is capable of emulating both traditional and software-defined networks, it does not provide a tool to visually observe and monitor the packets flowing over the created network topology. Our utility includes all the components required to initiate, visualize and modify Mininet network flows in real-time. MiniNAM provides a graphical user interface that allows dynamic modification of preferences and packet filters: a user can view selective flows with options to color code packets based on packet type and/or source node. This establishes MiniNAM as a very powerful tool for debugging network protocols or teaching, learning and understanding network concepts. This demonstration illustrates a number of sample use cases and examples of using MiniNAM to create networks and view the generated network flows with customized preferences.
CORD is a new design of a telco central office that replaces closed and proprietary hardware with software running on commodity servers, switches, and access devices. It allows network operators to benefit from both the economies of scale (infrastructure constructed from a few commodity building blocks) and agility (the ability to rapidly deploy and elastically scale services) that commodity cloud providers enjoy today. This article introduces the CORD architecture and describes an open reference implementation of CORD that is available for evaluation.
Conference Paper
We propose the combination of optical network virtualization and NFV for deployment of on-demand OpenFlow-controlled Virtual Optical Networks (VON). Each tenant SDN controller will be run on the Cloud, so the tenant can control the deployed VON.
Conference Paper
Virtualisation technology has become a very common trend in modern datacentres as Virtual Machine (VM) migration brings several benefits like improved performance, high manageability, resource consolidation and fault tolerance. Live Migration (LM) of VMs is used for transferring a working VM from one host to another host of a different physical machine without interfering with the existing VMs. However, little research has been done in considering the real time resource consumption and latency of live VM migration that reduces these benefits to much less than their potential. In this paper, we present an analysis of LM in our unique TransAtlantic high speed optical fibre network connecting Northern Ireland, Dublin and Halifax (Canada). We show that the total migration times as well as total network data transfer for post-copy LM are both dominated by specific VM memory patterns using loaded or unloaded VMs. We also found that the downtime for different VM memory patterns is not extremely varied and no severe effect is experienced over our long distance network.