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Fifth-generation (5G) networks are envisioned to simultaneously support several services with different connectivity requirements. In this respect, service creation time is a key performance indicator (KPI) for service providers when planning the migration to 5G. For example, the European 5G infrastructure public private partnership (5G-PPP) suggests to reduce this time from 90 hours to 90 minutes, in the different phases of the service creation time KPI identified by this organization. This reduction can be achieved by leveraging on 5G state-of-the-art technologies: network function virtualization, network slicing, software-defined networking, and cloud computing, among others. Although some authors and projects have already studied the service creation time KPI in 5G, there is no literature that comprehensively analyzes and presents results related to each phase of this KPI. In this paper, we explore the potential of network function virtualization technologies to reduce service creation time. To this end, we investigate the various phases of the service creation time KPI by designing and implementing, a realistic as well as complex network service that leverages on network function virtualization and related technologies. For our use case, we chose a content delivery network service specifically designed to distribute video. This decision was based on an analysis where we considered several parameters, like the complexity in the phases of design, fulfillment, and service assurance. We dissected all phases of the service creation time KPI required to turn our service blueprint into a deployment by utilizing network function virtualization tools. Henceforth, we defined and conducted several experiments, which were oriented to analyzing the different phases of the service creation time KPI. After analyzing the obtained results, we can conclude that using these new tools permits a substantial reduction in the time taken by each phase of the service creation time KPI.
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Digital Object Identifier 10.1109/ACCESS.2017.DOI
Reducing Service Creation Time
Leveraging on Network Function
Virtualization
WINNIE NAKIMULI1, JAIME GARCIA-REINOSO1,(Member, IEEE), BORJA NOGALES1, IVAN
VIDAL1, DIOGO GOMES2,(Member, IEEE), and DIEGO LOPEZ.3
1Departamento de Ingenieria Telematica, Universidad Carlos III de Madrid, Madrid 28911 Spain
2Instituto de Telecomunicações, Universidade de Aveiro, Aveiro P-3810-193 Portugal
3Telefonica I+D, Distrito Telefonica, Madrid 28050 Spain
Corresponding author: Jaime Garcia-Reinoso (e-mail: jgr@it.uc3m.es).
“This work was supported in part by the European Commission under the European Union’s Horizon 2020 program - grant agreement
number 815074 (5G EVE project).”
ABSTRACT Fifth-generation (5G) networks are envisioned to simultaneously support several services
with different connectivity requirements. In this respect, service creation time is a key performance indicator
(KPI) for service providers when planning the migration to 5G. For example, the European 5G infrastructure
public private partnership (5G-PPP) suggests to reduce this time from 90 hours to 90 minutes, in the
different phases of the service creation time KPI identified by this organization. This reduction can be
achieved by leveraging on 5G state-of-the-art technologies: network function virtualization, network slicing,
software-defined networking, and cloud computing, among others. Although some authors and projects have
already studied the service creation time KPI in 5G, there is no literature that comprehensively analyzes
and presents results related to each phase of this KPI. In this paper, we explore the potential of network
function virtualization technologies to reduce service creation time. To this end, we investigate the various
phases of the service creation time KPI by designing and implementing, a realistic as well as complex
network service that leverages on network function virtualization and related technologies. For our use
case, we chose a content delivery network service specifically designed to distribute video. This decision
was based on an analysis where we considered several parameters, like the complexity in the phases of
design, fulfillment, and service assurance. We dissected all phases of the service creation time KPI required
to turn our service blueprint into a deployment by utilizing network function virtualization tools. Henceforth,
we defined and conducted several experiments, which were oriented to analyzing the different phases of the
service creation time KPI. After analyzing the obtained results, we can conclude that using these new tools
permits a substantial reduction in the time taken by each phase of the service creation time KPI.
INDEX TERMS 5G, MANO, Network Slicing, NFV, service creation time KPI
I. INTRODUCTION
THE fifth-generation (5G) of wireless mobile networks
has been designed to boost usability and provide en-
hanced performance, aiming at supporting new services with
stringent requirements. Due to the heterogeneity of these ser-
vices, they can be classified into three groups [1]: enhanced
mobile broadband (eMBB), massive machine-type commu-
nications (mMTC), and ultra-reliable low-latency commu-
nications (URLLC). The former covers services requiring
a high throughput; mMTC requires low throughput but a
large number of devices connected to the network; whereas
the latter imposes a very low latency in all parts of the
network, including the radio part. To deploy such complex
networks over a shared infrastructure, the next generation
mobile networks (NGMN) alliance defined the concept of
network slicing [2]. With this capability, a 5G mobile net-
work can support the instantiation of specific network slices
by creating virtual instances of the main components of
the 5G architecture. Thus, a given network slice would be
designed to support one of the aforementioned groups of
services. One of the main cornerstones of network slicing
is network functions virtualization (NFV). ETSI NFV has
VOLUME 4, 2016 i
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10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
defined an architectural framework [3] where a management
and orchestration (MANO) block controls the lifecycle of
virtual network functions, as well as the connectivity among
them. This block acts on behalf of the operations/business
support system (OSS/BSS), taking advantage of the avail-
able hardware resources. Telecommunication operators have
shown particular interest in these technologies to boost their
revenues, allowing the deployment of new services on top of
their 5G infrastructure [4].
Several alliances and partnerships have emerged to speed
up the deployment of 5G networks. In Europe, the 5G
infrastructure public private partnership (5G-PPP) was cre-
ated between the European Union and the 5G Infrastruc-
ture Association, integrating the essential 5G stakeholders.
The contract signed by these parties1includes a clause to
monitor the progress of the partnership, specifying a set
of key performance indicators (KPIs) in the areas of busi-
ness, performance and societal KPIs. One of the ambitious
performance KPIs listed in this document is the service
creation time, and 5G-PPP proposes the reduction of this
KPI, i.e., the average service creation time from 90 hours to
90 minutes. According to 5G-PPP, the service creation time
KPI is composed of five phases: phase 0, platform provision;
phase1, onboarding; phase2, instantiation, configuration and
activation; phase3, modification; and phase4, termination. In
traditional networks, service creation requires the installation
and configuration of hardware, potentially from different
vendors, connection and reconnection of physical data links,
and heterogeneous software configuration. All these actions
led to increased delays in making services operational, so
reducing all these times will bring significant benefits to all
the involved actors. Although some previous authors have
studied the 5G service creation time KPI, existing results are
incomplete or inconclusive. Therefore, to the best knowledge
of the authors, we are the first to present a comprehensive
analysis and results regarding all phases of the 5G service
creation time KPI.
Accordingly, this paper aims to explore the potential of
state-of-the-art NFV technologies to effectively reduce the
average service creation time KPI of complex network ser-
vices in all the constituent phases. Thus, it is crucial to
analyze the whole lifecycle of any service, from the design
stage to the fulfillment of the end-to-end (E2E) service,
including service assurance. Subsequently, our study will
leverage a complex network service to dissect the different
phases of the service creation time KPI, suggested by 5G-
PPP. In this respect, we follow a practical approach, using
well-known, widely adopted, open-source technologies to
automatically deploy a real-world multi-site content-delivery
network (CDN) service. This service was selected to show-
case the advantages of using NFV technologies under a real-
istic context, given its intrinsically challenging operational
aspects regarding service design, fulfillment, update, and
assurance. We studied the service creation times that may be
1https://5g-ppp.eu/contract/
achieved in the context of our CDN service implementation,
analyzing the potential of NFV technologies to scale the re-
sources of a network slice and adapt to varying user demands.
The tests have been carried out on a multi-site enabled NFV
platform built in the context of the European H2020 5G EVE
project [5]. Henceforth, the key contributions of this paper
are:
Design of a realistic, moderately complex CDN man-
agement service, composed of network slices, imple-
mented across multiple sites in two different countries,
that leverage on NFV technologies.
Implementation and deployment of the CDN service
using state-of-the-art open-source NFV technologies,
following all the phases involved in the 5G service
creation time KPI.
For each of the phases encompassed in the 5G service
creation time KPI, we comprehensively analyze all the
activities involved in each phase by conducting several
experiments to measure, and quantify the time taken
regarding our CDN management service.
The rest of the paper is organized as follows. Section
II presents a brief discussion of the other works that have
been carried out on the subject matter while pointing out
the major differences with our work. Section III provides a
discussion of the main 5G technologies used in our work;
the tools used to implement these technologies and finally,
a brief introduction to content delivery networks. Section IV
dissects the different phases of the service creation time KPI,
by describing the various experiments designed to analyze
these phases. Section V summarizes the time required in
each phase of the service creation time KPI, presenting a
discussion of the key findings of this paper. Finally, Section
VI concludes with the most important lessons learned in this
work, presenting some future work.
II. RELATED WORK
There have been several 5G-PPP projects whose work has
focused, among other tasks, on measuring the 5G service
creation time KPI, considering numerous use cases. In this
section, we present the definition of the service creation time
KPI in each project, measurement results, and how it relates
to our work. Besides, we provide a summary table indicating
the comparison results.
In the 5G-TRANSFORMER project [6], the service cre-
ation time KPI is defined as the time taken to deploy a
network service. This time is measured from the time a
network service deployment request is sent from the 5G-
TRANSFORMER Vertical Slicer to the time a positive re-
sponse is received from the 5G-TRANSFORMER stack [7].
Accordingly, their service creation time KPI measurements
focus on the instantiation, configuration, and activation
phase. For the KPI measurement, they considered four use-
cases, i.e., Entertainment, E-Health, E-Industry, and mobile
virtual network operator (MVNO). All these use-cases were
orchestrated using the 5G Transformer platform [7], and the
corresponding service creation times have been documented
ii VOLUME 4, 2016
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
in [6]. In comparison, our work also leverages a complex
network service to measure the service creation time KPI
considering four phases, however, i.e., (i) Onboarding of
descriptors, (ii) Instantiation, configuration and activation,
(iii) Modification of the service, and (iv) Service termination.
Moreover, the network service was orchestrated using the
publicly available open-source tool, i.e., ETSI OSM.
The MATILDA project [8] provides some operational KPI
values related to the 5G-PPP service creation time KPI,
i.e., onboarding time, deployment time, and scaling time
which in our case is similar to service modification phase.
They provide values corresponding to these times for dif-
ferent use-cases: (i) Emergency Infrastructure with Service
Level Agreement (SLA) Enforcement (5G PPDR), (ii) High-
Resolution Media on Demand Vertical, with Smart Retail
Venue integration (5GPACE), and Industry 4.0. Hereto, the
use-cases were demonstrated utilizing the MATILDA project
test facilities [9]. However, they do not provide a formal
definition of the service creation time KPI in regards to their
project. Secondly, the methodologies used to measure these
KPI differ across the distinct use-cases. Henceforth, we find
the provided service creation time KPI results inconclusive.
On the other hand, the 5G-MoNArch project [10] evalu-
ated the service creation time KPI utilizing two testbeds, i.e.,
The Smart Sea Port testbed in Hamburg, Germany, and the
Touristic city testbed in Turin, Italy. Each of these testbeds
is capable of supporting different use cases with the aid of
network slicing. In the Hamburg testbed, the service creation
time KPI was measured considering the following phases:
(i) Network Slice instantiation and activation, and (ii) Net-
work Slice re-configuration, which in our case is similar to
service modification. Whereas, in the Touristic city testbed,
the KPI measurement was decomposed into the following
phases: (i) Onboarding, (ii) Instantiation and Activation of
the network slices, and (iii) Network Slice modification. The
measurement results for each phase for the touristic testbed
are documented in [10]. Nevertheless, it is essential to note
that the “Instantiation and Activation” phase does not include
the configuration stage as proposed by 5G-PPP because the
5G-MoNArch project makes use of pre-provisioned virtual
machines (VMs). The 5G-MoNArch virtual infrastructure
manager (VIM) platform [11] always maintains a pool of
pre-provisioned running VMs to reduce on the network slice
deployment time. Consequently, the KPI values presented
for the instantiation and activation phase do not include the
creation and configuration time of the VMs composing the
network slice and, henceforth, incomplete.
Lastly, the 5GTANGO project [12] specifies a perfor-
mance KPI known as “automation KPI”, which is similar
to the 5G-PPP service creation time KPI. The automation
KPI is defined as the time to deploy a network service and
should always be as small as possible. The project uses
three pilots, i.e., Communications suite, Immerse Media, and
Smart Manufacturing. Each of these pilots supports some
use-cases, which are orchestrated by the 5GTANGO service
platform [13]. In [12], they present the automation KPI
results for one of the use cases of the Immersive Media Pilot,
i.e., Single-location scenario, where the camera, end-users,
and the network service components are located in the same
location. The KPI results are split into two phases: Instan-
tiation, configuration and activation time, and Termination
time. Accordingly, the minimum, average, and maximum
values for each of these phases are provided. For the other
two pilots, a qualitative analysis of the Automation KPI is
provided. Although the 5GTANGO project provides some
results related to two of the phases of the service creation
time KPI for one of the pilots, the presented results are
partial. Moreover, in the other two pilots, the provided results
are qualitative.
A summary of the comparison between our work and the
mentioned 5G-PPP projects regarding the provided service
creation time KPI results are provided in Table 1.
III. STATE OF THE ART AND BACKGROUND
In this section, we discuss the leading 5G technologies uti-
lized in our service; the open-source tools used to implement
them and finally, we provide an introduction to content
delivery networks.
A. KEY 5G TECHNOLOGIES
This section presents the main 5G enabling technologies (i.e.,
Network Slicing and ETSI NFV) used in this work.
1) 5G Network Slicing
5G Network Slicing is a networking concept that supports the
creation of complete and independent logical networks on
a shared physical network infrastructure [2]. These logical
networks are composed of virtualized and physical resources
and are designed to satisfy a specific network requirement
[14]. For example, on the same physical network infrastruc-
ture, we can create two or more network slices, for example:
(i) providing low-latency communications to exchange sen-
sors data between vehicles, (ii) provisioning video content
from a CDN to those vehicles, and (iii) offering application
and network security services to other users.
According to [2], the network slicing paradigm is com-
posed of three layers: Resource Layer, Network Slice In-
stance Layer, and the Service Instance Layer, as depicted
in Fig. 1. The Resource Instance layer provides the phys-
ical and virtual network resources (which may be shared
and/or dedicated) to the network slices. At the Network Slice
Instance Layer, these network resources and functions are
combined in diverse ways to form complete and isolated
logical networks that satisfy the network requirements of the
slice-supported services. Lastly, the Service Instance layer
represents the services supported by the network slice, e.g.,
a network slice supporting a CDN service is also capable of
supporting services that require load-balancing mechanisms.
To create and instantiate network slice and network slice
subnet instances according to the use case requirements,
network slice templates (NSTs) are used [15].
VOLUME 4, 2016 iii
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
TABLE 1. Related work - Service creation time KPI results evaluation
Phase 5G-TRANSFORMER MATILDA 5G-MoNArch 5GTANGO OUR WORK
Phase0: Platform provision Not Applicable Not Applicable Not Applicable Not Applicable Not Applicable
Phase1: On-boarding of descriptors 7 3 3 7 3
Phase2: Instantiation, configuration
and activation 3 3
Only instantiation
and activation 3 3
Phase3: Modification of the service 7 3 3 7 3
Phase4: Service Termination 7 7 7 3 3
Final remark on presented results Incomplete Inconclusive Incomplete Incomplete Complete
FIGURE 1. Network Slicing paradigm [2]
2) ETSI NFV
With the impending advent of the fifth generation of mobile
networks or 5G, the softwarization of network functions,
commonly known as network functions virtualization (NFV),
has recently acquired particular relevance, coming to the
forefront in the research and development efforts of relevant
stakeholders in the telecommunications market.
NFV reduces the dependency of network operations on
specific-purpose proprietary hardware devices, with the uti-
lization of network appliances that can be implemented
in software and run in more general-purpose cloud/edge
computing environments. On the one hand, this enables
to alleviate investment and maintenance costs, by a) using
commodity equipment that can be provided attending to
economies of scale; b) supporting the on-demand deployment
of value-added network services in a reduced timeframe, and
c) enabling the flexible allocation of infrastructure resources
to network services and tenants and adapting resource provi-
sioning to varying service demands. On the other hand, the
utilization of NFV technologies reduces the development cy-
cle of new functions and services, which may be prototyped
and tested in production-like environments and facilitates
their installation and evolution in operational environments.
Moreover, it fosters innovation, providing better opportuni-
ties to reach the market of network appliances to telecommu-
nication operators and vendors and other stakeholders in the
sector of information technology and communications, such
FIGURE 2. Relationship between network slices, network services, VNFs and
VDUs
as vertical service providers, software developing companies,
and academia.
Standardization activities on NFV technologies are primar-
ily conducted by the European Telecommunications Stan-
dards Institute (ETSI), through a specific Industry Speci-
fication Group. The ETSI NFV reference framework [16]
specifies the main building blocks and open interfaces that
are needed for a functional NFV deployment, as well as for
the interoperation of different vendor implementations.
In this reference framework, a network service descriptor
(NSD) clearly defines any network service that has to be
deployed using NFV technologies. An NSD is composed
of virtual network functions (VNFs) and the virtual links
connecting these VNFs. The VNFs provide the software
implementation of network functions and service specific-
functionalities utilizing their constituent virtual deployment
units (VDUs). A VDU refers to a virtualized environment
that hosts a network function, and a VNF can be composed
of one or more VDU components. In addition, VNFs are
interconnected to build a network and vertical-specific ser-
vices. The NFV framework uses virtual networks, which are
established as abstractions over the NFVIs. The relationship
between network slices, network services, virtual network
functions, and virtual deployment units is shown in Fig. 2.
VNFs and, in turn, VDUs, are executed over a programmable
substrate of hardware and software resources, which may
be provided by server computers and other heterogeneous
capacity equipment. These types of equipment conform to
what is commonly known as the NFV infrastructure (NFVI),
which supports the instantiation of VNFs with the utilization
of virtualization technologies.
iv VOLUME 4, 2016
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
A management and orchestration (MANO) system coor-
dinates all the operations related to the lifecycle manage-
ment of network and vertical-specific services on the NFV
infrastructures. These include the commission/decommission
of VNFs and their deployment, interconnection, and termina-
tion, using the open interfaces defined by the ETSI reference
architectural framework. To this purpose, the MANO system
is further decomposed into three components: the virtual
infrastructure manager (VIM), which provides the functions
needed to allocate, deallocate and scale the NFVI compute,
storage and network resources to running VNFs, as well as
to monitor the NFVI status; the VNF manager (VNFM), in
charge of managing the VNF lifecycle (i.e., instantiation,
configuration, modification, and termination); and the NFV
orchestrator (NFVO), which coordinates the allocation of
resources interacting with multiple VIMs, as well as the life-
cycle of network and vertical-specific services by interfacing
with different VNFM entities.
B. MANAGEMENT AND NETWORK ORCHESTRATION
COMPONENTS
In this section, we introduce two of the most used solutions to
implement the MANO components presented in subsection
III-A2. For the NFVO and VNFM components, we chose
the ETSI OSM [17] platform, whereas OpenStack [18] was
chosen as the VIM solution. The motivation behind these
choices and the main functionalities used in each platform
(i.e., ETSI OSM and OpenStack) are discussed in the subse-
quent sections III-B1 and III-B2 respectively.
1) ETSI OSM
ETSI Open Source MANO (OSM) is an ETSI initiative
to create an Open Source NFV-MANO framework aligned
with its reference architecture and standards [17]. ETSI
OSM separates virtual resource orchestration from service
orchestration, resulting in a modular framework which can
be extended in several directions without affecting its overall
operation. This modular design principle allows ETSI OSM
to provide an end-to-end network service orchestration func-
tionality through the interaction of the core components of its
architecture.
In order to carry out its modular architectural framework,
ETSI OSM adopted the cloud-native implementation tech-
nique to offer the functionalities encompassed by the MANO
stack as a set of standalone components. These components
have been implemented using the container virtualization
technology (in particular, Dockers containers starting from
release FOUR). This technology allows ETSI OSM to be
flexibly and agilely deployed, reduce resource consumption
to carry out its operation and facilitate the integration of
forthcoming modules that will extend the functionality of-
fered by the software stack.
Within these components comprising the ETSI OSM ar-
chitecture, the module referred to as Resource Orchestra-
tor (RO) plays a fundamental role since it is the block in
charge of managing the allocation of computational resources
(i.e., the compute, network and storage). In turn, these will
accommodate the subsequent execution over an NFVI of
various virtual network functionalities included in a network
service. For this purpose, the RO supports the interactivity
with a large variety of VIM solutions such as OpenVIM [17],
OpenStack [18], VMware Cloud Director [19] and Amazon
Web Services (AWS) [20].
Another major aspect contributed by the RO module to
the ETSI OSM stack is the capacity to deploy multi-site
network services by managing the resource allocation across
several NFVI domains distributed in different geographic
locations and controlled by the diverse types of VIMs. In
this context, the RO also offers a plug-in to coordinate the
operations with a WIM (WAN Infrastructure Manager) to
enable the data exchange between VNFs running in different
NFVI domains, configuring dynamically for that purpose the
intermediate network entities that will allow the inter-site
communications.
Concerning the service orchestration, the entity in charge
of this milestone is closely linked to the RO and is called
the Life Cycle Management (LCM) module. In this case, the
LCM serves the operations related to the lifecycle manage-
ment, handling operations such as the deployment, scaling,
and deletion of network services once the RO has completed
the required resource allocation. This management operation
is included in those services that encompass several net-
work services as an entity, commonly referred to as network
slices by the ETSI OSM community. Thus, the ETSI OSM
software stack provides a functional block aligned with the
ETSI NFVO (see sectionIII-A2) through the interoperability
between the LCM and the RO modules.
On the other hand, the VNF configuration, and abstraction
(VCA) module allows the configuration of the VNFs com-
posing the network services, including both the initialization
of the services to be provided by the VNF (Day-1 config-
uration) and the execution of the defined operations at run-
time (Day-2 configuration). In particular, the VCA module
leverages on Juju [21], which is an open-source application
tool for software modeling, to carry out the configuration
and monitoring tasks associated with the VNFs in OSM.
Furthermore, the VCA module communicates with the LCM
aiming to manage the lifecycle of the VNFs through the
interface provided by the network to the VNF configuration
(N2VC) plugin. Thus, the VCA includes the functionality
associated with the VNFM entity specified within the ETSI-
NFV architecture.
Moreover, the monitoring (MON) module is responsible
for collecting the VNF and VIM metrics specified inside
the VNF descriptor. Inside the VNF descriptor, the metrics
to be monitored by the ETSI OSM platform are specified
both at the VNF and virtual deployment unit (VDU) levels.
For performance management purposes, the MON module
exposes these metrics to a Prometheus module [22], which is
used as a time-series data store for the metrics. Once inside
Prometheus, these metrics can be queried and displayed using
any metric analytic tool capable of retrieving Prometheus
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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
data. In addition, inside the VNF descriptor, it is also possible
to set scaling policies (in/out) for a VDU component of
the VNF. This feature is also known as “Auto-scaling”, in
the ETSI OSM platform, which implies deploying more
instances (i.e., scaling-out), or removing the newly deployed
instances (i.e., scaling-in) of the VDU depending on spe-
cific criteria. For a specific VIM metric being monitored, a
user sets threshold values for which the scaling operations
will be triggered for a VDU component(s) inside the VNF
descriptor. Once the user has set these threshold values,
then the Policy (POL) module of ETSI OSM sets alarms
inside the MON module for these metrics. After that, the
MON module continuously monitors these metrics. Once the
set threshold values have been traversed, the MON module
places a notification on the ETSI OSM Kafka bus [23], which
is used to ensure asynchronous communication between its
different modules. This notification is consumed by the POL
module, which triggers the configured scaling operations for
this scenario, according to the VNF descriptor. These scaling
operations are consumed by the LCM module, which either
scales out or in a given VDU component(s) of the VNF,
depending on the received notification.
As mentioned earlier, in ETSI OSM, network slices are
modeled as a composition of network services, i.e., the
network slice subnets of a network slice are synonymous to
ETSI OSM network services. ETSI OSM offers two modes
of handling network slices: full end-to-end (E2E) manage-
ment (integrated modeling) and standalone management. In
full E2E management, ETSI OSM acts as a slice-manager
and therefore handles the entire lifecycle operations of the
network slice. As the slice manager, ETSI OSM supports
the sharing of the same network service(s) between different
network slices. In this mode, ETSI OSM uses network slice
templates (NSTs) to define the slice requirements, compos-
ite network services, and the virtual links connecting the
network services. Whereas, in the standalone management
mode, the lifecycle management operations of the network
slice are handled by an external agent, and ETSI OSM is only
responsible for orchestrating the network services associated
with the slice.
2) OpenStack
OpenStack is an open-source cloud computing platform ca-
pable of pooling large amounts of virtual resources (i.e.,
compute, storage, and network), building and managing pub-
lic, and private clouds [18]. As a VIM solution, OpenStack
is supported by a large, vibrant community of developers
and end-users, with a new release every six months with
advancements and additional features. Besides, OpenStack
is always accompanied by excellent user documentation for
each release. Moreover, it does not require any specialized
vendor hardware, but rather runs on standard general-purpose
hardware. OpenStack is composed of a number of plug and
play services that can be bundled up in different ways, to
provide customized cloud deployments to OpenStack users.
Henceforth, the main OpenStack services are:
OpenStack Compute: The compute service also known
as “Nova” is the core component of the OpenStack
cloud computing platform, and is therefore used to
provision and control cloud computing systems. Open-
Stack compute service relies on other services to achieve
this functionality: (i) Identity service for authentication
and authorization to access all the required OpenStack
services, (ii) Image service to provision the images to
be used by the compute instances, (iii) Networking
service to provide the physical and virtual networks that
the compute instances connect to, and (iv) Placement
service for resource and resource-usage tracking.
OpenStack Image: The Image service alternatively
known as “Glance” provides a RESTful API that en-
ables an OpenStack user to register, discover, provision
and store virtual machine (VM) images, as well as
to query other data related to the VM images, such
as image metadata. This metadata may include such
information as the image disk and container formats.
OpenStack networking: The networking service alias
Neutron” is used to create, and manage networks
and/or sub-networks. During instance creation, the
OpenStack networking service interacts with the com-
pute service to create network interfaces and provide
the required connectivity to the compute instances.
OpenStack networking provides two networking op-
tions, i.e., pre-created and tenant networks. The former
networks are created beforehand by OpenStack users
inside OpenStack and are used to provide layer-2 (bridg-
ing/switching) connectivity between compute instances
and the physical infrastructure. The latter are private
networks created on-demand by OpenStack users that
augment pre-created networks with layer-3 (routing)
connectivity. Tenant networks can be connected to the
physical network infrastructure with the help of Open-
Stack routers. Furthermore, to create tenant networks,
an OpenStack user does not need to know about the
existing physical network infrastructure, whereas to cre-
ate pre-created networks, an OpenStack user needs in-
depth knowledge about the existing physical network
infrastructure.
OpenStack Telemetry: The telemetry service is com-
posed of metering, monitoring, and alarming services.
In our experiment, we only focused on the metering
service, which is in charge of efficiently polling, col-
lecting, storing, and publishing metering data produced
by OpenStack services. To achieve this functionality,
OpenStack utilizes two software tools; Ceilometer [18]
and Gnocchi [24]. Ceilometer is in-charge of metering
data polling, as well as collection, and subsequently
publishes these data to “Gnocchi ”. Gnocchi provides
time-series storage for OpenStack data in a persistent
and scalable way. In addition, Gnocchi offers resource-
indexing for OpenStack resources.
vi VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
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10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
FIGURE 3. Block diagram of a CDN.
C. INTRODUCTION TO CONTENT DELIVERY
NETWORKS
This section presents a brief introduction to Content Delivery
Networks (CDN) [25]. A CDN arranges a large number of
servers, usually placed close to end-users, facilitating the
reduction of the latency to distribute content, improving the
quality of experience of end-users. Usually, content providers
contract CDN providers to delegate their content, so end-
users of content providers can access such information from
servers managed by CDN providers. Fig. 3 shows the main
building blocks of a complete system, including a content
provider, a CDN provider, and end-users. Focusing on the
blocks of the CDN provider, it is possible to distinguish
three main blocks: (i) Surrogate Servers (SS) offering content
to end-users, (ii) Request/Routing block, which receives re-
quests from end-users and redirects these requests to the most
suitable Surrogate Server, and (iii) the Content Manager,
which manages the distribution of the original content from
the Origin Server, where the content provider has all the
information to be delivered to end-users, to the Surrogate
Servers.
There exist different mechanisms to implement the ser-
vices provided by these blocks. For example, the Re-
quest/Routing block can be implemented using the http redi-
rection mechanism, where web servers redirect part of its
routes to other web servers; URL rewriting, requiring mod-
ifications to the web pages; anycasting, where different web
servers have the same IP address; and a mechanism based
on Domain Name System (DNS) redirection, which is one of
the most used mechanisms to implement this block in CDNs.
Because of the popularity of DNS redirection, this will be
the mechanism used in this work, so a brief introduction is
presented next.
DNS [26] is a service mainly used to map domain names to
IP addresses. Usually, end-users have software DNS clients
used to resolve names to IP addresses, which in turn con-
tact DNS servers. These DNS servers are hierarchically
distributed: after receiving a DNS request, a DNS server
may redirect or forward such request towards another DNS
server, until an authoritative DNS for the requested domain
is reached. This redirection is exploited by the DNS-based
mechanism to implement the Request/Routing block in a
CDN. After receiving a DNS request from an end-user de-
vice, the DNS server of the CDN selects the most appropriate
Surrogate server to handle such requests and replies to the
request using the IP address of that server. After receiving the
reply, the end-user will use that surrogate server to request the
proper content.
IV. EVALUATING THE SERVICE CREATION TIME KPI
This section is devoted to evaluating the service creation
time KPI of a complex service, mainly focusing on a content
delivery network (CDN). In order to analyze the average
service creation time of a service using state-of-the-art NFV
technologies, in this paper we have decided to create a
complex service composed of three main providers offering
different services: (i) a video content provider offering access
to its video catalogue through its web portal to end-users
by means of, (ii) a CDN provider offering a service to
automatically deploy and scale surrogate servers close to end-
users who are connected to the Internet using, and (iii) a
telecommunications operator. For the sake of readability and
without any loss ofs generality, we assume a scenario with
only one telecommunications operator offering access to a
5G network, MANO platforms, and the capacity to handle
network slices.
As shown in Fig. 4, in the design of our service, we
envision a video content provider with clients located both
in Spain and Portugal. To provide an outstanding quality of
experience to their clients, the content provider contracts the
services of a CDN provider who has a pool of surrogate
servers located both in the 5TONIC laboratory2(Spain)
and IT-Aveiro3(Portugal). The telecommunications operator
places these two services in independent slices, to isolate
both services. Henceforth, the overall scenario was designed
and executed using the different tools provided by ETSI OSM
and OpenStack platforms in the 5TONIC laboratory and IT-
Aveiro datacenter.
To better understand the implication of the service creation
time KPI, it is essential to highlight once again all phases
involved in this time, namely: phase 0, platform provision;
phase 1, onboarding of descriptors; phase 2, instantiation,
configuration and activation; phase 3, modification; and,
phase 4, termination [27]. These phases showcase the need
to perform several steps to deploy and manage a service on
a 5G network completely. In the rest of this section, we will
follow the order of these phases to structure all experiments
performed.
A. PLATFORM PROVISION
As already stated in Section I, the platform used in this paper
was built in the context of the European H2020 5G EVE
project, which in turn is based on the infrastructure deployed
25TONIC is an open research and innovation laboratory focusing on 5G
technologies. https://www.5tonic.org/
3Instituto de Telecomunicações in Aveiro, Portugal.
https://www.it.pt/ITSites/Index/3
VOLUME 4, 2016 vii
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
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10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
FIGURE 4. High overview of the CDN management service.
for the H2020 5GinFIRE project [28]. In this section, we dis-
cuss the steps involved in getting this infrastructure platform
ready to provide access to the users of the aforementioned
projects and, in particular, for the experiments defined in this
work.
In order to carry out the experimentation described in the
introduction of this section, here we present an NFV system
mainly constituted by a software MANO stack and three
private cloud platforms, as depicted in Fig. 5. The cloud
platforms represent the NFV infrastructures that enable the
deployment of the designed CDN service for the experimen-
tation procedure. Related to management and orchestration,
ETSI OSM (see section III-B1) is in charge of providing the
MANO implementation and, in particular, our experiments
utilize OSM Release SEVEN [17]. To facilitate the instal-
lation and maintenance of the OSM software stack, it is pro-
vided in a virtual machine within the 5TONIC laboratory and
in compliance with the recommended footprint established
by the OSM community (2 CPUs, 8 GB of RAM, and 40GB
of hard drive).
Besides the OSM stack, the 5TONIC laboratory hosts
two of the aforementioned cloud platforms, both based on
the OpenStack Ocata release (see section III-B2). For the
first platform, two high-profile server computers provide a
computational capacity of 48 vCPUs, 256 GB of RAM, and
8 TB for storage. To manage these resources, the OpenStack
node that fulfills the VIM operations (i.e., the OpenStack
controller) runs in a virtual machine with 4 vCPUS, 16
GB RAM, and 180 GB of storage. This cloud platform
is represented as “5TONIC-site1 DC” in Fig. 5. A second
cloud platform is also included in the 5TONIC laboratory,
encompassing three server computers that account for total
resources of 24 vCPUs, 96 GB RAM, and 6 TB of storage.
Analogously to the previous case, the VIM of this cloud
platform is based on an OpenStack controller, which runs
in a virtual machine with 4 vCPUs and 12 GB RAM, 600
GB of storage. The second cloud platform is referred to as
“5TONIC- site2 DC” in Fig. 5,
The resources of both cloud platforms are exposed to the
OSM stack through their respective VIMs, which are com-
pliant with ETSI OSM Release SEVEN. Besides, the cloud
platforms integrate the OpenStack Telemetry service, sup-
porting the performance metrics collection of the deployed
VNFs. Thus, the experimentation procedures can benefit
from the auto-scaling functionality provided by ETSI OSM.
Moreover, both infrastructures implement the OpenStack
networking service, where both the pre-created and tenant
network options have been configured.
To complete the testbed infrastructure, IT-Aveiro provides
a third cloud platform to the NFV system (shown as “IT-
AVeiro DC” in Fig. 5). The cloud platform features a two-
node deployment based on the OpenStack Train release.
The controller node managing this NFV infrastructure is a
physical server. The compute node is also a physical server
and has the computational capacity of 24 vCPUs, 200GB of
RAM, 1 TB for computing storage, and 1 TB for volume
storage.
On the other hand, it is worth mentioning that the testbed
integrates the required networking capacity that enables the
proper operation of the complete NFV system, supporting
the deployment of multi-site network services over the three
cloud platforms. In this respect, the following networks have
been configured:
1) Infrastructure management networks (i.e., the blue
round dot line in Fig. 5): these networks aim to allow
each VIM to manage the computational resources of
the cloud platform under its control. Therefore, these
networks exist in all the three cloud platforms of the
NFV system.
2) Orchestrator-to-VIM network (i.e., the green long dash
line in Fig. 5): this network is in charge of supporting the
reservation and allocation of the necessary resources for
enabling the subsequent network services deployment.
Furthermore, this network is in charge of the lifecycle
management of the stated network services and slices.
This network supports the communication of the ETSI
OSM stack with each of the VIMs, to support NFV
management and orchestration operations.
3) Orchestrator-to-VNF network (i.e., the solid red line in
Fig. 5): the objective, in this case, is to allow the ETSI
OSM stack to control and monitor the VNFs lifecycle
(i.e., get the VNFs state information and support the
scaling operations based on this information), as well
as to configure the VNFs during their deployment.
4) Service-oriented network (i.e., the purple long dash-dot
line in Fig. 5): this network supports multi-site data
communications among VNFs deployed on different
cloud platforms.
The 5TONIC laboratory provides specific services to sup-
port all the networks mentioned above, which need to be
realized considering the distributed nature of the NFV sys-
tem (the ETSI OSM stack and two cloud platforms are
available in Madrid, whereas another cloud platform is lo-
viii VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
FIGURE 5. Platform infrastructure used to implement and support the CDN
management service.
cated at Aveiro). In particular, a VPN service has been
configured at 5TONIC [28], allowing the communications
with the IT-Aveiro site over secure access with certificate-
based authentication across the Internet. The network path
between 5TONIC and IT-Aveiro is supported by the Spanish
and Portuguese national research and education networks,
RedIRIS and RCTS, respectively, which are interconnected
through the GÉANT pan-European network. To get a better
understanding of the performance provided by this network
path, we have measured the maximum throughput and the
Round Trip Time (RTT) delay on the path. To this purpose,
we have used an open-source traffic scheduler, Trafic [29], to
send two consecutive TCP flows at the maximum possible
rate, one on each direction of the network path. A pair of
TCP flows was scheduled every hour during a period of 10
days. Our results indicate a maximum average throughput
of 66.4Mb/s from 5TONIC to IT-Aveiro, and 61.3Mb/s
in the opposite direction. The average RTT observed by the
TCP flows scheduled in the 5TONIC to IT-Aveiro direction
was 26.56ms, considering a period of one day (24 average
RTT values). The standard deviation was 0.71ms, showing
a reduced dispersion of the average RTT during the day.
These results suggest that the performance of the network
path is appropriate to perform the experimentation activities
considered in this article.
Thus, the outlined testbed is built with an appropriate
amount of hardware resources, allowing the deployment of a
wide range of multi-site network services and slices across
all the platforms described in this section. The design of
this infrastructure requires in-depth knowledge of computer
networks, but its management is not as demanding as the
former stages.
B. ONBOARDING OF DESCRIPTORS
Even though this phase of the service creation time only com-
prises the time required for the onboarding of descriptors, for
the sake of completeness and in order to better understand
the experiment results in next phases, we also include the
previous steps to the onboarding phase where the service has
to be defined. Thus, in the following sub-sections, we will
explain further the following steps: (i) design layout, (ii) for-
mal service preparation, and (iii) the descriptors onboarding
phase.
1) CDN management service design layout
As previously discussed in Section I, in this paper, we plan
to analyze the service creation time of a complex service.
To this end, we plan to provide a service where end-users
contract video streaming services from the video content
provider, who, in turn, contracts a CDN provider to provide
the service to end-users effectively. This way, after an end-
user, selects content from the web portal offered by the video
content provider, the actual request of the video will be
forwarded to the CDN provider, which stores all or part of the
content offered by the video content provider. In our design,
the CDN service will be implemented in two different sites.
The proper site to serve a given request will be selected by the
CDN provider management system, using the Domain-Name
System (DNS) service.
To efficiently and dynamically compose multiple services
on top of a shared infrastructure, our design will leverage
the network slicing functionality provided by ETSI OSM.
The conducted experiment involves two network slices: the
CDN network slice (CDN-NSlice) and the video content
provider network slice (VCP-NSlice) implemented across
multiple sites, as shown in Fig. 6. The CDN network slice
(i.e., the green slice) is used to deliver the video content to
the end-users, whereas the video content provider network
slice (i.e., the pink slice) is the one that provides the end-user
with Internet connectivity. It is essential to highlight that our
infrastructure does not provide the intrinsic characteristics of
network slices yet to support the three groups of services
described in Section I (i.e., eMBB, URLLC and mMTC).
This functionality will be incorporated in the next upgrade of
the platform. In this work, we only test the capacity of ETSI
OSM to compose several network services (possibly shared)
into a network slice to provide a given functionality.
The CDN network slice is composed of three network
services: CDN-NServ-S, offering CDN services to end-users
located in Spain; CDN-NServ-P, offering CDN services to
end-users located in Portugal; and the shared operator NS
(Op-NServ) is used to interconnect the two previous network
services. The CDN-NServ-S provides the CDN service by
utilizing the surrogate servers in Spain (SS-S), while CDN-
NServ-P achieves the same purpose through the aid of the
surrogate servers in Portugal (SS-P). The two CDN network
services (i.e., CDN-NServ-S and CDN-NServ-P) are con-
VOLUME 4, 2016 ix
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10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
FIGURE 6. CDN management using 5G and NFV principles experiment design
nected through the shared operator network service, which
plays the role of the telecommunications operator in our
experiment set up.
Conversely, the video content provider network slice
(VCP-NSlice) consists of two network services: video con-
tent provider network service (VCP-NServ) and the shared
operator network service (OP-NServ). The VCP-NServ is
used to enable the end-user to access the web portal with
all available content. In contrast, the shared operator network
service is used to link the video content provider network
slice to the CDN network slice and the end-users. It suffices
to say, both network slices, VCP-NSlice and CDN-NSlice,
share the operator network service (Op-NServ).
To deploy a realistic environment, the CDN network slice
spans across two locations: one in the 5TONIC laboratory in
Madrid, Spain, the other in the Instituto de Telecomunicações
in Aveiro (IT-Aveiro), Portugal. On the other hand, the video
content provider network slice is also implemented across
two data-centers; however, both data-centers are located in-
side the 5TONIC laboratory. Table 2 presents a summary of
the network slices deployment information, i.e., constituent
network services (NServ(s)), data-centers (DC(s)) and the
location where these data-centers are deployed (DC-loc).
2) Formal service preparation
In this part, we explain the procedures undertaken to pre-
cisely define all services used in this validation, following
the information models specified by ETSI OSM. This phase
aims to prepare a set of descriptors that will be onboarded in
TABLE 2. CDN and video content provider network slices deployment
information
Network Slice NServ DC DC-loc
CDN-NSlice
CDN-NServ-S 5TONIC-site1 Spain
CDN-NServ-P IT-Aveiro Portugal
Op-NServ 5TONIC-site1 Spain
VCP-NSlice Op-NServ 5TONIC-site1 Spain
VCP-NServ 5TONIC-site2 Spain
the following phase, which is the intermediate step between
the design and the operation of the service.
As already explained in section III-B1, ETSI OSM has
defined network slices as a composition of network services
(NServ), which in turn are composed of virtual network
functions (VNF). Similarly, virtual network functions are
composed of one or more virtual deployment units (VDU).
Furthermore, we know that network slices, network services,
and VNFs make use of descriptors in order to deliver the
required customized service.
Accordingly, for each of the composite network services in
the CDN and video content provider network slices presented
in section IV-B1, we introduce the constituent virtual net-
work functions (VNFs) and virtual deployment units (VDUs)
in Table 3. In order to prepare a service, an experiment
developer4has to take the following steps:
Firstly, the developer has to prepare the descriptors for
all the constituents VNFs, i.e., VNF descriptors. These
VNF descriptors describe the compute, storage, and
network resources required to achieve the required VNF.
Secondly, the developer has to prepare and compile
the day-1 & day-2 configurations package for each of
the VNFs included in the service. As mentioned in
section III-B1, in ETSI OSM these VNF configurations
are carried out by Juju through the use of charms; a
collection of configuration files and scripts that are used
to deploy and manage VNFs efficiently and reliably. The
compiled charms are packaged together with the VNF
descriptors inside the VNF packages. Additionally, the
developer has to reference these VNF charms packages
inside the VNF descriptors. The day-1 VNF configu-
rations include actions automatically called when the
VNF is launched for example, bringing up network
interfaces, setting packet routes, and enabling packet
forwarding. In contrast, the day-2 configurations include
actions that are to be called on-demand by the telecom-
munications network operator after the service is al-
ready running for example, triggering scaling actions
and updating a software package.
Thirdly, the developer has to define the network ser-
vice descriptors (NSD) for all the constituent network
4In this case, an experiment developer is someone very familiar with the
ETSI OSM and OpenStack platforms, henceforth an expert at creating VNF
descriptors, network service descriptors, as well as network slice templates.
For inexperienced developers, designing the descriptors can be challenging
at the start.
xVOLUME 4, 2016
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10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
TABLE 3. Network Ser vice(s) constituent VNFs and VDUs in reference to Fig.
6
Network Service Functionality VDU(s)
CDN-NServ-S
Routing CDN Router-S
Load-balancing CDN SS-S - HAProxy
Webserver CDN SS-S - Apache
CDN-NServ-P
Routing CDN Router-P
DNS CDN DNS Server
Webserver CDN SS-P
Op-NServ
Routing Operator Router
DNS Operator DNS server
Client UE
VCP-NServ
Routing VCP Router
DNS VCP DNS server
Webserver Portal WWW server
services. These network service descriptors are used
to define the composite VNFs i.e., VNF descriptors
and their interconnections to implement the required
network service. In addition, they are used to define the
network service configuration information.
Finally, the developer has to prepare the network slice
templates (NSTs) for the constituent network slices.
These NSTs serve to capture the composite network
slice subnets (in this case, network services) and the
virtual links connecting the composite network services.
Network slice templates reference the network service
descriptors, i.e., NSDs composing a slice and their in-
terconnections. They can also contain slice properties,
such as slice type (i.e., eMBB, URLLC, and mMTC)
and quality of service parameters. We have provided all
the descriptors (i.e., VNF and NSD) and the NSTs used
in our work5.
3) Descriptors onboarding phase
After all the descriptors are ready, the experiment developer
has to on-board all these files to the ETSI OSM platform.
Besides, developers have to contact the telecommunication
operator administrator to upload the required VNF images
to the OpenStack image service and ensure that the neces-
sary pre-created networks already exist inside the OpenStack
networking service. However, if the required pre-created
networks were not already present, the telecommunication
operator administrator would have to create them. Once
this was done, the experiment developer could contact the
telecommunications operator to schedule the execution of the
already on-boarded service descriptors.
C. INSTANTIATION, CONFIGURATION AND ACTIVATION
After the service was correctly designed, the next stage is
to instantiate, configure, and activate it. The previous step is
5https://github.com/Winnie-Nakimuli/NFV_Service_Creation_time
new compared to the traditional way of guaranteeing the ful-
fillment of a service. With the integration of the last two steps
in the overall process, it is vital to reduce the service creation
time. We have designed an instantiation, configuration, and
activation experiment to collect results for this phase of the
service creation time, which will be presented next. Care to
note, the ETI OSM platform has to perform the following
steps in sequential order: (i) Creation and configuration of
the requested networks as per the VNF descriptors, NSDs
and NSTs, (ii) Creation and configuration of the VNFs as
per the VNF descriptors, (iii) Interconnection of the VNFs
to form network service(s) as per the NSDs, and (iv) Finally
interconnection of the network services to form the network
slice(s) as per the NST(s).
In this experiment, we tested the amount of time it takes
to launch every network service and, in turn, every network
slice already presented in the previous sections. For these
tests, we considered two network slicing deployment scenar-
ios:
(i) Scenario #1: In this deployment, the CDN network
slice was launched first, followed by the video con-
tent provider network slice. In reference to Table 2,
this implies that the CDN-NServ-S and Op-NServ
are launched simultaneously in 5TONIC-site1, while
the CDN-NServ-P is launched in the IT-Aveiro site.
Once this instantiation is complete, the video con-
tent provider network slice is launched, which in
turn launches the VCP-NServ in 5TONIC-site2 and
connects to the already running shared Op-NServ in
5TONIC-site1. It should be noted that part of the CDN
network slice runs in 5TONIC-site1, whereas the video
content provider network slice runs in 5TONIC-site1
(Op-NServ) and 5TONIC-site2 (VCP-NServ).
(ii) Scenario #2: In this case, the video content provider
network slice was launched first and subsequently the
CDN network slice. With reference to Table 2, this
means that the Op-NServ is launched in 5TONIC-site1,
and in parallel, the VCP-NServ is launched in 5TONIC-
site2. Once this is complete, the CDN network slice
is launched, which simultaneously launches the CDN-
NServ-S in 5TONIC-site1 and the CDN-NServ-P in
IT-AVeiro. It is important to note here, that the video
content provider network slice runs in 5TONIC-site2,
whereas the CDN network slice runs in 5TONIC-site1.
For each scenario, we instantiated each of the network
slices ten times, as the results were stable enough for compar-
ison purposes, as will be shown next. All experiments were
executed under similar conditions, starting with an empty
and clean environment where all available resources were as-
signed to these tests. At each instantiation, the amount of time
it takes to launch each network service and consequently,
each network slice as provided by the ETSI OSM platform
was recorded. This time is computed as the difference from
the time a network slice instantiation request is sent to the
ETSI OSM platform, to the time the network slice is suc-
VOLUME 4, 2016 xi
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10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
FIGURE 7. Network Service(s) and Network Slice Instantiation,configuration, and
activation times for Scenario #1.
FIGURE 8. Network Service(s) and Network Slice Instantiation, configuration and
activation times for Scenario #2.
cessfully deployed and active. Henceforth, the average results
for each deployment scenario #1 and #2 are shown in Fig. 7
and Fig. 8 respectively, all experiments were successfully
deployed.
From Figures 7 and 8, we see that deployment scenario
#2 launches on average much faster than scenario #1. This is
because, in scenario #1, we have up to 6VDUs instantiated
simultaneously in 5TONIC-site1. In contrast, in Scenario
#2, we have only 3VDUs instantiated at once in 5TONIC-
site1, and the other 3VDUs are instantiated in 5TONIC-
site2. Accordingly, network slicing deployment scenario #2
launches much faster than scenario #1.
Therefore, when designing network slices and network
services, it is crucial to account for the number of VDUs to
be instantiated in each data center. Consequently, this will
affect the instantiation, configuration, and activation time of
the network services and, in turn, the network slices.
D. MODIFICATION OF THE SERVICE
For the service modification (or assurance) phase, we have
designed two experiments to show how to manage a running
service and compute the performance in different conditions.
The first experiment explained in IV-D1 is oriented towards
showing that it is still possible to manage all services using
traditional mechanisms. The goal of the second experiment is
to compare the traditional management of the services with
the one using the new tools offered by NFV and especially the
auto-scaling tools implemented by ETSI OSM using the cor-
responding OpenStack functionalities. This last experiment
is described in IV-D2.
After all the services shown in Fig. 6 are completely
instantiated, the first experiment starts with the CDN provider
redirecting all requests to the server located in Portugal. After
receiving requests from clients located in Spain, the CDN ser-
vice is manually6configured to redirect those requests to the
Spanish site, where one surrogate server is already prepared
to receive requests. In the last experiment, we increase the
number of clients in Spain requesting content to the CDN,
which will trigger the auto-scaling function of the ETSI OSM
platform.
1) DNS redirection
In this experiment, we investigated the ability of the CDN
service to redirect end-user requests to the proper surrogate
servers, i.e., the proper web servers designated to service end-
users given the prevailing network traffic demands.
For this test and in reference to Fig. 6, the Operator DNS
server was configured as a forwarder with two forwarding
zones, i.e., cp.com and cdn.com to the VCP and CDN DNS
nameservers respectively. The VCP DNS server has an A
record7entry to redirect DNS requests to the CDN DNS. On
the other hand, the CDN DNS server was initially configured
with an Arecord value to redirect the CDN end-user requests
to the CDN SS-P surrogate server. However, as the number
of user requests kept increasing and the CDN SS-P server
was over-loaded, the CDN DNS server Arecord entry was
modified to redirect those requests to CDN SS-S. This VNF
is capable of scaling out automatically i.e., instantiating more
instances of the Apache VDU (indicated as CDN SS-S -
Apache in Table 3) as the number of requests increases.
In order to test that the DNS request and redirection
function was working, the following steps were taken:
1) Initially, we configured the CDN DNS server with CDN
SS-P as the surrogate server to service end-user re-
quests.
2) Next, we sent up to 20000 http GET requests sequen-
tially from the UE to the CDN service with the domain
name server.cp.cdn.com. The UE is emulated as a VNF
with both Ubuntu 16.04 server edition operating system
and Apache Bench [30] tool installed. This UE is lo-
cated in “5TONIC-site1”, in Madrid, Spain, as indicated
in Fig. 6. This choice of using a VNF as the UE instead
of a physical mobile device was motivated by (i) flex-
ibility in terms of management and configuration and
6For the sake of simplicity, in our experiments, the DNS redirection is
done manually. However, this can be done automatically as in current CDN
deployments.
7An Arecord is the most basic type of DNS record and is used to map a
domain name to an IP address
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W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
FIGURE 9. CDN DNS request and resolution functionality
(ii) superior performance due to the lack of limitations
imposed by network access technologies, among others.
3) Concurrently, we started capturing the http traffic that
was traversing the UE.
4) After approximately 60 seconds, we modified the CDN
DNS server with CDN SS-S as the surrogate server this
time around and reloaded the new DNS configuration.
5) In another terminal on the UE, we flushed the DNS
cache in order to force the UE to do a new DNS
resolution.
6) Henceforth, we continued to run the experiment with the
CDN SS-S as the surrogate server for another approxi-
mately 30 seconds.
At the end of the experiment, we plotted the captured http
request (GET) traffic generated by the UE (in semi-log scale)
from step 3 above against the experiment time and the results
are shown in Fig. 9.
On the one hand, from Fig. 9, we can see that the rate,
i.e., requests/second, handled by CDN SS-P is around 40
requests/second and after approximately 63 seconds, the
rate now handled by CDN SS-S increases (i.e., above 300
requests/second). The rationale behind this increase is due
to the mechanisms implemented by the transmission con-
trol protocol (TCP), where low round-trip times imply high
bandwidth and vice versa. In our experiments, the requests
of CDN SS-P experience much more delay than the requests
handled by CDN SS-S. This is due to CDN SS-P being
instantiated in a data-center (IT-AVeiro, Portugal) that is
distant from the UE, whereas CDN SS-S is located much
closer to the UE (i.e., both are located in 5TONIC, Spain).
These first results show that all services deployed using
Network Function Virtualization tools can be managed using
traditional protocols. In the next experiments, we will show
how auto-scaling improves these benefits.
2) Testing the Auto-scaling Functionality of the CDN Service
In this subsection, we tested our deployment of the ETSI
OSM autoscaling, functionality explained in section III-B1
above inside the CDN service. In order to test this function-
ality, we performed the following steps:
In IV-B2, we developed the CDN SS-S VNF as a
composition of two VDUs, i.e., apache webserver and
Scaling parameter Value
min-instances 0
max-instances 5
scaling-type automatic
monitored parameter CPU utilisation
scale-out threshold 70%
scale-in threshold 10%
threshold time 10 seconds
cool-down time 180 seconds
TABLE 4. CDN SS-S Scaling Policy
load balancer VDUs, reflecting this composition in the
VNF descriptor. For the load balancer VDU, we used
HAProxy as the load balancing scheme of choice [31].
Inside the CDN SS-S VNF descriptor, we configured
the apache webserver VDU to scale automatically ac-
cording to the scaling policy represented in Table 4. The
min-instances and max-instances parameters represent
the minimum and maximum number of instances to
provision when performing scaling in and out opera-
tions. Setting the scaling-type parameter to “automatic”
triggers the scaling operations to start automatically as
long as the threshold values have been met. Inside the
VIM, the CPU utilization parameter is continually being
monitored; on the one hand, once the CPU utilization
value is greater than or equal to 70% (i.e., scale-out
threshold) for more than 10 seconds (i.e., threshold
time), then the scale-out operations are triggered until
the maximum number of instances (in this case 5) has
been reached, however, the ETSI OSM platform has to
wait for 180 seconds (i.e., cool-down time declared in
Table 4) each time before instantiating a new instance.
On the other hand, once the CPU utilization has been
less than or equal than 10% (i.e., scale-in threshold) for
more than 10 seconds (i.e., threshold time), then the
scaling in operations are triggered until the minimum
number of instances (in this case 0) are reached. In
this case, too, the ETSI OSM platform has to wait for
180 seconds (i.e., cool-down time) each time before
removing a scaled instance.
Accordingly, we configured the monitoring parameters
of the CDN SS-S VNF descriptor. These parameters
were set to monitor the CPU load of the Apache web-
server VDU.
Finally, we stress-tested the CDN SS-S server using the
Apache Bench tool, by emulating 100 concurrent UE
clients connecting to the CDN SS-S server, and sending
5.0×106requests in total.
The results of this test are shown in Fig. 10. From Fig. 10,
we can observe a continual decrease in the delay. This is
due to the autoscaling function implemented inside the CDN
SS-S server. To better analyze the impact of autoscaling, we
decided to plot the load distribution of the incoming http
VOLUME 4, 2016 xiii
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W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
requests among the web servers by the HAProxy, represent-
ing the rate per server against time, which is shown Fig. 11.
Using Fig. 10 and Fig. 11, we observed the following:
1) For the first 320 seconds, we have only one instance
of the webserver running that is handling all the end-
user requests. The requests in this phase experience an
average delay of 20 ms, with a standard deviation of 3
seconds.
2) For the next 190 seconds, the apache VDU inside the
CDN SS-S web server has scaled-out to two instances,
and now we have two instances of the apache VDU
running with the HAProxy perfectly balancing the load
between the two instances. Henceforth, we notice a fur-
ther decrease in the delay with the samples experiencing
an average delay of 12 ms, with a standard deviation of
6seconds.
3) For the last 200 seconds, we have three instances of the
webserver running. Hereto, the load balancer balances
the load perfectly between all three instances, and the
result is a much abated average delay of 11 ms with a
standard deviation of 4seconds.
The merits of performing scaling operations using the
Auto-Scaling” feature of ETSI OSM can be summarized as
follows: it enhances the ability of VNFs to quickly adapt to
varying traffic demands by instantiating new VDU instances
of the VNF when monitored parameters go beyond a set max-
imum threshold (i.e., scale-out threshold); removing these
scaled instances when the monitored parameters go below
the minimum threshold (i.e., scale-in threshold). ETSI OSM
platform is capable of performing these scaling operations
automatically without need of manual intervention by con-
tinuously monitoring the VIM and VNF metrics set in the
VNF descriptors.
In our experiment, by configuring the CDN SS-S VNF to
utilize the autoscaling feature, the VNF was able to serve a
higher number of requests, with a much lesser delay. Care
to note, for this experiment, the average delay experienced
by the UE was 14.258ms. It is worth mentioning that, even
the quality of experience perceived by end-users with no
autoscaling is really good in our experiments, with delays
in the order of ms [32], autoscaling would have a great
impact improving the quality of experience in services with
thousands of users.
E. SERVICE TERMINATION
This phase involved the termination of the CDN-NSlice and
the VCP N-Slice network slices of the CDN management
service. The service termination was carried out in two
stages:
Initially, the CDN-NSlice was terminated, which trans-
lated to termination of the CDN-NServ-S and CDN-
NServ-P network services as well as the deletion of
the associated VNFs and networks. However, the Op-
NServ network service was not terminated since it is
shared with the VCP-NSlice network slice. The CDN-
0
10
20
30
40
0e+00 1e+06 2e+06 3e+06 4e+06 5e+06
Number of Requests
Total time [ms]
FIGURE 10. CDN SS-S UE Experienced Delay with Autoscaling
0
2000
4000
6000
0 200 400 600
Time (s)
Requests/s
Server
1
2
3
FIGURE 11. CDN SS-S: HAProxy vdu rate with time
NServ-S termination request was sent to the 5TONIC
laboratory in Spain, whereas the termination request for
the CDN-NServ-P was sent to the IT-Aveiro datacenter
in Portugal.
Secondly, after the CDN-NSlice was terminated, ac-
cordingly, we sent a request to terminate the VCP-
NSlice. This request was translated to the termination
of the Op-NServ and VCP-NServ network services, as
well as the deletion of affiliated VNFs and networks.
Both network service termination requests were sent to
the 5TONIC laboratory in Spain.
Once terminated, all the resources (i.e., network, compute,
and storage) that were initially dedicated to our services are
now freely available to be used by other services.
V. RESULTS AND DISCUSSION
In this section, we present a qualitative and quantitative
analysis of how much effort is required and the respon-
sible parties for each of the phases of the CDN manage-
ment service creation times. In our analysis, we consider
a telecommunications operator with already existing data
centers. OpenStack controls each data center, and ETSI OSM
is in charge of service orchestration and VNFs management
across the entire infrastructure. After analyzing the obtained
results, we discuss the viability to achieve the envisioned
service creation time KPI.
xiv VOLUME 4, 2016
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W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
A. RESULTS
1) Phase 0: Platform provision
Since this phase involves the configuration of the existing
telecommunications network infrastructure to foster the new
service, this phase requires: (i) infrastructure resource reser-
vation in terms of networks, compute and storage for the new
service, and (ii) in case the service providers already have
their own customized VNF images, then these images are
validated and later uploaded to the infrastructure platform by
the OpenStack system administrators. However, if the service
providers do not have any customized VNF images, then
these images are created in the next phase with the help of
both the ETSI OSM and OpenStack system administrators.
Once the infrastructure configuration is done, the platform
is ready to be available for the new service. It suffices to say,
the amount of time taken in this phase is highly dependent on
the new service requirements. However, for most services,
the already provisioned platform is sufficient to service all
end-user requests with minor adjustments. This was the case
for the analysis performed in this paper, where we used an
existing NFV platform that was built to support experimenta-
tion activities in the context of the European H2020 5G EVE
project.
2) Phase 1: On-boarding of descriptors
Considering that this phase encompasses the onboarding of
previously generated descriptor packages to the ETSI OSM
platform by the developer(s) and optionally uploading of
the VNF images to the OpenStack image service by the
OpenStack system administrator, this stage can take a few
minutes to complete.
3) Phase 2: Instantiation, configuration and activation
Taking into account that this phase involves the instantiation,
configuration and activation of the constituent Network slices
and in turn the corresponding network services and VNFs
of the service, the amount of time taken in this phase will
depend on (i) the number of VNFs, Network Services and
Network slices that have to be instantiated and configured,
(ii) the order in which the network slices and in turn the net-
work services and VNFs are instantiated and configured; and
additionally their data center location, and (iii) the amount
of computing (i.e., memory, storage, and CPU cores) and
network resources assigned to the service by the network
operator.
In this paper, as already presented in subsection IV-C,
we considered two scenarios when it came to instantiating,
configuring, and activating our CDN management service.
On the one hand, considering scenario #1, the time taken
for this phase was approximately 7minutes, as illustrated in
Fig. 7. On the other hand, when we instantiated, configured,
and activated our service using scenario #2, the amount of
time taken was less than the time taken in scenario #1,
i.e., approximately 4minutes as displayed in Fig. 8. This
difference was solely due to two factors: (i) the order in
which the network slices and in turn the network services
and VNFs were instantiated and configured, and (ii) the dat-
acenters where this instantiation and configuration occurred.
The former factor cannot be decided by telecommunications
operators, as this depends on the order the different service
providers prefer to instantiate and activate their network
services and slices (for example, a CDN provider might
decide to activate their network slice today, and a month
later, a VCP provider decides to activate their slice, or vice-
versa); the latter factor can be reduced by improving the
telecommunications operator infrastructure.
4) Phase 3: Modification of the service
After the service is activated and handling traffic from the
above phase (see subsection V-A3), then the performance
metrics of the activated network slices and network services
are continuously being monitored for possible modification.
This modification could involve: changing the network slice
and/or the network service configuration(s), manual or auto-
matic triggering of scaling operations, and modifying some
elements of the telecommunications network infrastructure.
In our particular service, as already discussed in sub-
section IV-D, this phase entailed: performing manual DNS
redirection actions and triggering automatic scaling in/out
operations in one of the constituent VNFs of the service. To
manually configure the DNS service to redirect the requests
to the proper surrogate server, takes a few minutes. However,
if this DNS redirection is done automatically as it is currently
the case, then this DNS redirection can be done in the order
of milli-seconds.
On the other hand, since the scaling operations in our
service were set to automatically trigger whenever the per-
formance metric (in this case, the CPU load) went above
or below the set threshold for more than 10 seconds, then
the scaling operations were triggered immediately. However,
after triggering a scaling operation (i.e., in/out), the ETSI
OSM platform was configured to wait for 3minutes and
thereafter, re-evaluate the monitored performance metric be-
fore triggering the next scaling operation. This waiting time
has to be carefully selected depending on the dynamics of the
service under consideration. We have selected 3 minutes for
experimentation purposes only.
5) Phase 4: Service termination
Given that this phase comprises: (i) Termination of the ser-
vice provider dedicated network slices and in turn affiliated
network services and VNFs; plus associated networks, and
(ii) Re-configuration of the shared network services with
other network slices (in case the terminated network slice was
sharing network services with other network slices). Once
complete, the telecommunication operator reclaims back all
the resources, i.e., compute, storage, and network that were
initially allocated to this service provider. Therefore, the
amount of time taken for this phase depends on: (i) the num-
ber of dedicated network slices and in turn network services,
VNFs as well as networks that have to be terminated, and
(ii) the capabilities and location of the data center; where the
VOLUME 4, 2016 xv
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W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
termination request will be sent and finally, on the number
of shared network services that have to be re-configured as a
result.
For this paper, as already presented in subsection IV-E,
the termination of the service was done in two stages, i.e.,
Initially, the CDN-NSlice was terminated, and sequentially,
the VCP-NSlice was also terminated. The termination of the
CDN-NSlice took close to 4minutes, whereas the termina-
tion of the VCP-NSlice took close to 2minutes. The dif-
ference in the termination times is because the CDN-NSlice
contains a network service (i.e., CDN-NServ-P) whose data
center is located in Portugal, so that termination request
experiences a bit more delay compared to the rest of the
network services; whose termination requests were sent to
data centers located inside the 5TONIC laboratory.
A summary of the achieved average results in regards
to our CDN service is presented in Table 5, considering
the different components of the service creation time KPI
phases. As commented in previous sections, our analysis
of the service creation time phases has been done over an
existing NFV platform, built in the context of the European
H2020 5G EVE project. Hence, phase 0 (platform provision)
is not applicable in the provision and management of the
CDN service.
TABLE 5. Exper iments results - Service creation time KPI
Phase Phase Components
Time taken
(minutes)
Phase0:
Platform provision Platform configuration Not
Applicable
Platform deployment
Phase1:
On-boarding of
descriptors
VNF packages 4
Network Service packages 2
Network Slice templates 1
Phase2: Instantiation,
configuration and
activation
Scenario #1 7
Scenario #2 4
Phase3: Modification
of the Service DNS redirection 3
Autoscaling 0.2
Phase4: Service
termination Termination of CDN-NSlice 4
Termination of VCP-NSlice 2
B. DISCUSSION
To analyze the viability of reducing the service creation time
from 90 hours to 90 minutes, as proposed by the 5G-PPP, we
first have to understand the constraints and starting point of a
new service. Because of the lack of a formal definition of ser-
vice creation time, we have used the report done from several
European projects, dissecting this time in several phases [27].
All these different projects have analyzed the service creation
lifecycle from different angles, putting together their results
in various project deliverables as discussed in section II.
On the one hand, if we consider a scenario where the
service creation time besides all five phases, includes service
design and preparation as was the case in our experiment,
then reducing this time to 90 minutes would be more than
challenging. On the other hand, if we consider a scenario
where the platform is already provisioned, which is a reason-
able assumption in our point of view; the service descriptors
are already designed and prepared, or at least there are pre-
defined building blocks to facilitate its composition, then it
would be more than feasible to achieve this service creation
time in 90 minutes or less. Furthermore, initiatives like
the H2020 5G EVE project have defined and implemented
different tools to facilitate the implementation of all phases
involved in the service creation time [33], [34].
VI. CONCLUSION
In this paper, we have presented a comprehensive analysis
of the 5G service creation time KPI phases by utilizing a
complex network service that leverages on NFV technolo-
gies. As earlier mentioned in section I, this time is a Key
Performance Indicator in 5G networks, and the goal is to
reduce current times from 90 hours to 90 minutes. Hence-
forth, we have defined and conducted several experiments to
dissect the time taken by each phase of the service creation
time KPI. From our results, we can conclude that, on the one
hand, some of these phases (for example, platform provision)
require a significant amount of time to be completed, as
well as proper network planning and dimensioning by the
telecommunication network operators. On the other hand,
NFV tools may have an outstanding impact on reducing the
time to create a service in other phases, such as descriptors
onboarding, instantiation, configuration, and management,
as well as service modification. Therefore, in a scenario
where the infrastructure is already provisioned with adequate
resources, and the VNF and descriptors preparations are not
considered part of the service creation KPIs (as is currently
the case), it is possible to reduce the service creation time to
90 minutes or less. These short service creation times will
attract new services to 5G, which will strengthen the position
of telecommunications providers, and all 5G stakeholders in
general.
In future works, we plan to tackle some of the challenges
detected in this study. For example, intent-based networking
and machine learning may be useful in the service design,
instantiation, configuration and activation as well as modifi-
cation phases. Furthermore, we plan to extend ETSI OSM to
include the functionality to update a running network slice
and avoid downtimes when a service has to be modified.
ACKNOWLEDGMENTS
This work was partly funded by the European Commission
under the European Union’s Horizon 2020 program - grant
agreement number 815074 (5G EVE project). The paper
solely reflects the views of the authors. The Commission is
not responsible for the contents of this paper or any use made
xvi VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
thereof. The authors thank Jorge Oliveira for his support
during the realization of this work.
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WINNIE NAKIMULI received the B.S. degree in
Telecommunications Engineering from Makerere
University in Kampala, Uganda in 2011, and the
MSc. degree in Telecommunications Engineering
from the University of Trento, Italy in 2014. She
is currently pursuing her PhD in Telematics En-
gineering at the Univ. Carlos III de Madrid, Spain.
She is involved in the EU H2020 5G-EVE research
project. Her research interests include; 5G Net-
work Slicing, Network Functions Virtualization
(NFV), Software Defined Networking (SDN) and Machine Learning.
JAIME GARCIA-REINOSO (M’04) received the
Telecommunications Engineering degree in 2000
from the University of Vigo, Spain and the Ph.D.
in Telecommunications in 2003 from the Univer-
sity Carlos III of Madrid, Spain. He is currently
a visiting professor at Univ. Carlos III of Madrid
having joined in 2002 and he has published over
60 papers in the field of broadband computer
networks in top magazines and conferences. He
has been involved in several international and na-
tional projects related with broadband access, peer-to-peer overlays, Next
Generation Networks, signalling protocols, information-centric networking,
5G, SDN and NFV like the EU IST MUSE, Trilogy 2, the EU H2020 5G-
Crosshaul, EU H2020 5G-Transformer, 5G-EVE, BioGridNet, MEDIANET
and MASSES, leading the last two projects. He was involved in several 5G-
PPP working groups in the area of 5G like in Spectrum, Vision and Trials.
VOLUME 4, 2016 xvii
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3018583, IEEE Access
W. Nakimuli et al.: Reducing Service Creation Time Leveraging on Network Function Virtualization
BORJA NOGALES received the bachelor’s de-
gree in Telecommunication Technologies Engi-
neering in 2016, from the University Carlos III of
Madrid (UC3M), and he is currently a PhD student
in Telematics Engineering at UC3M. He is in-
volved in the European research project 5GinFIRE
and in the national project 5GCity. His research
interests include Network Functions Virtualization
(NFV), 5G networking, and Unmanned aerial ve-
hicles (UAVs).
IVAN VIDAL received the Telecommunication
Engineering degree in 2001 from the University of
Vigo, and the Ph.D. in Telematics Engineering in
2008 from the University Carlos III of Madrid. He
is currently working as visiting professor at Uni-
versidad Carlos III de Madrid. His research inter-
ests include Unmanned aerial vehicles (UAVs), 5G
networks, multimedia networking, and network
security. He has been involved in several interna-
tional and national research projects, including the
EU IST MUSE, Trilogy, the H2020 5GINFIRE, MEDIANET, and 5GCITY.
He has published more than 50 scientific papers in several conferences and
international journals, lately in the areas of UAVs and network functions
virtualization.
DIOGO GOMES (M’06) graduated in Comput-
ers and Telematics Engineering from the Univer-
sity of Aveiro in 2003 with first-class honors,
and concluded his Phd by the same University
on Resource Optimization for Broadcast Networks
in 2009. He is currently a Professor Auxiliar at
the University of Aveiro. In the last 17 years has
participated in several EU funded projects such
as IST-Mobydick, IST-Daidalos, IST-Akogrimo,
IST-C-MOBILE, ICT-C-Cast, ICT-Onelab2, ICT-
Medieval, H2020-5GinFIRE and currently H2020-5Growth. He has con-
ducted research on QoS, IP Mobility, Multicast/Broadcast, Service & Appli-
cation Development and Network Orchestration. He has always been deeply
involved in the deployment of prototypes and demonstrators.
DIEGO LOPEZ joined Telefonica I+D in 2011
as a Senior Technology Expert, and is currently
in charge of the Technology Exploration activities
within the GCTIO Unit. Before joining Telefonica
he spent some years in the academic sector, dedi-
cated to research on network services, and was ap-
pointed member of the High-Level Expert Group
on Scientific Data Infrastructures by the European
Commission. Diego is currently focused on ap-
plied research applicable to network infrastruc-
tures, with a special emphasis on virtualization, data-enhanced management,
new architectures, and security. Diego chairs the ETSI ISGs on Network
Function Virtualization and Permissioned Distributed Ledgers. Apart from
this, Diego is a more than acceptable Iberian ham carver, and extremely fond
of seeking and enjoying comics, and good discussions on any (in)appropriate
matter. More can be found at https://www.linkedin.com/in/dr2lopez/
xviii VOLUME 4, 2016
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Article
Full-text available
Fifth-generation (5G) and beyond networks are envisioned to provide multi-services with diverse specifications. Network slicing is identified as a key enabling technology to enable 5G networks with multi-services. Network slicing allows a transition from a network-as-an-infrastructure setup to a network-as-a-service to enable numerous 5G smart services with diverse requirements. Although several surveys and tutorials have discussed network slicing in detail, there is no comprehensive study discussing the taxonomy and requirements of network slicing. In this paper, we present and investigate key recent advances of network slicing towards enabling several Internet of Things (IoT) smart applications. A taxonomy is devised for network slicing using different parameters: key design principles, enablers, slicing resources levels, service function chaining schemes, physical infrastructures, and security. Furthermore, we discuss key requirements for network slicing to enable smart services. Finally, we present several open research challenges along with possible guidelines for network slicing.
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Traditional operators deploy and operate telecom networks that rely on monolithic network elements that incorporate distinct network functions and implemented with a vertical integration of control and data planes. This mode of operation is transitioning towards a new situation where the control plane is separated from the data plane, and the network functions are no longer tightly bound to specific elements in the network. The two paradigms pushing in that direction are Software Defined Networking (SDN) and Network Functions Virtualization (NFV). This paper presents a number of challenges that traditional network operators must adapt to during this transition. We categorize these challenges using three important dimensions for telecom operators: operation, organization and business.
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