189 research outputs found
Allocation des ressources dans les environnements informatiques en périphérie des réseaux mobiles
Abstract: The evolution of information technology is increasing the diversity of connected devices and leading to the expansion of new application areas. These applications require ultra-low latency, which cannot be achieved by legacy cloud infrastructures given their distance from users. By placing resources closer to users, the recently developed edge computing paradigm aims to meet the needs of these applications. Edge computing is inspired by cloud computing and extends it to the edge of the network, in proximity to where the data is generated. This paradigm leverages the proximity between the processing infrastructure and the users to ensure ultra-low latency and high data throughput. The aim of this thesis is to improve resource allocation at the network edge to provide an improved quality of service and experience for low-latency applications. For better resource allocation, it is necessary to have reliable knowledge about the resources available at any moment. The first contribution of this thesis is to propose a resource representation to allow the supervisory xentity to acquire information about the resources available to each device. This information is then used by the resource allocation scheme to allocate resources appropriately for the different services. The resource allocation scheme is based on Lyapunov optimization, and it is executed only when resource allocation is required, which reduces the latency and resource consumption on each edge device. The second contribution of this thesis focuses on resource allocation for edge services. The services are created by chaining a set of virtual network functions. Resource allocation for services consists of finding an adequate placement for, routing, and scheduling these virtual network functions. We propose a solution based on game theory and machine learning to find a suitable location and routing for as well as an appropriate scheduling of these functions at the network edge. Finding the location and routing of network functions is formulated as a mean field game solved by iterative Ishikawa-Mann learning. In addition, the scheduling of the network functions on the different edge nodes is formulated as a matching set, which is solved using an improved version of the deferred acceleration algorithm we propose. The third contribution of this thesis is the resource allocation for vehicular services at the edge of the network. In this contribution, the services are migrated and moved to the different infrastructures at the edge to ensure service continuity. Vehicular services are particularly delay sensitive and related mainly to road safety and security. Therefore, the migration of vehicular services is a complex operation. We propose an approach based on deep reinforcement learning to proactively migrate the different services while ensuring their continuity under high mobility constraints.L'évolution des technologies de l'information entraîne la prolifération des dispositifs connectés qui mène à l'exploration de nouveaux champs d'application. Ces applications demandent une latence ultra-faible, qui ne peut être atteinte par les infrastructures en nuage traditionnelles étant donné la distance qui les sépare des utilisateurs. En rapprochant les ressources aux utilisateurs, le paradigme de l'informatique en périphérie, récemment apparu, vise à répondre aux besoins de ces applications. L’informatique en périphérie s'inspire de l’informatique en nuage, en l'étendant à la périphérie du réseau, à proximité de l'endroit où les données sont générées. Ce paradigme tire parti de la proximité entre l'infrastructure de traitement et les utilisateurs pour garantir une latence ultra-faible et un débit élevé des données. L'objectif de cette thèse est l'amélioration de l'allocation des ressources à la périphérie du réseau pour offrir une meilleure qualité de service et expérience pour les applications à faible latence. Pour une meilleure allocation des ressources, il est nécessaire d'avoir une bonne connaissance sur les ressources disponibles à tout moment. La première contribution de cette thèse consiste en la proposition d'une représentation des ressources pour permettre à l'entité de supervision d'acquérir des informations sur les ressources disponibles à chaque dispositif. Ces informations sont ensuite exploitées par le schéma d'allocation des ressources afin d'allouer les ressources de manière appropriée pour les différents services. Le schéma d'allocation des ressources est basé sur l'optimisation de Lyapunov, et il n'est exécuté que lorsque l'allocation des ressources est requise, ce qui réduit la latence et la consommation en ressources sur chaque équipement de périphérie. La deuxième contribution de cette thèse porte sur l'allocation des ressources pour les services en périphérie. Les services sont composés par le chaînage d'un ensemble de fonctions réseau virtuelles. L'allocation des ressources pour les services consiste en la recherche d'un placement, d'un routage et d'un ordonnancement adéquat de ces fonctions réseau virtuelles. Nous proposons une solution basée sur la théorie des jeux et sur l'apprentissage automatique pour trouver un emplacement et routage convenable ainsi qu'un ordonnancement approprié de ces fonctions en périphérie du réseau. La troisième contribution de cette thèse consiste en l'allocation des ressources pour les services véhiculaires en périphérie du réseau. Dans cette contribution, les services sont migrés et déplacés sur les différentes infrastructures en périphérie pour assurer la continuité des services. Les services véhiculaires sont en particulier sensibles à la latence et liés principalement à la sûreté et à la sécurité routière. En conséquence, la migration des services véhiculaires constitue une opération complexe. Nous proposons une approche basée sur l'apprentissage par renforcement profond pour migrer de manière proactive les différents services tout en assurant leur continuité sous les contraintes de mobilité élevée
5G Multi-access Edge Computing: Security, Dependability, and Performance
The main innovation of the Fifth Generation (5G) of mobile networks is the
ability to provide novel services with new and stricter requirements. One of
the technologies that enable the new 5G services is the Multi-access Edge
Computing (MEC). MEC is a system composed of multiple devices with computing
and storage capabilities that are deployed at the edge of the network, i.e.,
close to the end users. MEC reduces latency and enables contextual information
and real-time awareness of the local environment. MEC also allows cloud
offloading and the reduction of traffic congestion. Performance is not the only
requirement that the new 5G services have. New mission-critical applications
also require high security and dependability. These three aspects (security,
dependability, and performance) are rarely addressed together. This survey
fills this gap and presents 5G MEC by addressing all these three aspects.
First, we overview the background knowledge on MEC by referring to the current
standardization efforts. Second, we individually present each aspect by
introducing the related taxonomy (important for the not expert on the aspect),
the state of the art, and the challenges on 5G MEC. Finally, we discuss the
challenges of jointly addressing the three aspects.Comment: 33 pages, 11 figures, 15 tables. This paper is under review at IEEE
Communications Surveys & Tutorials. Copyright IEEE 202
Life-cycle management and placement of service function chains in MEC-enabled 5G networks
Recent advancements in mobile communication technology have led to the fifth generation of mobile cellular networks (5G), driven by the proliferation in data traffic demand, stringent latency requirements, and the desire for a fully connected world. This transformation calls for novel technology solutions such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) to satisfy service requirements while providing dynamic and instant service deployment. MEC and NFV are two principal and complementary enablers for 5G networks whose co-existence can lead to numerous benefits. Despite the numerous advantages MEC offers, physical resources at the edge are extremely scarce and require efficient utilization.
In this doctoral dissertation, we first attempt to optimize resource utilization at the network edge for the scenario of live video streaming. We specifically utilize the real-time Radio Access Network (RAN) information available at the MEC servers to develop a machine learning-based prediction solution and anticipate user requests. Consequently, Integer Linear Programming (ILP) models are used to prefetch/cache video contents from a centralized video server.
Regarding the advantages of NFV technology for the deployment of NFs, the second problem that this dissertation address is the proper association of the users to the gNBs along with efficient placement of SFCs on the substrate network. Our primary purpose is to find a proper embedding of the SFC in a hierarchical 5G network. The problem is formulated as a Mixed Integer Linear Programming (MILP) model, having the objective to minimize service provisioning cost, link utilization, and the effect of VNF migration on users' perceived quality of experience.
After rigorously analyzing the proposed SFC placement and considering mobile networks' dynamicity, our next goal is to develop an ILP-based model that minimizes the resource provisioning cost by dynamically embed and scale SFCs so that provisioning cost is minimized while user requirements are met
Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services
Current robotic systems handle a different range of applications such as video surveillance, delivery
of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems
have been embraced not only by the general population but also by the vertical industries to
help them in performing daily activities. Traditionally, the robotic systems have been deployed in
standalone robots that were exclusively dedicated to performing a specific task such as cleaning the
floor in indoor environments. In recent years, cloud providers started to offer their infrastructures
to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed
robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions
are appearing in this form. As a result, standalone robots became software-enhanced objects
with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading
the heavy processing from the robot to the cloud, it is easier to share services and information from
various robots or agents to achieve better cooperation and coordination.
Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities
(e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications
(e.g., remote control, motion planning, autonomous navigation) that can not be executed with
cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots.
While the cloud providers can ensure certain performance in their infrastructure, very little can be
ensured in the network between the robots and the cloud, especially in the last hop where wireless
radio access networks are involved. Over the last years advances in edge computing, fog computing,
5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating
the interest of the industrial sector to satisfy the stringent and real-time requirements of their
applications. Robotic systems are a key piece in the industrial digital transformation and their benefits
are very well studied in the literature. However, designing and implementing a robotic system
that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency,
reliability) is an ambitious task.
This thesis studies the integration of modern Information andCommunication Technologies (ICTs)
in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of
robotic services. To evaluate the performance of the proposed enhancements, this thesis departs
from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge
robotics system serves to represent two exemplary robotic applications. In particular, autonomous
navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic
system is distributed between the robots and the edge server. The open-source prototype implementation
of the designed edge native robotic system resulted in the creation of two real-world testbeds
that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in
robotic systems.
After detailing the design and prototype implementation of the end-to-end edge native robotic
system, this thesis proposes several enhancements that can be offered to robotic systems by adapting
the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it
proposes exemplary network context-aware enhancements in which the real-time information about
robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to
the actual status of the communication (e.g., radio channel). Three different exemplary context-aware
enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later,
the thesis studies the capability of the edge native robotic system to offer potential savings by means of
computation offloading for robot manipulators in different deployment configurations. Further, the
impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a
robot manipulator and its remote controller are assessed.
In the following part of the thesis, the focus is set on how orchestration solutions can support
mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm
and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile
robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated
solutions present high compatibility with the designed edge robotics system where the robot
driving range is extended without any interruption of the end-to-end edge robotics service. While the
DLT-based federation extends the robot driving range by deploying access point extension on top of
external domain infrastructure, OKpi selects the most suitable access point and computing resource
in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled
mobile robots.
To conclude the thesis the focus is set on how robotic systems can improve their performance by
leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions.
To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical
System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling
technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are
integrated with computing and network domains. The role of AI in each technology domain is identified
by analyzing a set of AI agents at the application and infrastructure level. In the last part of the
thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery
mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer
lost commands caused by interference in the wireless channel. The obtained results are showcasing
the its potential in simulation and real-world experimentation.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett
Efficient solutions to the placement and chaining problem of User Plane Functions in 5G Networks
This study attempts to solve the placement and chaining problem of 5G User Plane Functions (UPFs) in a Multi-access Edge Computing (MEC) ecosystem. The problem is formalized as a multi-objective Integer Linear Programming (ILP) model targeted at optimizing provisioning costs and quality of service. Our model takes into account several aspects of the system such as UPF-specific considerations, the Service Function Chain (SFC) requests topology (single and multiple branches), Virtual Network Function (VNF) order constraints, service demands, and physical network capacities. Since the formulated problem is NP-hard, two heuristic solutions are devised to enhance solution efficiency. Specifically, an algorithm called Priority and Cautious-UPF Placement and Chaining (PC-UPC) and a simulated annealing (SA) meta-heuristic are proposed. Through extensive simulation experiments, we evaluated the performance of the proposed solutions. The results revealed that our solutions outperformed the baselines (i.e., two greedy-based heuristics and a variant of the classical SA) and that we had obtained nearly optimal solutions with significant reductions in running time. Moreover, the PC-UPC algorithm can effectively avoid SFC rejections and improve provisioning costs by considering session requirements, current network conditions, and the effects of VNF mapping decisions. Additionally, the proposed SA approach incorporates several mechanisms (e.g., variable Markov chain length and restart–stop) that allow the improvement of not only the quality of the solutions but also their computation time.Postprint (published version
NFV orchestration in edge and fog scenarios
Mención Internacional en el título de doctorLas infraestructuras de red actuales soportan una
variedad diversa de servicios como video bajo demanda,
video conferencias, redes sociales, sistemas
de educación, o servicios de almacenamiento de
fotografías. Gran parte de la población mundial ha
comenzado a utilizar estos servicios, y los utilizan
diariamente. Proveedores de Cloud y operadores
de infraestructuras de red albergan el tráfico de
red generado por estos servicios, y sus tareas de
gestión no solo implican realizar el enrutamiento
del tráfico, sino también el procesado del tráfico de
servicios de red. Tradicionalmente, el procesado
del tráfico ha sido realizado mediante aplicaciones/
programas desplegados en servidores que estaban
dedicados en exclusiva a tareas concretas
como la inspección de paquetes. Sin embargo, en
los últimos anos los servicios de red se han virtualizado
y esto ha dado lugar al paradigma de
virtualización de funciones de red (Network Function
Virtualization (NFV) siguiendo las siglas en
ingles), en el que las funciones de red de un servicio
se ejecutan en contenedores o máquinas virtuales
desacopladas de la infraestructura hardware. Como
resultado, el procesado de tráfico se ha ido
haciendo más flexible gracias al laxo acople del
software y hardware, y a la posibilidad de compartir
funciones de red típicas, como firewalls, entre
los distintos servicios de red.
NFV facilita la automatización de operaciones
de red, ya que tareas como el escalado, o la migración
son típicamente llevadas a cabo mediante
un conjunto de comandos previamente definidos
por la tecnología de virtualización pertinente, bien
mediante contenedores o máquinas virtuales. De
todos modos, sigue siendo necesario decidir el en rutamiento y procesado del tráfico de cada servicio
de red. En otras palabras, que servidores tienen
que encargarse del procesado del tráfico, y que
enlaces de la red tienen que utilizarse para que las
peticiones de los usuarios lleguen a los servidores
finales, es decir, el conocido como embedding problem.
Bajo el paraguas del paradigma NFV, a este
problema se le conoce en inglés como Virtual Network
Embedding (VNE), y esta tesis utiliza el termino
“NFV orchestration algorithm” para referirse
a los algoritmos que resuelven este problema. El
problema del VNE es NP-hard, lo cual significa
que que es imposible encontrar una solución optima
en un tiempo polinómico, independientemente
del tamaño de la red. Como consecuencia, la comunidad
investigadora y de telecomunicaciones
utilizan heurísticos que encuentran soluciones de
manera más rápida que productos para la resolución
de problemas de optimización.
Tradicionalmente, los “NFV orchestration algorithms”
han intentado minimizar los costes de
despliegue derivados de las soluciones asociadas.
Por ejemplo, estos algoritmos intentan no consumir
el ancho de banda de la red, y usar rutas cortas
para no utilizar tantos recursos. Además, una tendencia
reciente ha llevado a la comunidad investigadora
a utilizar algoritmos que minimizan el
consumo energético de los servicios desplegados,
bien mediante la elección de dispositivos con un
consumo energético más eficiente, o mediante el
apagado de dispositivos de red en desuso. Típicamente,
las restricciones de los problemas de VNE se
han resumido en un conjunto de restricciones asociadas
al uso de recursos y consumo energético, y las
soluciones se diferenciaban por la función objetivo
utilizada. Pero eso era antes de la 5a generación de
redes móviles (5G) se considerase en el problema
de VNE. Con la aparición del 5G, nuevos servicios
de red y casos de uso entraron en escena. Los estándares
hablaban de comunicaciones ultra rápidas
y fiables (Ultra-Reliable and Low Latency Communications
(URLLC) usando las siglas en inglés) con
latencias por debajo de unos pocos milisegundos y
fiabilidades del 99.999%, una banda ancha mejorada
(enhanced Mobile Broadband (eMBB) usando
las siglas en inglés) con notorios incrementos en
el flujo de datos, e incluso la consideración de comunicaciones
masivas entre maquinas (Massive
Machine-Type Communications (mMTC) usando
las siglas en inglés) entre dispositivos IoT. Es más,
paradigmas como edge y fog computing se incorporaron a la tecnología 5G, e introducían la idea
de tener dispositivos de computo más cercanos al
usuario final. Como resultado, el problema del VNE
tenía que incorporar los nuevos requisitos como
restricciones a tener en cuenta, y toda solución
debía satisfacer bajas latencias, alta fiabilidad, y
mayores tasas de transmisión.
Esta tesis estudia el problema des VNE, y propone
algunos heurísticos que lidian con las restricciones
asociadas a servicios 5G en escenarios
edge y fog, es decir, las soluciones propuestas se
encargan de asignar funciones virtuales de red a
servidores, y deciden el enrutamiento del trafico
en las infraestructuras 5G con dispositivos edge y
fog. Para evaluar el rendimiento de las soluciones
propuestas, esta tesis estudia en primer lugar la
generación de grafos que representan redes 5G.
Los mecanismos propuestos para la generación de
grafos sirven para representar distintos escenarios
5G. En particular, escenarios de federación en
los que varios dominios comparten recursos entre
ellos. Los grafos generados también representan
servidores en el edge, así como dispositivos fog con
una batería limitada. Además, estos grafos tienen
en cuenta los requisitos de estándares, y la demanda
que se espera en las redes 5G. La generación de
grafos propuesta sirve para representar escenarios
federación en los que varios dominios comparten
recursos entre ellos, y redes 5G con servidores edge,
así como dispositivos fog estáticos o móviles con
una batería limitada. Los grafos generados para
infraestructuras 5G tienen en cuenta los requisitos
de estándares, y la demanda de red que se espera
en las redes 5G. Además, los grafos son diferentes
en función de la densidad de población, y el área
de estudio, es decir, si es una zona industrial, una
autopista, o una zona urbana.
Tras detallar la generación de grafos que representan
redes 5G, esta tesis propone algoritmos de
orquestación NFV para resolver con el problema
del VNE. Primero, se centra en escenarios federados
en los que los servicios de red se tienen que
asignar no solo a la infraestructura de un dominio,
sino a los recursos compartidos en la federación
de dominios. Dos problemas diferentes han sido estudiados,
uno es el problema del VNE propiamente
dicho sobre una infraestructura federada, y el otro
es la delegación de servicios de red. Es decir, si
un servicio de red se debe desplegar localmente
en un dominio, o en los recursos compartidos por
la federación de dominios; a sabiendas de que el último caso supone el pago de cuotas por parte del
dominio local a cambio del despliegue del servicio
de red. En segundo lugar, esta tesis propone
OKpi, un algoritmo de orquestación NFV para conseguir
la calidad de servicio de las distintas slices
de las redes 5G. Conceptualmente, el slicing consiste
en partir la red de modo que cada servicio
de red sea tratado de modo diferente dependiendo
del trozo al que pertenezca. Por ejemplo, una
slice de eHealth reservara los recursos de red necesarios
para conseguir bajas latencias en servicios
como operaciones quirúrgicas realizadas de manera
remota. Cada trozo (slice) está destinado a
unos servicios específicos con unos requisitos muy
concretos, como alta fiabilidad, restricciones de
localización, o latencias de un milisegundo. OKpi
es un algoritmo de orquestación NFV que consigue
satisfacer los requisitos de servicios de red en los
distintos trozos, o slices de la red. Tras presentar
OKpi, la tesis resuelve el problema del VNE en redes
5G con dispositivos fog estáticos y móviles. El
algoritmo de orquestación NFV presentado tiene
en cuenta las limitaciones de recursos de computo
de los dispositivos fog, además de los problemas
de falta de cobertura derivados de la movilidad de
los dispositivos.
Para concluir, esta tesis estudia el escalado
de servicios vehiculares Vehicle-to-Network (V2N),
que requieren de bajas latencias para servicios como
la prevención de choques, avisos de posibles
riesgos, y conducción remota. Para estos servicios,
los atascos y congestiones en la carretera pueden
causar el incumplimiento de los requisitos de latencia.
Por tanto, es necesario anticiparse a esas
circunstancias usando técnicas de series temporales
que permiten saber el tráfico inminente en los
siguientes minutos u horas, para así poder escalar
el servicio V2N adecuadamente.Current network infrastructures handle a diverse
range of network services such as video
on demand services, video-conferences, social
networks, educational systems, or photo
storage services. These services have been
embraced by a significant amount of the
world population, and are used on a daily basis.
Cloud providers and Network operators’
infrastructures accommodate the traffic rates
that the aforementioned services generate, and
their management tasks do not only involve
the traffic steering, but also the processing of
the network services’ traffic. Traditionally,
the traffic processing has been assessed via
applications/programs deployed on servers
that were exclusively dedicated to a specific
task as packet inspection. However, in recent
years network services have stated to be
virtualized and this has led to the Network
Function Virtualization (Network Function
Virtualization (NFV)) paradigm, in which the
network functions of a service run on containers
or virtual machines that are decoupled
from the hardware infrastructure. As a result,
the traffic processing has become more flexible
because of the loose coupling between
software and hardware, and the possibility
of sharing common network functions, as
firewalls, across multiple network services.
NFV eases the automation of network operations,
since scaling and migrations tasks
are typically performed by a set of commands
predefined by the virtualization technology,
either containers or virtual machines. However,
it is still necessary to decide the traffic steering and processing of every network
service. In other words, which servers will
hold the traffic processing, and which are the
network links to be traversed so the users’ requests
reach the final servers, i.e., the network
embedding problem. Under the umbrella of
NFV, this problem is known as Virtual Network
Embedding (VNE), and this thesis refers
as “NFV orchestration algorithms” to those
algorithms solving such a problem. The VNE
problem is a NP-hard, meaning that it is impossible
to find optimal solutions in polynomial
time, no matter the network size. As a
consequence, the research and telecommunications
community rely on heuristics that find
solutions quicker than a commodity optimization
solver.
Traditionally, NFV orchestration algorithms
have tried to minimize the deployment
costs derived from their solutions. For example,
they try to not exhaust the network
bandwidth, and use short paths to use less
network resources. Additionally, a recent
tendency led the research community towards
algorithms that minimize the energy consumption
of the deployed services, either
by selecting more energy efficient devices
or by turning off those network devices that
remained unused. VNE problem constraints
were typically summarized in a set of resources/energy constraints, and the solutions
differed on which objectives functions were
aimed for. But that was before 5th generation
of mobile networks (5G) were considered
in the VNE problem. With the appearance
of 5G, new network services and use cases
started to emerge. The standards talked about
Ultra Reliable Low Latency Communication
(Ultra-Reliable and Low Latency Communications
(URLLC)) with latencies below few
milliseconds and 99.999% reliability, an enhanced
mobile broadband (enhanced Mobile
Broadband (eMBB)) with significant data
rate increases, and even the consideration
of massive machine-type communications
(Massive Machine-Type Communications
(mMTC)) among Internet of Things (IoT) devices.
Moreover, paradigms such as edge and
fog computing blended with the 5G technology
to introduce the idea of having computing
devices closer to the end users. As a result, the VNE problem had to incorporate the new
requirements as constraints to be taken into
account, and every solution should either
satisfy low latencies, high reliability, or larger
data rates.
This thesis studies the VNE problem, and
proposes some heuristics tackling the constraints
related to 5G services in Edge and
fog scenarios, that is, the proposed solutions
assess the assignment of Virtual Network
Functions to resources, and the traffic steering
across 5G infrastructures that have Edge and
Fog devices. To evaluate the performance
of the proposed solutions, the thesis studies
first the generation of graphs that represent
5G networks. The proposed mechanisms to
generate graphs serve to represent diverse 5G
scenarios. In particular federation scenarios
in which several domains share resources
among themselves. The generated graphs
also represent edge servers, so as fog devices
with limited battery capacity. Additionally,
these graphs take into account the standard
requirements, and the expected demand for
5G networks. Moreover, the graphs differ depending
on the density of population, and the
area of study, i.e., whether it is an industrial
area, a highway, or an urban area.
After detailing the generation of graphs
representing the 5G networks, this thesis proposes
several NFV orchestration algorithms
to tackle the VNE problem. First, it focuses
on federation scenarios in which network services
should be assigned not only to a single
domain infrastructure, but also to the shared
resources of the federation of domains. Two
different problems are studied, one being the
VNE itself over a federated infrastructure, and
the other the delegation of network services.
That is, whether a network service should be
deployed in a local domain, or in the pool
of resources of the federation domain; knowing
that the latter charges the local domain
for hosting the network service. Second, the
thesis proposes OKpi, a NFV orchestration
algorithm to meet 5G network slices quality
of service. Conceptually, network slicing consists
in splitting the network so network services
are treated differently based on the slice
they belong to. For example, an eHealth network
slice will allocate the network resources necessary to meet low latencies for network
services such as remote surgery. Each network
slice is devoted to specific services with
very concrete requirements, as high reliability,
location constraints, or 1ms latencies. OKpi is
a NFV orchestration algorithm that meets the
network service requirements among different
slices. It is based on a multi-constrained
shortest path heuristic, and its solutions satisfy
latency, reliability, and location constraints.
After presenting OKpi, the thesis tackles the
VNE problem in 5G networks with static/moving
fog devices. The presented NFV orchestration
algorithm takes into account the limited
computing resources of fog devices, as well
as the out-of-coverage problems derived from
the devices’ mobility.
To conclude, this thesis studies the scaling
of Vehicle-to-Network (V2N) services, which
require low latencies for network services as
collision avoidance, hazard warning, and remote
driving. For these services, the presence
of traffic jams, or high vehicular traffic congestion
lead to the violation of latency requirements.
Hence, it is necessary to anticipate to
such circumstances by using time-series techniques
that allow to derive the incoming vehicular
traffic flow in the next minutes or hours,
so as to scale the V2N service accordingly.The 5G Exchange (5GEx) project (2015-2018) was an EU-funded project (H2020-ICT-2014-2 grant agreement 671636).
The 5G-TRANSFORMER project (2017-2019) is an EU-funded project (H2020-ICT-2016-2 grant agreement 761536).
The 5G-CORAL project (2017-2019) is an EU-Taiwan project (H2020-ICT-2016-2 grant agreement 761586).Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Ioannis Stavrakakis.- Secretario: Pablo Serrano Yáñez-Mingot.- Vocal: Paul Horatiu Patra
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