44 research outputs found
A Distributed SON-Based User-Centric Backhaul Provisioning Scheme
5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes
Maximising the Utility of Enterprise Millimetre-Wave Networks
Millimetre-wave (mmWave) technology is a promising candidate for meeting the
intensifying demand for ultra fast wireless connectivity, especially in
high-end enterprise networks. Very narrow beam forming is mandatory to mitigate
the severe attenuation specific to the extremely high frequency (EHF) bands
exploited. Simultaneously, this greatly reduces interference, but generates
problematic communication blockages. As a consequence, client association
control and scheduling in scenarios with densely deployed mmWave access points
become particularly challenging, while policies designed for traditional
wireless networks remain inappropriate. In this paper we formulate and solve
these tasks as utility maximisation problems under different traffic regimes,
for the first time in the mmWave context. We specify a set of low-complexity
algorithms that capture distinctive terminal deafness and user demand
constraints, while providing near-optimal client associations and airtime
allocations, despite the problems' inherent NP-completeness. To evaluate our
solutions, we develop an NS-3 implementation of the IEEE 802.11ad protocol,
which we construct upon preliminary 60GHz channel measurements. Simulation
results demonstrate that our schemes provide up to 60% higher throughput as
compared to the commonly used signal strength based association policy for
mmWave networks, and outperform recently proposed load-balancing oriented
solutions, as we accommodate the demand of 33% more clients in both static and
mobile scenarios.Comment: 22 pages, 12 figures, accepted for publication in Computer
Communication
QoS-aware Adaptive Resource Management in OFDMA Networks
PhDOne important feature of the future communication network is that users in the
network are required to experience a guaranteed high quality of service (QoS) due
to the popularity of multimedia applications. This thesis studies QoS-aware radio
resource management schemes in different OFDMA network scenarios.
Motivated by the fact that in current 4G networks, the QoS provisioning is severely
constrained by the availability of radio resources, especially the scarce spectrum
as well as the unbalanced traffic distribution from cell to cell, a joint antenna and
subcarrier management scheme is proposed to maximise user satisfaction with load
balancing. Antenna pattern update mechanism is further investigated with moving
users.
Combining network densi fication with cloud computing technologies, cloud radio
access network (C-RAN) has been proposed as the emerging 5G network architecture
consisting of baseband unit (BBU) pool, remote radio heads (RRHs) and
fronthaul links. With cloud based information sharing through the BBU pool,
a joint resource block and power allocation scheme is proposed to maximise the
number of satisfi ed users whose required QoS is achieved. In this scenario, users
are served by high power nodes only. With spatial reuse of system bandwidth by
network densi fication, users' QoS provisioning can be ensured but it introduces
energy and operating effciency issue. Therefore two network energy optimisation
schemes with QoS guarantee are further studied for C-RANs: an energy-effective
network deployment scheme is designed for C-RAN based small cells; a joint RRH
selection and user association scheme is investigated in heterogeneous C-RAN.
Thorough theoretical analysis is conducted in the development of all proposed
algorithms, and the effectiveness of all proposed algorithms is validated via comprehensive
simulations.China Scholarship Counci
Adaptive vehicular networking with Deep Learning
Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities.
In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects.
The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup.
The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios.
The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark
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
User Association Optimisation in HetNets: Algorithms and Performance
PhDThe fifth generation (5G) mobile networks expect significantly higher transmission rate
and energy efficiency than existing networks. Heterogeneous networks (HetNets), where
various low power base stations (BSs) are underlaid in a macro-cellular network, are
likely to become the dominate theme during the wireless evolution towards 5G. However
the complex HetNets scenario poses substantial challenges to the user association design.
This thesis focuses on the user association optimisation for different HetNets scenarios.
First, user association policy is designed for conventional grid-powered HetNets via game
theory. An optimal user association algorithm is proposed to improve the downlink (DL)
system performance. In order to address the uplink-downlink (UL-DL) asymmetry issue
in HetNets, a joint UL and DL user association algorithm is further developed to enhance
both UL and DL energy efficiencies. In addition, an opportunistic user association
algorithm in multi-service HetNets is proposed for quality of service (QoS) provision of
delay constraint traffic while providing fair resource allocation for best effort traffic.
Second, driven by increasing environmental concerns, user association policy is designed
for green HetNets with renewable energy powered BSs. In such a scenario, the proposed
adaptive user association algorithm is able to adapt the user association decision to the
amount of renewable energy harvested by BSs.
Third, HetNets with hybrid energy sources are investigated, as BSs powered by both
power grid and renewable energy sources have the superiority in supporting uninterrupted
service as well as achieving green communications. In this context, an optimal
user association algorithm is developed to achieve the tradeoffs between average traffic
delay and on-grid energy consumption. Additionally, a two-dimensional optimisation on
user association and green energy allocation is proposed to minimise both total and peak
on-grid energy consumptions, as well as enhance the QoS provision.
Thorough theoretical analysis is conducted in the development of all proposed algorithms,
and performance of proposed algorithms is evaluated via comprehensive simulations
A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems
Producción CientíficaIn recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42, PID2021-124463OBI00 y RED2018-102585-T, financiados por MCIN/AEI/10.13039/501100011033