Study, evaluation and contributions to new algorithms for the embedding problem in a network virtualization environment

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change and to enable a new business model decoupling the network services from the underlying infrastructure. The problem of embedding virtual networks in a substrate network is the main resource allocation challenge in network virtualization and is usually referred to as the Virtual Network Embedding (VNE) problem. VNE deals with the allocation of virtual resources both in nodes and links. Therefore, it can be divided into two sub-problems: Virtual Node Mapping where virtual nodes have to be allocated in physical nodes and Virtual Link Mapping where virtual links connecting these virtual nodes have to be mapped to paths connecting the corresponding nodes in the substrate network. Application of network virtualization relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as VNE algorithms. This thesis proposes a set of contributions to solve the research challenges of the VNE that have not been tackled by the research community. To do that, it performs a deep and comprehensive survey of virtual network embedding. The first research challenge identified is the lack of proposals to solve the virtual link mapping stage of VNE using single path in the physical network. As this problem is NP-hard, existing proposals solve it using well known shortest path algorithms that limit the mapping considering just one constraint. This thesis proposes the use of a mathematical multi-constraint routing framework called paths algebra to solve the virtual link mapping stage. Besides, the thesis introduces a new demand caused by virtual link demands into physical nodes acting as intermediate (hidden) hops in a path of the physical network. Most of the current VNE approaches are centralized. They suffer of scalability issues and provide a single point of failure. In addition, they are not able to embed virtual network requests arriving at the same time in parallel. To solve this challenge, this thesis proposes a distributed, parallel and universal virtual network embedding framework. The proposed framework can be used to run any existing embedding algorithm in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network Energy efficiency is one of the main challenges in future networking environments. Network virtualization can be used to tackle this problem by sharing hardware, instead of requiring dedicated hardware for each instance. Until now, VNE algorithms do not consider energy as a factor for the mapping. This thesis introduces the energy aware VNE where the main objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. To evaluate and validate the aforementioned VNE proposals, this thesis helped in the development of a software framework called ALgorithms for Embedding VIrtual Networks (ALEVIN). ALEVIN allows to easily implement, evaluate and compare different VNE algorithms according to a set of metrics, which evaluate the algorithms and compute their results on a given scenario for arbitrary parameters

Network Virtualization strategy based on Paths Algebra to implement the concept of Infrastructure as a Service

One of the main challenges of network virtualization is the virtual network embedding problem(VNE). The objective of the VNE is to map a set of Virtual Network Request (VNR) to a physical node and link. VNR is composed by a set of virtual nodes and links with several demands (Processing power, Bandwidth.), which they need to be assigned into a set of paths in the substrate network with sufficient resources to accomplish their demands. Furthermore, these embedding can be optimized with regard to several parameters, such as: embedding cost, link bandwidth, energy- efficiency, packet loss rate, throughput, etc. To solve the VNE program a mathematical tools was proposed, called “Paths algebra”. This framework can helped solve the multiple multi- constraint routing problems of VNE using linear metrics as bandwidth, number of hops and delay, or non-linear metrics as availability and package loss rate Most of the existing VNE proposals treat the single-path virtual link-mapping problem as a mono-constraint, that is, their objective is to map the virtual links in substrate paths that minimize/maximize the usage of one resource (typically bandwidth). This paper introduces a virtual link mapping approach supporting multiple constraints using the “paths algebra” routing framewor

Power aware resource allocation and virtualization algorithms for 5G core networks

Most of the algorithms that solved the resource allocation problem, used to apply greedy algorithms to select the physical nodes and shortest paths to select the physical edges, without sufficient coordination between selecting the physical nodes and edges. This lack of coordination may degrade the overall acceptance ratios and network performance as whole, in addition, that may include non-necessary physical resources, which will consume more power and computational processing capacities, as well as cause more delays. Therefore, the main objective of this PhD thesis is to develop power aware resource allocation and virtualization algorithms for 5G core networks, which will be achieved through developing a virtualization resource allocation technique to perform virtual nodes and edges allocations in full coordination, and on the least physical resources. The algorithms will be general and solve the resource allocation problem for virtual network embedding and network function virtualization frameworks, while minimizing the total consumed power in the physical network, and consider end-to-end delay and migration as new optional features. This thesis suggested to solve the power aware resource allocation problem through brand new algorithms adopting a new technique called segmentation, which fully coordinates allocating the virtual nodes and edges together, and guarantees to use the very least physical resources to minimize the total power consumption, through consolidating the virtual machines into least number of nodes as much as possible. The proposed algorithms, solves virtual network embedding problem for off-line and on-line scenarios, and solves resource allocations for network function virtualization environment for off-line, on-line, and migration scenarios. The evaluations of the proposed off-line virtual network embedding algorithm, PaCoVNE, showed that it managed to save physical network power consumption by 57% in average, and the on-line algorithm, oPaCoVNE, managed to minimize the average power consumption in the physical network by 24% in average. Regarding allocation times of PaCoVNE and oPaCoVNE, they were in the ranges of 20-40 ms. For network function virtualization environment, the evaluations of the proposed offline NFV power aware algorithm, PaNFV, showed that on average it had lower total costs and lower migration cost by 32% and 65:5% respectively, compared to the state-of-art algorithms, while the on-line algorithm, oPaNFV, managed to allocate the Network Services in average times of 60 ms, and it had very negligible migrations. Nevertheless, this thesis suggests that future enhancements for the proposed algorithms need to be focused around modifying the proposed segmentation technique to solve the resource allocation problem for multiple paths, in addition to consider power aware network slicing, especially for mobile edge computing, and modify the algorithms for application aware resource allocations for very large scale networks. Moreover, future work can modify the segmentation technique and the proposed algorithms, by integrating machine learning techniques for smart traffic and optimal paths prediction, as well as applying machine learning for better energy efficiency, faster load balancing, much accurate resource allocations based on verity of quality of service metrics.La mayoría de los algoritmos que resolvieron el problema de asignación de recursos, se utilizaron para aplicar algoritmos codiciosos para seleccionar los nodos físicos y las rutas más cortas para seleccionar los bordes físicos, sin una coordinación suficiente entre la selección de los nodos físicos y los bordes. Esta falta de coordinación puede degradar los índices de aceptación generales y el rendimiento de la red en su totalidad, además, que puede incluir recursos físicos no necesarios, que consumirán más potencia y capacidades de procesamiento computacional, además de causar más retrasos. Por lo tanto, el objetivo principal de esta tesis doctoral es desarrollar algoritmos de virtualización y asignación de recursos para las redes centrales 5G, que se lograrán mediante el desarrollo de una técnica de asignación de recursos de virtualización para realizar nodos virtuales y asignaciones de bordes en total coordinación, y al menos recursos físicos. Los algoritmos serán generales y resolverán el problema de asignación de recursos para la integración de redes virtuales y los marcos de virtualización de funciones de red, al tiempo que minimizan la potencia total consumida en la red física y consideran el retraso y la migración de extremo a extremo como nuevas características opcionales. Esta tesis sugirió resolver el problema de la asignación de recursos conscientes de la potencia a través de nuevos algoritmos que adoptan una nueva técnica llamada segmentación, que coordina completamente la asignación de los nodos virtuales y los bordes, y garantiza el uso de los recursos físicos mínimos para minimizar el consumo total de energía, a través de consolidar las máquinas virtuales en el menor número de nodos tanto como sea posible. Los algoritmos propuestos solucionan el problema de integración de la red virtual para los escenarios sin conexión y en línea, y resuelve las asignaciones de recursos para el entorno de virtualización de la función de red para los escenarios sin conexión, en línea y de migración. Las evaluaciones del algoritmo de integración de red virtual sin conexión propuesto, PaCoVNE, mostraron que logró ahorrar el consumo de energía de la red física en un 57% en promedio, y el algoritmo en línea, oPaCoVNE, logró minimizar el consumo de energía promedio en la red física en un 24% en promedio. Con respecto a los tiempos de asignación de PaCoVNE y oPaCoVNE, estuvieron en los rangos de 20-40 ms. Para el entorno de virtualización de la función de red, las evaluaciones del algoritmo consciente de la potencia NFV sin conexión propuesto, PaNFV, mostraron que, en promedio, tenía menores costos totales y menores costos de migración en un 32% y 65: 5% respectivamente, en comparación con el estado de la técnica. Los algoritmos, mientras que el algoritmo en línea, oPaNFV, logró asignar los Servicios de Red en tiempos promedio de 60 ms, y tuvo migraciones muy insignificantes. Sin embargo, esta tesis sugiere que las futuras mejoras para los algoritmos propuestos deben centrarse en modificar la técnica de segmentación propuesta para resolver el problema de asignación de recursos para múltiples rutas, además de considerar el corte de la red que requiere energía, especialmente para la computación de borde móvil, y modificar el Algoritmos para asignaciones de recursos conscientes de la aplicación para redes de gran escala. Además, el trabajo futuro puede modificar la técnica de segmentación y los algoritmos propuestos, mediante la integración de técnicas de aprendizaje automático para el tráfico inteligente y la predicción de rutas óptimas, así como la aplicación del aprendizaje automático para una mejor eficiencia energética, un equilibrio de carga más rápido, asignaciones de recursos mucho más precisas basadas en la veracidad de Métricas de calidad de servicio

Increasing the Reliability of Power and Communication Networks via Robust Optimization

Uncertainty plays an increasingly significant role in the planning and operation of complex networked infrastructure. The inclusion of variable renewable energy in power systems makes ensuring basic grid requirements such as transmission line constraints and the power balance between supply and demand more involved. Likewise, data traffic in communication networks varies greatly with user preferences and service availability, and with communication networks carrying more traffic than ever due to the surge in network-enabled devices, coping with the highly variable data flows between server and end-users becomes more crucial for the network's overall stability. Within this context, we propose in this thesis new adaptable methods for optimizing flows in power and communication systems that explicitly consider the growing variability in these systems to guarantee optimal operation with a flexible degree of reliability. The proposed methods use a robust optimization framework, making constraints dependent on uncertain factors tractable by replacing originally stochastic conditions with deterministic counterparts. The primary benefit of robust methods is that they ensure the system is feasible for any values of the uncertain variables within a given continuous set of possible realizations. This, however, can lead to excessively conservative solutions. Therefore, we also investigate how to reduce the conservativeness of the proposed algorithms. This thesis focuses on two classes of problems in power and communication systems, flow control and the placement of flow-controlling devices. In power systems, flow control refers to actions that induce changes in the power carried by transmission lines to minimize or maximize a specific objective value while considering the electrical grid's physical constraints. Some examples of power flow control actions are the change of switching equipment's state, regulation of generators' set points, and the management of the so-called Flexible AC Transmission Systems (FACTS) devices. For the last two action types, we propose a robust approach to optimize the corresponding control policies. As for communication networks, (data) flow control is implemented at each router in the network. These routers define the path and the rate data is forwarded using routing tables. We show that it is possible to robustly design policies to adapt these routing tables that optimize the data flows in the network depending on the instantaneous rate of the system's exogenous inputs. For both flow problems, we employ a robust optimization framework where affine-linear functions parametrize the flow control policies. The parametrized policies can be efficiently computed via linear or quadratic programming, depending on the system's constraints. Furthermore, we consider the placement problems in the form of FACTS placement and the embedding of virtual networks in an existing communication network to improve the reliability of the network systems. Both problems are formulated as robust Mixed-Integer Linear Programs (MILP). However, because finding provable optimal solutions in large networks is computationally challenging, we also develop approximate algorithms that can yield near-optimal results while being several times faster to solve than the original MILP. In the proposed robust framework, the flow control and the placement of controlling-devices problems are solved together to take into account the coupling effects of the two optimization measures. We demonstrate the proposed methodology in a series of use cases in power and communication systems. We also consider applications in Smart Grids, where communication and electric networks are closely interlinked. E.g., communication infrastructure enables real-time monitoring of the status of power grids and sending timely control signals to devices controlling the electric flow. Due to the increasing number of renewable energy resources, Smart Grids must adapt to fast changes in operating conditions while meeting application-dependent reliability requirements. The robust optimization methods introduced in this thesis can thus use the synergy between flexible power and communication systems to provide secure and efficient Smart Grid operation

Du placement des services à la surveillance des services dans les réseaux 5G et post-5G

5G and beyond 5G (B5G) networks are expected to accommodate a plethora of network services with diverse requirements using a single physical infrastructure. Hence, the ``one-size fits all'' paradigm that characterized the 4th generation of wireless networks is no longer suitable. By leveraging the last advent of Network Function Virtualization (NFV) and Software-Defined Networking (SDN), Network Slicing (NS) is considered as one of the key enablers of this paradigm shift. NS will enable the coexistence of heterogeneous services by partitioning the physical infrastructure into a set of virtual networks ''(the slices)'', each running a particular service. Besides, NS offers more flexibility and agility in business operations.Despite the advantages it brings, NS raises some technical challenges. The placement of network slices is one of them, it is known in the literature as the Virtual Network Embedding Problem (VNEP), and it is an NP-Hard problem. Therefore, the first part of this thesis focuses on unveiling the potential of Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) to solve the network slice placement problem and overcome the limitations of existing methods. Two approaches are considered: The first one aims to learn automatically how to solve the VNEP. Instead of putting any constraint on the topology of the physical infrastructure or extracting features manually, we formulate the task as a reinforcement problem, and we use a graph convolutional-based neural architecture to learn how to find an optimal solution. Next, instead of training a DRL agent from scratch to find the optimal solution, a process that may result in unsafe training, we train it to reduce the optimality gap of existing heuristics. The motivation behind this contribution is to ensure safety during the training of the DRL agent.The placement of the slices is not the only challenge raised by NS. Once the slices are placed, monitoring the status of network slices becomes a priority for both network slices' tenants and providers in order to ensure that Service Level Agreements (SLAs) are not violated. In the second part of this thesis, we propose to leverage machine learning techniques and network tomography to monitor the network slices. Network Tomography (NT) is defined as a set of methods that aim to infer unmeasured network metrics using an end-to-end measurement between monitors.We focus on two main challenges. First, on the inference of slices metrics based on some end-to-end measurements between monitors, as well as on the efficient monitor placement. For the inference, we model the task as a multi-output regression problem, which we solve using neural networks. We propose to train on synthetic data to augment the diversity of the training data and avoid the overfitting issue. Moreover, to deal with the changes that may occur either on the slices we monitor or the topology on top of which they are placed, we use transfer learning techniques.Regarding the monitor's placement problem, we consider a special case where only cycles' probes are allowed. The probing cycle schemes have a significant advantage compared to regular paths since the source probe is actually the destination, which reduces the synchronization problems. We formulate the problem as a variant of the Minimum Set Cover problem. Owing to its complexity, we introduce a standalone solution based on GNNs and genetic algorithms to find a trade-off between the quality of monitors placement and the cost to achieve it.Les réseaux 5G et au-delà sont destinés à servir un large éventail de services réseau aux besoins très disparates tout en utilisant la même infrastructure physique. En scindant l'infrastructure physique en un ensemble de réseaux virtuels, chacun exploitant un service spécifique, le Network Slicing (NS) permettra la coexistence de ces services. En dépit de ses avantages, le NS est complexe d'un point de vue technique puisqu'il s'agit d'un problème NP-hard. La première section de la thèse explore le potentiel de l'apprentissage par renforcement profond (DRL) basé sur des graphes de réseaux neuronaux pour résoudre le problème du placement des tranches de réseau et remédier aux limites des techniques existantes. Deux approches sont proposées : la première consiste à apprendre à résoudre automatiquement le problème du placement. Plutôt que de se limiter à la topologie de l'infrastructure physique ou à extraire manuellement des caractéristiques, le problème est formulé sous la forme d'un processus de décision markovien qui est résolu à l'aide d’un réseau de neurones convolutif à base de graphes pour apprendre à découvrir une solution optimale. Ensuite, plutôt que de former un agent DRL de zéro pour identifier la meilleure solution, ce qui pourrait entraîner un défaut de fiabilité, un agent est présenté pour réduire l'écart d'optimalité des heuristiques existantes. Une fois les tranches placées, la surveillance de l'état des tranches de réseau devient une priorité pour s'assurer que les SLAs sont respectés. Ainsi, dans la deuxième partie de la thèse, il est proposé d'utiliser des techniques d'apprentissage automatique et la tomographie réseau (NT) pour surveiller les tranches de réseau. Il y a deux problèmes majeurs à prendre en compte. Premièrement, les métriques de slices sont déduites sur la base de diverses mesures de bout en bout entre les moniteurs, ainsi que du placement efficace des moniteurs. Des réseaux neuronaux sont utilisés pour traiter l'inférence des métriques. Une approche d'apprentissage par transfert est également utilisée pour faire face aux changements qui peuvent se produire sur les slices surveillés ou sur la topologie physique sur laquelle elles sont placées. Des sondes cycliques sont envisagées pour le problème du placement des moniteurs. Le problème est formulé comme une variante du problème de couverture par ensembles. En raison de sa complexité, il est proposé d'introduire une solution autonome basée sur des réseaux neuronaux à base de graphes (GNN) et des algorithmes génétiques pour trouver un compromis entre la qualité du placement des moniteurs et le coût pour y parvenir

Network virtualization and traffic engineering in Software-Defined Networks

[ANGLÈS]Lately, the emerging paradigm of Software-Defined Networking has grown in presence and claims to simplify future networking. The decoupling of network control and forwarding plane proposed by this architecture allows the control of the entire network behavior by means of a logically centralized software program (controller). Such separation of planes opens the way to Network Virtualization, which provides users a logical abstraction of underlying network resources. However, network virtualization requires a mapping phase of the virtual resources over the physical resources, which is not trivial, formally known as the virtual network embedding problem. The present document focuses in the development of a variant of one of the proposed strategies to solve this critical step, prioritizing the real-time response. The proposed algorithm uses the properties offered by the Paths Algebra mathematical framework to provide a flexible environment where it is possible to combine any number of both linear and non-linear metrics. In addition, it is also used the multi-lexical ordination, a criterion to better distinguish paths that may be considered equal by other approaches. Such algorithm has been implemented as a software application that provides a simulation environment where the virtual network embedding process can be tested for any given topology. Subsequently, all the algorithm features have been checked in a set of performance tests, focusing on those oriented to the commitment among the real-time response and the quality of the embedding solutions. In general, testing results are very promising even in densely populated backbone topologies, where the number of alternative paths among each possible pair of origin and destination nodes grows exponentially.[CASTELLÀ] Software-Defined Networking (o Redes Definidas por Software) es un nuevo paradigma que tiene como objetivo simplificar la creación y gestión de redes de ordenadores. El desacoplamiento entre el control de la red y el plano de reenvío propuesto por esta arquitectura permite el control de todo el comportamiento de la red mediante un elemento lógico centralizado, llamado controlador. Esta separación de los planos abre la puerta a la virtualización de redes, proporcionando a los usuarios una abstracción lógica de los recursos de red subyacentes. Sin embargo, la virtualización de red requiere de una fase de asignación de los recursos virtuales a los recursos físicos, que no es trivial y que se conoce formalmente como el problema de incrustación de redes virtuales. El presente documento se centra en el desarrollo de una variante de una de las estrategias propuestas para resolver este paso crítico, dando prioridad a la respuesta en tiempo real. El algoritmo propuesto utiliza las propiedades ofrecidas por el marco matemático de Paths Algebra (o Álgebra de Caminos) para proporcionar un entorno flexible donde es posible combinar cualquier número de métricas lineales y no lineales. Además, también utiliza la ordenación multi-léxica, un criterio para distinguir mejor aquellos caminos que podrían ser considerados equivalentes por otros enfoques. Este algoritmo se ha implementado como una aplicación de software que proporciona un entorno de simulación en el que se puede probar el proceso de incrustación de redes virtuales para cualquier topología. Posteriormente, se han comprobado todas las características del algoritmo mediante un conjunto de pruebas de rendimiento, priorizando aquellas orientadas al compromiso entre la respuesta en tiempo real y la calidad de las soluciones de incrustación. En general, los resultados de las pruebas son muy prometedores incluso en topologías de redes troncales densamente pobladas, donde el número de caminos alternativos entre cada posible nodo origen y destino crece exponencialmente

Contribution to multi-domain network slicing : resource orchestration framework and algorithms

5G/6G services and applications, in the context of the eMBB, mMTC and uRLLC network slicing framework, whose network infrastructure requirements may span beyond the coverage area of a single Infrastructure Provider (InP), are envisaged to be supported by leasing resources from multiple InPs. A challenging aspect for a Service Provider (SP) is how to obtain an optimal set of InPs on which to provision the requests and the particular substrate nodes and links within each InP on which to map the different VNFs and virtual links of the service requests, respectively, for a seamless, reliable and cost-effective orchestration of service requests. Existing works in this area either perform service mapping in uncoordinated manner, do not incorporate service reliability or do so from the perspective of stateless VNFs. Also they assume full information disclosure, or are based on exact approaches, which considerations are not well suited for future network scenarios characterized by delay sensitive mission critical applications and resource constrained networks. This thesis contributes to the above challenge by breaking the multi-domain service orchestration problem into two interlinked sub-problems that are solved in a coordinated manner: (1) Request splitting/partitioning (sub-problem 1), involving obtaining a subset of InPs and the corresponding inter-domain links on which to provision the different VNFs and virtual links of the service request; (2) Intra-domain VNF orchestration (sub-problem 2), involving obtaining the intra-domain nodes and links to provision the VNFs and virtual links of the sub-SFC associated with each InP. In this way, the thesis sets out four key targets that are necessary to align with the mission critical and delay sensitive use-cases envisaged in 5G and future networks in terms of service deployment cost and QoS: (1) coordinated mapping of service requests, with a view of realizing better utilization of the substrate resources; (2) survivability and fault-tolerant orchestration of service requests, to tame both QoS violations and the penalties from such violations; (3) limited disclosure of InP internal information, in order adhere to the privacy requirements InPs, and (4) achieving all the above targets in polynomial time. In order to realize the above targets, the thesis sought for solution techniques that are: (1) able to incorporate information learned in the previous solutions search space and historical mapping decisions, hence, resulting in acceptable performance even in scenarios of limited information exposure and fuzzy environments; (2) robust and less problem specific, hence, can be tailored to different optimization objectives, network topologies and service request constraints, thus enabling to deal with requests with either chained topologies or with bifurcated paths; (3) capable of dealing with an optimization problem that is jointly affected by multiple attributes, since in practice, the service deployment cost is jointly affected by multiple conflicting costs; (4) able to realize near-optimal solutions in practical run-times, thus rendering well suited approaches for delay sensitive and resource constrained scenarios. Three different algorithms namely, an RL, Genetic Algorithm (GA) and a fully distributed multi-stage graph-based algorithms are proposed for sub-problem 1. In addition, five different algorithms based on GA, Harmony search, RL, and multi-stage graph approach are proposed for sub-problem 2. Finally, in order to guide the implementation and adherence of the thesis proposals to the four main targets of the thesis, an architectural framework is proposed, aligned with the ETSI NFV-MANO architectural framework. Overall, the simulations results proved that the thesis proposals are optimized in terms of request acceptance ratios, mapping cost and execution time, hence, rendering such proposals well suited for 5G and future scenarios.Els serveis que es poden presentar en el marc de la tecnologia de “slicing” de xarxa de 5G/6G, com ara eMBB, mMTC o uRLLC, es possible que no els pugui oferir un sol proveïdor d’infraestructura (InP) degut a les limitacions que pot tenir la seva xarxa, i per tant que faci necessària la cooperació de múltiples InPs. En aquest cas, el primer repte que afronta el Proveïdor de Servei (SP) que rep la sol·licitud de desplegament es determinar el conjunt òptim de InPs que hi han d’intervenir i en concret els nodes i enllaços de cada un d’ells que s’han d’utilitzar per al mapatge de les diferents VNFs i enllaços virtuals de la sol·licitud. Els treballs que existeixen en aquesta àrea duen a terme el mapatge del servei be sigui de manera no coordinada, o no incorporen la fiabilitat, o ho fan des de la perspectiva de VNFs sense estat. També, pressuposen la divulgació total de la informació, o estan basats en metodologies exactes que fa que no siguin idonis per a escenaris de xarxes del futur, caracteritzats per aplicacions de missió critica, sensibles al retard i sobre xarxes amb recursos limitats. Aquesta tesi contribueix a afrontar aquests reptes dividint el problema d’orquestració de serveis multi domini en dos subproblemes relacionats, que es resolen de manera coordinada. (1) Divisió / partició de la sol·licitud de servei (sub-problema 1), que implica l'obtenció d'un subconjunt d'InPs i els enllaços interdomini corresponents sobre els quals proporcionar les diferents VNF i enllaços virtuals de la sol·licitud de servei; (2) Orquestració VNF intradomini (sub-problema 2), que implica l'obtenció dels nodes i enllaços intradomini per aprovisionar les VNF i enllaços virtuals dels sub-SFC associats a cada InP. D'aquesta manera, la tesi estableix quatre objectius clau que són necessaris per alinear-se amb els casos d'ús de missió crítica i sensibles al retard previstos en 5G i xarxes futures en termes de cost de desplegament del servei i QoS: (1) mapatge coordinat de les sol·licituds de servei, amb l'objectiu de realitzar una millor utilització dels recursos del substrat; (2) orquestració de les sol·licituds de servei contemplant la supervivència del servei en situacions de fallides, minimitzant les violacions de la QoS i les sancions derivades d'aquestes violacions; (3) divulgació limitada de la informació interna de l’InP, per tal d'adherir-se als requisits de privadesa dels InPs, i (4) aconseguir tots els objectius anteriors en temps polinòmic. Per tal de realitzar els objectius anteriors, la tesi busca solucions que siguin: (1) capaces d'incorporar informació apresa en les solucions anteriors de l'espai de cerca i decisions de mapatge històric, donant lloc a un rendiment acceptable fins i tot en escenaris d'exposició limitada a la informació i entorns difusos; (2) robustes i menys dependents dels problemes específics, i per tant, que es poden adaptar a diferents objectius d'optimització, topologies de xarxa i restriccions de sol·licitud de servei, permetent així fer front a sol·licituds amb cadenes de funcions de topologies molt diverses; (3) capaces de fer front a un problema d'optimització de múltiples atributs, ja que a la pràctica, el cost de desplegament del servei depèn de múltiples costos; (4) capaces de trobar solucions gairebé òptimes en temps suficientment breus, resultant així adequades a escenaris sensibles al retard i amb limitació de recursos. La tesi proposa tres algorismes diferents per al sub-problema 1: un algorisme de RL, un algorisme genètic (GA) i un algorisme multi etapa basat en grafs i completament distribuït. A més, es proposen cinc algorismes diferents basats en l'enfocament de grafs, un algorisme GA, un algorisme de cerca d’harmonia, un algorisme de RL i un algorisme multi-etapa per al sub-problema 2. Finalment, per tal de guiar la implementació i l'adhesió de les propostes als quatre objectius principals de la tesi, es proposa...Postprint (published version

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

Architectures and algorithms for dynamic overlay networks

Most of today’s Internet of Things (IoT) applications assume that data will be moved offdevices into centralized cloud platforms. While existing IoT systems leverage cloud-based analytics for meaningful data reasoning, the assumption that data should always be moved off the devices is problematic. The amount of data to be moved from devices over Internet gateways to cloud platforms is huge which potentially make it cost inefficient. In other scenarios, privacy concerns of customers or organizational rules complicate the process of transferring data to third-party data centers.This dissertation proposes architectures and dynamic overlay network algorithms for in-networkand edge processing of data offered by the globally available IoT devices and provides a global platform for meaningful and responsive data analysis and decision making. The proposed techniques shift IoT analytics from a ”collect data now and analyze it later” scenario to directlyproviding meaningful information from the in-network processing of devices data at or near thedevices. The techniques serve future IoT use cases including distributed context awareness, on-demand data analysis, and in-network decision making. The dissertation comprises three main components.The first component is a device management protocol for cloning devices’ data in proximateEdge Computing platforms. Unlike existing application-layer IoT management protocols theproposed protocol uses the LTE LTE-A radio frame structure, device-to-device communication,and IoT data properties to avoid excessive network access latency in existing technologies.The second component realizes distributed IoT analytics as overlay networks of devices clones. By means of virtual network embedding, it selects and interconnects devices’ clones to efficiently realize applications’ virtual topologies to achieve goals such as minimum latency, minimum infrastructure cost, or maximum infrastructure utilization.Finally, the dissertation presents a communication middleware that allows autonomous discovery, self-deployment, and online migration of devices’ clones across heterogeneous Edge computing platforms. The middleware ensures that communication latency between clones is kept minimum despite the uncontrolled variability of the network and hosting platforms conditions.We evaluate the proposed architectures and algorithms through simulations and prototypeimplementation of various components in controlled testbed environments, which we evaluateusing real user applications. We explore the feasibility of the proposed techniques from boththeoretical and practical perspectives.Keywords: Cloud Computing, Internet of Things, Algorithmic Game Theory, Compressive Sensin