Search

Modern cloud orchestrators like Kubernetes provide a versatile and robust way to host applications at scale. One of their key features is autoscaling, which automatically adjusts cloud resources (compute, memory, storage) in order to adapt to the demands of applications. However, the scope of cloud autoscaling is limited to the datacenter hosting the cloud and it doesn't apply uniformly to the allocation of network resources. In I/O-constrained or data-in-motion use cases this can lead to severe performance degradation for the application. For example, when the load on a cloud service increases and the Wide Area Network (WAN) connecting the datacenter to the Internet becomes saturated, the application flows experience an increase in delay and loss. In many cases this is dealt with overprovisioning network capacity, which introduces additional costs and inefficiencies. On the other hand, thanks to the concept of "Network as Code", the WAN exposes a set of APIs that can be used to dynamically allocate and de-allocate capacity on-demand. In this paper we propose extending the concept of cloud autoscaling into the network to address this limitation. This way, applications running in the cloud can communicate their networking requirements, like bandwidth or traffic profile, to a Software-Defined Networking (SDN) controller or Network as a Service (NaaS) platform. Moreover, we aim to define the concepts of vertical and horizontal autoscaling applied to networking. We present a prototype that automatically allocates bandwidth to the underlay network, according to the requirements of the applications hosted in Kubernetes. Finally, we discuss open research challenges., This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

WireGuard is a VPN protocol that has gained significant interest recently. Its main advantages are: (i) simple configuration (via pre-shared SSH-like public keys), (ii) mobility support, (iii) reduced codebase to ease auditing, and (iv) Linux kernel implementation that yields high performance. However, WireGuard (intentionally) lacks a control plane. This means that each peer in a WireGuard network has to be manually configured with the other peers’ public key and IP addresses, or by other means. In this paper we present an architecture based on a centralized server to automatically distribute this information. In a nutshell, first we manually establish a WireGuard tunnel to the centralized server, and ask all the peers to store their public keys and IP addresses in it. Then, WireGuard peers use this secure channel to retrieve on-demand the information for the peers they want to communicate to. Our design strives to: (i) offer a key distribution scheme simpler than PKI-based ones, (ii) limit the number of public keys sent to the peers, and (iii) reduce tunnel establishment latency by means of an UDP-based protocol. We argue that such automation can help the deployment in enterprise or ISP scenarios. We also describe in detail our implementation and analyze several performance metrics. Finally, we discuss possible improvements regarding several shortcomings we found during implementation., This work was partially supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works propose the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS are barely robust to common adversarial attacks, which limits their applicability to real networks. A fundamental problem of these solutions is that they treat and classify flows independently. In contrast, in this paper we argue the importance of focusing on the structural patterns of attacks, by capturing not only the individual flow features, but also the relations between different flows (e.g., the source/destination hosts they share). To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information. In our evaluation, we first show that the proposed GNN model achieves state-of-the-art results in the well-known CIC-IDS2017 dataset. Moreover, we assess the robustness of our solution under two common adversarial attacks, that intentionally modify the packet size and interarrival times to avoid detection. The results show that our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under these attacks. This unprecedented level of robustness is mainly induced by the capability of our GNN model to learn flow patterns of attacks structured as graphs., This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), and by the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project, funded under grant agreement No. 871528. This article reflects only the authors’ view; the European Commission is not responsible for any use that may be made of the information it contains., Peer Reviewed, Postprint (published version)

Recent years have seen the vast potential of graph neural networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GNNs are becoming increasingly popular in the field of networking, as graphs are intrinsically present at many levels (e.g., topology, routing). The main novelty of GNNs is their ability to generalize to other networks unseen during training, which is an essential feature for developing practical machine learning (ML) solutions for networking. However, implementing a functional GNN prototype is currently a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to network engineers that often do not have the necessary ML expertise. In this article, we present IGNNITION, a novel open source framework that enables fast prototyping of GNNs for networking systems. IGNNITION is based on an intuitive high-level abstraction that hides the complexity behind GNNs, while still offering great flexibility to build custom GNN architectures. To showcase the versatility and performance of this framework, we implement two state-of-the-art GNN models applied to different networking use cases. Our results show that the GNN models produced by IGNNITION are equivalent in terms of accuracy and performance to their native implementations in TensorFlow., This open source project has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This article reflects only the author’s view; the European Commission is not responsible for any use that may be made of the information it contains. The work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA), Peer Reviewed, Postprint (author's final draft)

The Border Gateway Protocol (BGP) is the inter-domain routing protocol that glues the Internet. BGP does not incorporate security and instead, it relies on careful configuration and manual filtering to offer some protection. As a consequence, the current inter-domain routing infrastructure is partially vulnerable to prefix and path hijacks as well as in misconfigurations that results in route leaks. There are many instances of these vulnerabilities being exploited by malicious actors on the Internet, resulting in disruption of services. To address this issue the IETF has designed RPKI, a centralised trust architecture that relies on Public Key Infrastructure. RPKI has slow adoption and its centralised nature is problematic: network administrators are required to trust CAs and do not have the ultimate control of their own critical Internet resources (e.g,. IP blocks, AS Numbers). In this context, we have built the Decentralised Internet Infrastructure (DII), a distributed ledger to securely store inter-domain routing information. The main advantages of DII are (i) it offers flexible trust models where the Internet community can define the rules of a consensus algorithm that properly reflects the power balance of its members and, (ii) offers protection against vulnerabilities (path hijack and route leaks) that goes well beyond what RPKI offers. We have deployed the prototype on the wild in a worldwide testbed including 7 ASes, we will use the testbed to demonstrate in a realistic scenario how allocation and delegation of Internet resources in DII work, and how this protects ASes against artificially produced path and prefix hijack as well as a route leak., This work was partially supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic., This work has received funding from the European Union’s H2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This paper reflects only the authors’ view; the European Commission is not responsible for any use that may be made of the information it contains. This work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA), and by FI-AGAUR grant by the Catalan Government. Salzburg Research is grateful for the support by the WISS 2025 (Science and Innovation Strategy Salzburg 2025) project ”IDALab Salzburg” (20204-WISS/225/197-2019 and 20102-F1901166-KZP) and the 5G-AI-MLab by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and the Austrian state Salzburg., Peer Reviewed, Article escrit per 24 autors/autores: José Suárez-Varela (1), Miquel Ferriol-Galmés (1), Albert López (1), Paul Almasan (1), Guillermo Bernárdez (1), David Pujol-Perich (1), Krzysztof Rusek (1,2), Loïck Bonniot (3, 4), Christoph Neumann (3), François Schnitzler (3), François Taïani (4), Martin Happ (5, 6), Christian Maier (5), Jia Lei Du (5), Matthias Herlich (5), Peter Dorfinger (5), Nick Vincent Hainke (7), Stefan Venz (7), Johannes Wegener (7), Henrike Wissing (7), Bo Wu (8), Shihan Xiao (8), Pere Barlet-Ros (1), Albert Cabellos-Aparicio (1). 1- Barcelona Neural Networking center, Universitat Politècnica de Catalunya, Spain. 2- AGH University of Science and Technology, Department of Telecommunications, Poland. 3- InterDigital, France. 4- Univ. Rennes, Inria, CNRS, IRISA, France. 5- Salzburg Research Forschungsgesellschaft mbH, Austria. 6- IDA Lab, University of Salzburg, Austria. 7- Fraunhofer HHI, Germany. 8- Network Technology Lab., Huawei Technologies Co., Ltd., China., Postprint (author's final draft)

Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique for modeling graph-structured data. This makes them especially suitable for applications in the networking field, as communication networks inherently comprise graphs at many levels (e.g., topology, routing, user connections). In this demo, we will present IGNNITION, an open-source framework for fast prototyping of GNNs applied to communication networks. This framework is especially designed for network engineers and/or researchers with limited background on neural network programming. IGNNITION comprises a set of tools and functionalities that eases and accelerates the whole implementation process, from the design of a GNN model, to its training, evaluation, debugging, and integration into larger network applications. In the demo, we will show how a user can implement a complex GNN model applied to network performance modeling (RouteNet), following three simple steps., This open-source project has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This article reflects only the authors’ view; the EC is not responsible for any use that may be made of the information it contains. The work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

Modern cloud orchestrators like Kubernetes provide a versatile and robust way to host applications at scale. One of their key features is autoscaling, that automatically adjusts cloud resources (compute, memory, storage) in order to dynamically adapt to the demands of the application. However, the scope of cloud autoscaling is limited to the datacenter hosting the cloud and it doesn't apply uniformly to the allocation of network resources. In I/O-constrained or data-in-motion use cases this can lead to severe performance degradation for the application. For example, when the load on a cloud service increases and the Wide Area Network (WAN) connecting the datacenter to the Internet becomes saturated, the application experiences an increase in delay and loss. In many cases this is dealt by overprovisioning network capacity, which introduces significant additional costs and inefficiencies. On the other hand, thanks to the concept of "Network as Code", the WAN today exposes a programmable set ofAPIs that can be used to dynamically allocate and deallocate capacity on-demand. In this paper we propose extending the concept of cloud autoscaling into the network to address this limitation. This way, applications running in the cloud can communicate their networking requirements, like bandwidth or traffic profile, to an SDN controller or Network as a Service (NaaS) platform. Moreover, we aim to define the concepts of vertical and horizontal autoscaling applied to networking. We present a prototype that automatically allocates bandwidth in the underlay of an SD-WAN, according to the requirements of the applications hosted in Kubernetes. Finally, we discuss open research challenges.

The digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking community has seen a noticeable increase in the requirements of emerging network applications. One main open challenge is the need to accommodate control systems to highly dynamic network scenarios. Nowadays, existing network optimization technologies do not meet the needed requirements to effectively operate in real time. Some of them are based on hand-crafted heuristics with limited performance and adaptability, while some technologies use optimizers which are often too time-consuming. Recent advances in Deep Reinforcement Learning (DRL) have shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficiently solve a variety of relevant network optimization problems, such as online routing. In this paper, we explore the use of state-of-the-art DRL technologies for real-time routing optimization and outline some relevant open challenges to achieve production-ready DRL-based solutions., This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No 871528. This paper reflects only the author’s view; the European Commission is not responsible for any use that may be made of the information it contains. This work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)

Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and intra-datacentre (DC) network and computational resources into a single converged 5G network infrastructure. The present paper overviews the main achievements obtained in the ALLIANCE project. This project ambitiously aims at architecting a converged 5G-enabled network infrastructure satisfying those needs to effectively realise the envisioned upcoming Digital Society. In particular, we present two networking solutions for 5G and beyond 5G (B5G), such as Software Defined Networking/Network Function Virtualisation (SDN/NFV) on top of an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure, and the clean-slate Recursive Inter-Network Architecture (RINA) over packet networks, including access, metro, core and DC segments. The common umbrella of all these solutions is the Knowledge-Defined Networking (KDN)-based orchestration layer which, by implementing Artificial Intelligence (AI) techniques, enables an optimal end-to-end service provisioning. Finally, the cross-layer manager of the ALLIANCE architecture includes two novel elements, namely the monitoring element providing network and user data in real time to the KDN, and the blockchain-based trust element in charge of exchanging reliable and confident information with external domains., This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under contract FEDER TEC2017-90034-C2 (ALLIANCE project) and by the Generalitat de Catalunya under contract 2017SGR-1037 and 2017SGR-605., Peer Reviewed, Postprint (published version)

Overlay networks are a technique to build a new network on top of an existing one. They are a key tool to add functionality to existing networks, and are used in different layers of the Internet stack for a wide variety of purposes, like confidentiality, Quality of Service, virtual networking, etc. Specifically, network overlays in the IP networking layer are widely used in some of these use cases. However, these kind of overlay networks do not have as many functionalities as overlays in other layers. For example, thanks to the Zero Trust Networking paradigm it is possible to build secure overlay networks at L7 using HTTPS. Taking this into account, this thesis strives to add new features and improve on others of IP overlay networks, in order to support emerging challenges. This thesis focuses on three axes: security, trust, and deployment in enterprise scenarios. First, regarding security, we explore how to simplify the setup of secure tunnels over the Internet, without relying on external Public Key Infrastructure or proprietary solutions. To this purpose, we leverage WireGuard, a state of the art VPN protocol, and add a control plane on top of it to distribute encryption keys. In addition, we present the implementation of a prototype and a performance evaluation. Second, with respect to trust, we investigate how emerging blockchain technology can be used in distributed mapping systems. Mapping systems are a database used in some overlay network deployments to assist in the creation of tunnels, by storing overlay to underlay pairs of addresses. Mapping systems are not commonly used in scenarios with multiple administrative domains, due to configuration complexity and centralized control. We explore how some of the properties of blockchains, such as distributed control, or auditability, can help in building these type of mapping systems. We take into account both the policy aspects, that is, the advantages of a distributed trust scheme, and the technical ones, like, Les xarxes superposades o overlays són una tècnica per a construir una xarxa sobre una d'existent. Són una eina fonamental per a afegir funcionalitat a xarxes ja existents, i es fan servir en diferents capes de la pila de protocols d’Internet per a diferents objectius, com ara confidencialitat, Qualitat de Servei, virtualització de xarxes, etc. Específicament, les xarxes superposades a la capa de xarxa IP es fan servir àmpliament per alguns d’aquest casos d’ús. No obstant, aquest tipus de xarxes superposades no tenen tanta funcionalitat com overlays en altres capes. Per exemple, gràcies al paradigma Zero Trust és possible construir xarxes superposades segures a la capa 7 amb HTTPS. Tenint això en compte, aquesta tesi vol afegir noves funcionalitats i millorar-ne d’altres en xarxes IP superposades, amb l’objectiu d’afrontar nous reptes. Aquesta tesi es centra en tres eixos: seguretat, confiança i desplegament en escenaris de xarxes empresarials. En primer lloc, pel que fa a seguretat, explorem com simplificar la configuració de túnels segurs a través d’Internet, sense dependre duna infraestructura de clau pública externa o de solucions propietàries. Per a aquest objectiu, utilitzem WireGuard, un protocol VPN d’última generació i li afegim un pla de control per a distribuir les claus d’encriptació. A més, presentem la implementació d’un prototip i una avaluació del seu rendiment. En segon lloc, respecte la confiança, investiguem com les tecnologies emergents basades en cadenes de blocs (blockchain) es poden fer servir en sistemes de mapatge distribuïts. Els sistemes de mapatge són una base de dades que es fa servir en alguns desplegaments de xarxes superposades per a ajudar en la creació dels túnels; normalment guarden parelles d’adreces que tradueixen l’adreça de la xarxa superposada a la de la xarxa de sota. Els sistemes de mapatge no es solen utilitzar en escenaris amb múltiples dominis administratius, degut a la complexitat de la configuració i a la centralització, Las redes superpuestas u overlays son una técnica para construir una nueva red encima de una ya existente. Son una herramienta clave para añadir funcionalidad a redes existentes, y se usan en diferentes capas de la pila de protocolos de Internet para una gran variedad de propósitos, como confidencialidad, Calidad de Servicio, redes virtuales, etc. Específicamente, las redes superpuestas en la capa de red IP son ampliamente usadas para algunos de estos casos de uso. No obstante, este tipo de redes no disponen de tantas funcionalidades como redes superpuestas en otras capas. Por ejemplo, gracias al paradigma Zero Trust es posible construir redes superpuestas seguras en la capa 7 usando HTTPS. Teniendo esto en cuenta, esta tesis tiene como objetivo añadir nuevas funcionalidades y mejorar otras de las redes IP superpuestas, con el propósito de afrontar los nuevos retos que van apareciendo. Esta tesis se centra en tres ejes: seguridad, confianza y despliegue en escenarios empresariales. En primer lugar, y respecto a la seguridad, exploramos cómo simplificar la configuración de túneles seguros a través de Internet, sin usar una infraestructura de clave pública externa o soluciones propietarias. Para este objetivo, utilizamos WireGuard, un protocolo VPN de última generación, y le añadimos un plano de control para distribuir las claves de encriptado. Además, presentamos la implementación de un prototipo y una evaluación de rendimiento. En segundo lugar, en relación a la confianza, investigamos como las tecnologías emergentes basadas en cadena de bloques (blockchain) se pueden usar en sistemas de mapeado distribuidos. Los sistemas de mapeado son una base de datos que se utiliza en algunas redes superpuestas para ayudar en la creación de los túneles. Normalmente, estos sistemas guardan parejas de direcciones que traducen direcciones de la red superpuesta a la de la red subyacente. Los sistemas de mapeado no son muy utilizados en escenarios con múltiples dominios administrativ, Postprint (published version)

Sixth Generation (6G) of wireless networks will be even more heterogeneous and dense as compared to Fifth Generation (5G) and other legacy networks. Thus, the 6G architecture will need to be adapted to serve the ever-evolving capacity and quality of service requirements. To satisfy these ever-increasing demands, multiple enablers such as visible light communication, light fidelity, Reconfigurable Intelligent Surfaces (RISs), THz communications, etc., have been proposed. Specifically, RISs, through their programmable characteristics, can perform the fine-grained manipulation of the radio signals being generated by the myriad transmitter devices/access points for their corresponding receivers. Such manipulations include absorption of certain components of the impinging radio signals, as well as fine-grained control of these signals in terms of direction, polarization, phase, and power in a frequency-selective manner. An RIS consists of a device that controls the behavior of the Electromagnetic (EM) waves, alongside circuits that provide the tuning mechanism and the intelligence to control it. This device that controls the EM wave behavior can be realized using Metasurfaces (MSs), which are electromagnetically thin-film and planar artificial structures that enable the control of EM fields in engineered and even atypical ways. Hence, the MS is a component of the RIS. On a more granular level, an MS is composed of subwavelength building blocks known as unit cells or meta-atoms. The design of these unit cells depends on the required EM functionality, reconfigurability, or accuracy. The promises of the RIS paradigm, therefore, come at the expense of a non-trivial complexity in the MS. On the one hand, the performance of an RIS depends on the size of the unit cells, the number of unit cell states, or the size of the whole MS. On the other hand, there are costs and energy overheads associated with the fabrication and operation of RISs that also scale with the aforementioned, La sisena generació (6G) de xarxes sense fils seran encara més denses i heterogènies que les xarxes de cinquena generació. Per tant, la arquitectura de les xarxes 6G necessitaran adaptar-se a necessitats de capacitat i qualitat de servei en constant canvi. Per servir aquestes necessitats creixents, s’han proposat diverses tecnologies com comunicacions a l’espectre visible, Light Fidelity (LiFi), superfícies reconfigurables intel·ligents (RIS, per les sigles en anglès), comunicacions a la banda dels THz, etc. En concret, les RIS han despertat interès degut a la seva capacitat de manipular de forma precisa i programàtica les senyals de ràdio generades per diversos transmissors o punts d’accés i dirigir-les a múltiples receptors. Les manipulacions esmentades inclouen l’absorció de certs components de les ones incidents, o el control de la seva direcció, polarització, fase, o amplitud de forma selectiva en freqüència. En aquest context, les metasuperfícies (MSs) són part essencial de les RIS com a estructures artificials i planars que implementen la funció electromagnètica u òptica que la RIS necessita, que pot ser fins i tot anti-natural i atípica. Així doncs, els RIS consisteixen de dispositius que controlen les ones electromagnètiques (que podrien ser MS) co-integrades amb dispositius que doten a la MS de reconfigurabilitat i intel·ligència. Les MS, perla seva banda, estan composades de elements menor de la longitud d’ona que es coneixen coma “unit cells” o “meta-atoms”. El problema radica en que la promesa de les RIS, però, comporta una complexitat a nivell de MS que no és trivial. Per una banda, el rendiment de la RIS depèn de la mida de les unit cells, el número d’estats que pugui prendre la unit cell, o la mida de la MS completa. D’altra banda, hi ha problemes de costos i consum energètic associat amb la fabricació i operació de les RIS, que també depenen dels factors abans esmentats. Aquesta tesi té com a objectiu adreçar aquests problemes proposant un mètode pe, Postprint (published version)

Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes with high inner complexity. As a result, there is still certain skepticism among the networking industry about their practical viability to operate data networks. In this context, explainability techniques have recently emerged to unveil why DL models make each decision. This paper focuses on the explainability of Graph Neural Networks (GNNs) applied to networking. GNNs are a novel DL family with unique properties to generalize over graphs. As a result, they have shown unprecedented performance to solve complex network optimization problems. This paper presents NetXplain, a novel real-time explainability solution that uses a GNN to interpret the output produced by another GNN. In the evaluation, we apply the proposed explainability method to RouteNet, a GNN model that predicts end-to-end QoS metrics in networks. We show that NetXplain operates more than 3 orders of magnitude faster than state-of-the-art explainability solutions when applied to networks up to 24 nodes, which makes it compatible with real-time applications; while demonstrating strong capabilities to generalize to network scenarios not seen during training., This work has been supported by the Spanish MINECO under contract TEC2017‑90034‑C2‑1‑R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (published version)

Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution)., This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia as well as the European Social Fund., Peer Reviewed, Postprint (author's final draft)

Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes due to their high inner complexity. As a result, there is still certain skepticism among the computer network industry about their practical viability to operate data networks. In this context, explainability techniques have recently emerged to unveil why DL models make each decision. This paper focuses on Graph Neural Network (GNN) models applied to computer networks, which have already shown outstanding performance in different network optimization tasks. We thus present NetXplain, a novel real-time explainability solution that uses a GNN to interpret the output produced by another GNN. In the evaluation, we apply the proposed explainability method to RouteNet –a GNN model that predicts end-to-end performance metrics in computer networks. We show that NetXplain operates more than 3 orders of magnitude faster than state-of-the-art explainability solutions when applied to networks up to 24 nodes, which makes this solution compatible with real-time applications. Moreover, it demonstrated strong generalization capabilities over different network scenarios unseen during training., This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This paper reflects only the author’s view; the EC is not responsible for any use that may be made of the information it contains. This work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (published version)

Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, logistics). However, implementing a GNN prototype is still a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to researchers and practitioners that want to apply GNN to their specific problems but do not have the needed Machine Learning expertise. In this paper, we present IGNNITION, a novel open-source framework for fast prototyping of GNNs. This framework is built on top of TensorFlow, and offers an intuitive high-level abstraction that allows the user to define its GNN model via a YAML file, being completely oblivious to the tensor-wise operations made internally by the model. At the same time, IGNNITION offers great flexibility to build any GNN-based architecture. To showcase its versatility, we implement two state-of-the-art GNN models applied to the field of computer networks, which differ considerably from well-known standard GNN architectures. Our evaluation results show that the GNNs produced by IGNNITION are equivalent in performance to implementations directly coded in TensorFlow., This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This paper reflects only the authors’ view; the European Commission is not responsible for any use that may be made of the information it contains. This work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (published version)

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment., This research was funded by the European Commission grant number H2020-FETOPEN736876 (VISORSURF) and by ICREA under the ICREA Academia program., Peer Reviewed, Postprint (published version)

The goal of this project is to design a solution for massive mobility using LISP protocol and scalable database systems like Apache Kafka. The project consists of three steps: rst, understanding the requirements of the massive mobility scenario; second, designing a solution based on a stream processing software that integrates with OOR (open-source LISP implementation). Third, building a prototype with OOR and a stream processing software (or a similar technology) and evaluating its performance. Our objectives are: Understand the requirements in an environment for massive mo- bility;Learn and evaluate the architecture of Apache Kafka and similar broker messages to see if these tools could satisfy the requirements; Propose an architecture for massive mobility using protocol LISP and Kafka as mapping system, and nally; Evaluate the performance of Apache Kafka using such architecture. In chapters 3 and 4 we will provide a summary of LISP protocol, Apache Kafka and other message brokers. On these chapters we describe the components of these tools and how we can use such components to achieve our objective. We will be evaluating the di erent mechanisms to 1) authenticate users, 2) access control list, 3) protocols to assure the delivery of the message, 4)integrity and 5)communication patterns. Because we are interested only in the last message of the queue, it is very important that the broker message provides a capability to obtain this message. Regarding the proposed architecture, we will see how we adapted Kafka to store the information managed by the mapping system in LISP. The EID in LISP will be repre- sented by topics in Apache Kafka., It will use the pattern publish-subscribe to spread the noti cation between all the subscribers. xTRs or Mobile devices will be able to play the role of Consumers and Publisher of the message brokers. Every topic will use only one partition and every subscriber will have its own consumer group to avoid competition to consume the mes

In the inter-domain routing infrastructure, a route leak is defined as a violation of the routing policy agreed between two Autonomous Systems (AS). Route leaks have resulted in large-scale outages on the Internet, taking down several services. Although route leaks seem a simple problem, the solution is complex because: (i) ASes consider -partially- routing policy private, (ii) lack of a formal and standard language to express routing policy and (iii) BGP lacks adequate cryptographic-based security. In this paper, we present an experimental analysis of a distributed ledger-based architecture that provides a solution to route leaks. Specifically, the routing policy is unambiguously expressed using a formal language, that is then stored in a blockchain. This decentralized architecture allows private policies and interfaces seamlessly with the current BGP infrastructure, requiring no changes to routers. We build a prototype to evaluate our proposed architecture using Hyperledger, we analyze its performance using a real-world BGP dataset. Our results show that our architecture scales linearly with relevant metrics. Additionally, we validate the architecture preventing an artificially introduced route leak in a realistic 10 AS topology., This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

Metasurfaces, the ultrathin, 2D version of metamaterials, have recently attracted a surge of attention for their capability to manipulate electromagnetic waves. Recent advances in reconfigurable and programmable metasurfaces have greatly extended their scope and reach into practical applications. Such functional sheet materials can have enormous impact on imaging, communication, and sensing applications, serving as artificial skins that shape electromagnetic fields. Motivated by these opportunities, this progress report provides a review of the recent advances in tunable and reconfigurable metasurfaces, highlighting the current challenges and outlining directions for future research. To better trace the historical evolution of tunable metasurfaces, a classification into globally and locally tunable metasurfaces is first provided along with the different physical addressing mechanisms utilized. Subsequently, coding metasurfaces, a particular class of locally tunable metasurfaces in which each unit cell can acquire discrete response states, is surveyed, since it is naturally suited to programmatic control. Finally, a new research direction of software-defined metasurfaces is described, which attempts to push metasurfaces toward unprecedented levels of functionality by harnessing the opportunities offered by their software interface as well as their inter- and intranetwork connectivity and establish them in real-world applications., This work was supported by the European Union’s Horizon 2020 research and innovation programme-Future Emerging Topics (FETOPEN) under grant agreement No 736876 (VISORSURF). Financial support by the National Priorities Research Program grant No. NPRP9-383-1-083 from the Qatar National Research Fund is also acknowledged. O.T. acknowledges the financial support of the Stavros Niarchos Foundation within the framework of the project ARCHERS (“Advancing Young Researchers’ Human Capital in Cutting Edge Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges”)., Peer Reviewed, Postprint (published version)

The wireguard protocol has been recently introduced to provide communciation security among end-points. Wireguard has similar goals than traditional VPNs (Virtual Private Networks), but has been designed with efficiency and mobility in mind., This thesis is based on IP security, Internet Protocol (IP) was developed without security or mobility. Later with the time, IPsec was implemented, a secure IP protocol which is complicated, weighty and doesn?t include mobility either. Wireguard is a VPN protocol from the new security family protocols which provides a security layer for IP in a very simple way and also, includes mobility, a very useful feature in the nowadays networks. But, Wireguard doesn?t include a control-plane to manage all the cryptographic material into his users. I have used the Locator/Identifier Separation Protocol (LISP) to apply a Wireguard functional control-plane. In OOR software, where LISP is implemented, I merge all the Wireguard features with the LISP control-plane developing an efficient, functional and more important secure protocol. In the end, I achieve to include what Wireguard didn?t have, increasing his value and the use cases for that new secure protocol., Esta tesis se basa en la seguridad de IP, el Protocolo de Internet (IP) se desarrolló sin seguridad ni movilidad. Más adelante con el tiempo, se implementó IPsec, un protocolo de IP seguro que es complicado, pesado y que tampoco incluye movilidad. Wireguard es un protocolo de las nuevas familias VPNs que proporciona una capa de seguridad para IP de una manera muy simple y también incluye movilidad, una característica muy útil en las redes actuales. Pero, Wireguard no incluye un plano de control para administrar todo el material criptográfico en sus usuarios. He utilizado el Protocolo Locator/Identifier Separation Protocol (LISP) para aplicar un plano de control funcional a Wireguard. En el software OOR, donde se implementa LISP, fusiono todas las funciones de Wireguard con el plano de control de LISP, desarrollando un protocolo seguro eficiente y funcional. Al final, se consigue incluir lo que Wireguard no tenía al principio, aumentando su valor y los casos de uso donde se puede aplicar este nuevo protocolo de seguridad., Aquesta tesi es basa en la seguretat d'IP, el Protocol d'Internet (IP) es va desenvolupar sense seguretat ni mobilitat. Més endavant, amb el temps, es va implementar IPsec, un protocol d'IP segur que és complicat, pesat pero que tampoc inclou mobilitat. Wireguard és un protocol de les noves famílies de VPNs que proporciona una capa de seguretat per a IP d'una manera molt simple i tenint en compte la mobilitat a Internet, una característica molt útil en les xarxes actuals. Però, Wireguard no inclou un pla de control per administrar tot el material criptogràfic entre els seus usuaris. He utilitzat el Protocol Locator/*Identifier Separation Protocol (LISP) per a aplicar un pla de control funcional a Wireguard. En el programari OOR, on s'implementa LISP, fusiono totes les funcions de Wireguard amb el pla de control de LISP, desenvolupant un protocol segur eficient i funcional. Al final, s'aconsegueix incloure el que Wireguard no tenia al principi, augmentant el seu valor i els casos d'ús on es pot aplicart aquest nou protocol de seguretat.

Al llarg dels últims anys, hem pogut comprovar el gran potencial que tenen les Graph Neural Networks (GNN) al aplicar-se a una gran varietat de problemes que es formalitzen en forma de grafs (e.g xarxes de telecomunicacions, química, física..). Normalment, la aplicació de GNNs a cadascun d'aquests camps implica un procés de personalització del model per tal d'adaptar-lo al problema en qüestió. En aquest context, aquesta sol ser una tasca molt complexa i que, en conseqüència, requereix d'experts en aprenentatge automàtic per a dur-la a terme. Més concretament, per a implementar la solució en llenguatges basats en operacions de tensors, com per exemple Tensorflow o PyTorch. Tot i així, nosaltres argumentem que és imprescindible la creació d'eines que permetin dur a terme una abstracció del problema de manera automàtica, simplificant així la seva creació per part de l'usuari. Per aquest motiu, en aquesta tèsis introduïm un nou framework que anomenem IGNNITION, que permet crear GNNs ràpidament i de manera molt entenedora. IGNNITION, doncs, està enfocat a oferir flexibilitat per a la creació d'una gran varietat de GNNs, especialment als usuaris amb molts pocs coneixements d'aprenentatge automàtic. Per a fer-ho, el framework que proposem permet definir models a través d'una simple descripció de les seves parts i la manera en que aquestes es relacionen. A més a més, per tal d'assistir als usuaris, el framework inclou un sistema de debug que permet identificar ràpidament els possibles errors. Els nostres resultats experimentals, quelcom, demostren que la precisió dels models de GNN generats per IGNNITION són equivalents a la implementació original, així com que no es produeix cap pèrdua significativa d'eficiència., Recent years have witnessed the vast potential of Graph Neural Networks (GNN) to be applied to a plethora of problems where data is structured as graphs (e.g., computer networks, chemistry, physics). Typically, each new application requires custom GNN models adapted to the problem environment. In this context, implementing a GNN model is a cumbersome task that currently requires Machine Learning (ML) experts with high skills in neural network programming. For instance, to translate the model to complex tensor-wise operations in languages such as TensorFlow or PyTorch. We claim that to approach GNNs to real-world applications it is essential to count on tools that abstract users from the complexity behind the implementation of such models. In this thesis, we propose IGNNITION, a novel framework to fast prototype complex GNN models. IGNNITION targets users with little to no background on neural network programming, while still providing great flexibility to implement a broad variety of GNN architectures. Our framework enables us to define a GNN model as an intuitive human-readable description of the objects present in graphs and the type of relations they may have, and it automatically generates an efficient TensorFlow implementation of the model. To assist users, it provides advanced debugging mechanisms to easily identify and correct possible errors in GNN models. Our experimental results show that GNN models generated by our framework do not incur performance loss compared to native implementations in TensorFlow.

The final publication is available at Springer via http://dx.doi.org/10.1007/s10922-020-09545-w, Despite remarkable advances, current augmented and virtual reality (AR/VR) applications are a largely individual and local experience. Interconnected AR/VR, where participants can virtually interact across vast distances, remains a distant dream. The great barrier that stands between current technology and such applications is the stringent end-to-end latency requirement, which should not exceed 20 ms in order to avoid motion sickness and other discomforts. Bringing AR/VR to the next level to enable immersive interconnected AR/VR will require significant advances towards 5G ultra-reliable low-latency communication (URLLC) and a Tactile Internet of Things (IoT). In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality., Maria Torres Vega is funded by the Research Foundation Flanders (FWO), grant number 12W4819N. This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under contract TEC2017-90034-C2-1-R (ALLIANCE project) that receives funding from FEDER, Peer Reviewed, Postprint (published version)

Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces 5¿×5¿ or larger with unit cells of ¿/3 and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than ¿>60o and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal metasurface dimensioning., This work was supported in part by the European Commission (VISORSURF) under Grant H2020-FETOPEN-736876, and in part by Catalan Institution for Research and Advanced Studies (ICREA) through the ICREA Academia Programme. Odysseas Tsilipakos acknowledges support by the Stavros Niarchos Foundation within the framework of the project Advancing Young Researchers Human Capital in Cutting Edge Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges (ARCHERS)., Peer Reviewed, Postprint (published version)

Internet is used in our daily life in any technological device we handle. Confidential data is also sent between countries in just few seconds through the Internet. But what if I say that Internet is not perfectly secure and that there are known and opened vulnerabilities? This project aims to solve some of these vulnerabilities explained in a research paper using a decentralized approach and, more specifically, generate multiple benchmarks in order to validate the solution performance. The complete development of the system exceeds the scope for a single technical engineering project. The work has been divided in distinct sections and distributed between two engineers. As all the sections are complementary this document contains the details of all of them. Hyperledger has been used as the base decentralized system, nevertheless, many features have been modified to fulfil our project dependencies.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Recent years have seen the emergence of programmable metasurfaces, where the user can modify the electromagnetic (EM) response of the device via software. Adding reconfigurability to the already powerful EM capabilities of metasurfaces opens the door to novel cyber-physical systems with exciting applications in domains such as holography, cloaking, or wireless communications. This paradigm shift, however, comes with a non-trivial increase of the complexity of the metasurfaces that will pose new reliability challenges stemming from the need to integrate tuning, control, and communication resources to implement the programmability. While metasurfaces will become prone to failures, little is known about their tolerance to errors. To bridge this gap, this paper examines the reliability problem in programmable metamaterials by proposing an error model and a general methodology for error analysis. To derive the error model, the causes and potential impact of faults are identified and discussed qualitatively. The methodology is presented and exemplified for beam steering, which constitutes a relevant case for programmable metasurfaces. Results show that performance degradation depends on the type of error and its spatial distribution and that, in beam steering, error rates over 20% can still be considered acceptable., This work has been supported by the European Commission under grant H2020-FETOPEN-736876 (VISORSURF) and by ICREA under the ICREA Academia programme. The person and base station icons in Figure 1 were created by Jens Tärningand Clea Doltz from the Noun Project., Peer Reviewed, Postprint (author's final draft)

Enterprise networks, over the years, have become more and more complex trying to keep up with new requirements that challenge traditional solutions. Just to mention one out of many possible examples, technologies such as Virtual LANs (VLANs) struggle to address the scalability and operational requirements introduced by Internet of Things (IoT) use cases. To keep up with these challenges we have identified four main requirements that are common across modern enterprise networks: (i) scalable mobility, (ii) endpoint segmentation, (iii) simplified administration, and (iv) resource optimization. To address these challenges we designed SDA (Software Defined Access), a solution for modern enterprise networks that leverages Software-Defined Networking (SDN) and other state of the art techniques. In this paper we present the design, implementation and evaluation of SDA. Specifically, SDA: (i) leverages a combination of an overlay approach with an event-driven protocol (LISP) to dynamically adapt to traffic and mobility patterns while preserving resources, and (ii) enforces policies to groups of endpoints for scalable segmentation with low operational burden. We present our experience with deploying SDA in two real-life scenarios: an enterprise campus, and a large warehouse with mobile robots. Our evaluation shows that SDA, when compared with traditional enterprise networks, can (i) reduce overall data plane forwarding state up to 70% thanks to a reactive protocol using a centralized routing server, and (ii) reduce by an order of magnitude the handover delays in scenarios of massive mobility with respect to other approaches. Finally, we discuss lessons learned while deploying and operating SDA, and possible optimizations regarding the use of an event-driven protocol and group-based segmentation., This work was partially supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

State-of-the-art, flat structures called metasurfaces can filter and steer light and sound, render an object completely invisible to electromagnetic waves, and much more. They can deliver automation, remote operation, and advanced performance to a wide variety of existing systems, with applications in communications, medical imaging, sensing, and security. However, for non-specialists, individual metasurfaces are currently restricted to limited reusability and accessibility. This book brings together various scientific disciplines with the aim of outlining a programmable 'plug-and-play' metasurface. The book focuses on a recently proposed platform - known as the HyperSurface - that provides many electromagnetic functions of metasurfaces in a single structure, which can be controlled and reconfigured by software. This revolutionary approach paves the way for new opportunities in wireless communications and programmable wireless environments: HyperSurfaces could link networks with objects and physical environments and create smarter systems that are far more responsive to user demands. Walls that absorb radiation or block digital eavesdropping, and wireless, long-distance charging of devices are among the many possibilities. The book aspires to provide the foundational knowledge for creating an Internet of Materials, enabling smart environments at any scale - from indoor wireless communications to medical imaging equipment. Although the set of disciplines involved covers a considerable span, we hope that the material will benefit experts and students alike., Peer Reviewed, Postprint (published version)

Premi extraordinari doctorat UPC curs 2019-2020, àmbit d’Enginyeria de les TIC, Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision. In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and – as a result – proposes a novel architecture that relies on Software-Defined Networking (SDN) and modern network analytics techniques to facilitate the deployment of ML-based solutions for efficient network operation. This dissertation aims to be a step forward in the realization of Knowledge-Defined Networks. In particular, we focus on the application of AI techniques to control and optimize networks more efficiently and automatically. To this end, we identify two components within the KDN context whose development may be crucial to achieve self-operating networks in the future: (i) the automatic control module, and (ii) the network analytics platform. The first part of this thesis is devoted to the construction of efficient automatic control modules. First, we explore the application of Deep Reinforcement Learning (DRL) algorithms to optimize the routing configuration in networks. DRL has recently demonstrated an outstanding capability to solve efficiently decision-making problems in other fields. However, first DRL-based attempts to optimize routing in networks have failed to achieve good results, often under-performing traditional heuristics. In contrast to previous DRL-based solutions, we propose a more elaborate network representation that facilitates DRL agents to learn efficient routing strategies. Our evaluation results show that DRL agents using the proposed representation achieve better performance and learn faster how to route traffic in an Optical Transpo, La evolución del campo del Aprendizaje Maquina (ML) en la última década ha dado lugar a una nueva era de la Inteligencia Artificial (AI). En concreto, algunos avances en el campo del Aprendizaje Profundo (DL) han permitido desarrollar nuevas herramientas de modelado y optimización con múltiples aplicaciones en campos como el procesado de lenguaje natural, o la visión artificial. En este contexto, el paradigma de Redes Definidas por Conocimiento (KDN) destaca la falta de adopción de técnicas de AI en redes y, como resultado, propone una nueva arquitectura basada en Redes Definidas por Software (SDN) y en técnicas modernas de análisis de red para facilitar el despliegue de soluciones basadas en ML. Esta tesis pretende representar un avance en la realización de redes basadas en KDN. En particular, investiga la aplicación de técnicas de AI para operar las redes de forma más eficiente y automática. Para ello, identificamos dos componentes en el contexto de KDN cuyo desarrollo puede resultar esencial para conseguir redes operadas autónomamente en el futuro: (i) el módulo de control automático y (ii) la plataforma de análisis de red. La primera parte de esta tesis aborda la construcción del módulo de control automático. En primer lugar, se explora el uso de algoritmos de Aprendizaje Profundo por Refuerzo (DRL) para optimizar el encaminamiento de tráfico en redes. DRL ha demostrado una capacidad sobresaliente para resolver problemas de toma de decisiones en otros campos. Sin embargo, los primeros trabajos que han aplicado DRL a la optimización del encaminamiento en redes no han conseguido rendimientos satisfactorios. Frente a dichas soluciones previas, proponemos una representación más elaborada de la red que facilita a los agentes DRL aprender estrategias de encaminamiento eficientes. Nuestra evaluación muestra que cuando los agentes DRL utilizan la representación propuesta logran mayor rendimiento y aprenden más rápido cómo encaminar el tráfico en un caso práctico en Rede, Award-winning, Postprint (published version)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The authors gratefully acknowledge support from the Spanish MINECO under grant PCIN-2015-012, from the EU’s H2020 FET-OPEN program under grants No. 736876 and No. 863337, and by the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning., This work was supported in part by the Polish Ministryof Science and Higher Education with the subvention funds of the Facultyof Computer Science, Electronics and Telecommunications, AGH University,in part by the Spanish MINECO under Contract TEC2017-90034-C2-1-R(ALLIANCE), in part by the Catalan Institution for Research and AdvancedStudies (ICREA) and the FI-AGAUR Grant by the Catalan Government, andin part by PL-Grid Infrastructure., Peer Reviewed, Postprint (author's final draft)

Recent emergence of metasurfaces has enabled the development of ultra-thin flat optical components through different wavefront shaping techniques at various wavelengths. However, due to the non-adaptive nature of conventional metasurfaces, the focal point of the resulting optics needs to be fixed at the design stage, thus severely limiting its reconfigurability and applicability. In this paper, we aim to overcome such constraint by presenting a flat reflective component that can be reprogrammed to focus terahertz waves at a desired point in the near-field region. To this end, we first propose a graphene-based unit cell with phase reconfigurability, and then employ the coding metasurface approach to draw the phase profile required to set the focus on the target point. Our results show that the proposed component can operate close to the diffraction limit with high focusing range and low focusing error. We also demonstrate that, through appropriate automation, the reprogrammability of the metamirror could be leveraged to develop compact terahertz scanning and imaging systems, as well as novel reconfigurable components for terahertz wireless communications., Peer Reviewed, Postprint (published version)

El projecte consisteix a en la cerca de la viabilitat i el desenvolupament d'una aplicació que permeti executar Open Overlay Router sobre el sistema operatiu Apple iOS. Open Overlay Router és un implementació de codi amb llicencia Apache 2.0 per crear xarxes de overlay programables, esta disponible per als sistemes operatius Android, Linux i OpenWrt. Amb aquest projecte incorporarem Apple iOS a la llista de sistemes operatius suportats., This project consist of the feasibility investigation and the development of an application that allows to run Open Overlay Router on the Apple iOS operating System. Open Overlay Router is an implementation of code with Apache 2.0 license that allows you to create programable overlay networks. Is available for Android, Linux and OpenWRT operating Systems. With this project we will incorporate Apple iOS to the list of supported operating systems.

Metasurfaces, the two-dimensional counterpart of metamaterials, have caught great attention thanks to their powerful control over electromagnetic waves. Recent times have seen the emergence of a variety of metasurfaces exhibiting not only countless functionalities, but also a reconfigurable or even programmable response. Reconfigurability, however, entails the integration of tuning and control circuits within the metasurface structure and, as this new paradigm moves forward, new reliability challenges may arise. This paper examines, for the first time, the reliability problem in programmable metamaterials by proposing an error model and a general methodology for error analysis. To derive the error model, the causes and potential impact of faults are identified and discussed qualitatively. The methodology is presented and instantiated for beam steering, which constitutes a relevant example for programmable metasurfaces. Results show that performance degradation depends on the type of error and its spatial distribution and that, in beam steering, error rates over 10% can still be considered acceptable., This work has been supported by the European Commission under grant H2020-FETOPEN-736876 (VISORSURF) and by ICREA under the ICREA Academia programme., Peer Reviewed, Postprint (author's final draft)

Metasurfaces (MS) constitute effective media for manipulating and transforming impinging EM waves. Related studies have explored a series of impactful MS capabilities and applications in areas such as wireless communications, medical imaging and energy harvesting. A key-gap in the existing body of work is that the attributes of the EM waves to-be-controlled (e.g., direction, polarization, phase) are known in advance. The present work proposes a solution to the EM wave sensing problem using the intelligent and networked MS counterparts-the HyperSurfaces (HSFs), without requiring dedicated field sensors. An nano-network embedded within the HSF iterates over the possible MS configurations, finding the one that fully absorbs the impinging EM wave, hence maximizing the energy distribution within the HSF. Using a distributed consensus approach, the nano-network then matches the found configuration to the most probable EM wave traits, via a static lookup table that can be created during the HSF manufacturing. Realistic simulations demonstrate the potential of the novel scheme. Moreover, we show that the proposed workflow is also an ambient EM compiler, i.e., an autonomic HSF that translates high-level EM objectives to corresponding, low-level EM actuation commands., This work was funded by the European Union via the Horizon 2020: Future Emerging Topics call (FETOPEN), grant EU736876, project VISORSURF (http://www.visorsurf.eu)., Peer Reviewed, Postprint (author's final draft)

Metasurfaces, the two-dimensional counterpart of metamaterials, have caught great attention thanks to their powerful capabilities on manipulation of electromagnetic waves. Recent times have seen the emergence of a variety of metasurfaces exhibiting not only countless functionalities, but also a reconfigurable response. Additionally, digital or coding metasurfaces have revolutionized the field by describing the device as a matrix of discrete building block states, thus drawing clear parallelisms with information theory and opening new ways to model, compose, and (re)program advanced metasurfaces. This paper joins the reconfigurable and digital approaches, and presents a metasurface that leverages the tunability of graphene to perform beam steering at terahertz frequencies. A comprehensive design methodology is presented encompassing technological, unit cell design, digital metamaterial synthesis, and programmability aspects. By setting up and dynamically adjusting a phase gradient along the metasurface plane, the resulting device achieves beam steering at all practical directions. The proposed design is studied through analytical models and validated numerically, showing beam widths and steering errors well below 10° and 5% in most cases. Finally, design guidelines are extracted through a scalability analysis involving the metasurface size and number of unit cell states., This work was supported in part by the Iran’s National Elites Foundation (INEF), in part by the Spanish Ministry of Economía y Competitividad under Grant PCIN-2015-012, and in part by ICREA under the ICREA Academia programme., Peer Reviewed, Postprint (author's final draft)