Active Learning of Multiple Source Multiple Destination Topologies

We consider the problem of inferring the topology of a network with $M$ sources and $N$ receivers (hereafter referred to as an $M$-by-$N$ network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., $1$-by-$N$'s or $2$-by-$2$'s) and then merge these components to identify the $M$-by-$N$ topology. In this paper, we focus on the second part, which had previously received less attention in the literature. In particular, we assume that a $1$-by-$N$ topology is given and that all $2$-by-$2$ components can be queried and learned using end-to-end probes. The problem is which $2$-by-$2$'s to query and how to merge them with the given $1$-by-$N$, so as to exactly identify the $2$-by-$N$ topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, $\lceil \frac{N}{2} \rceil$, on the number of $2$-by-$2$'s required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of multiple hypothesis testing, in particular Generalized Binary Search (GBS), since our problem is one of active learning, from $2$-by-$2$ queries. The second algorithm is called the Receiver Elimination Algorithm (REA) and follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding $2$-by-$2$, and merges it with the given $1$-by-$N$; it requires exactly $N-1$ steps, which is much less than all $\binom{N}{2}$ possible $2$-by-$2$'s. Simulation results over synthetic and realistic topologies demonstrate that both algorithms correctly identify the $2$-by-$N$ topology and are near-optimal, but REA is more efficient in practice

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Practical Approach to Identifying Additive Link Metrics with Shortest Path Routing

© 2015 IEEE. We revisit the problem of identifying link metrics from end- to-end path measurements in practical IP networks where shortest path routing is the norm. Previous solutions rely on explicit routing techniques (e.g., source routing or MPLS) to construct independent measurement paths for efficient link metric identification. However, most IP networks still adopt shortest path routing paradigm, while the explicit routing is not supported by most of the routers. Thus, this paper studies the link metric identification problem under shortest path routing constraints. To uniquely identify the link metrics, we need to place sufficient number of monitors into the network such that there exist m (the number of links) linear independent shortest paths between the monitors. In this paper, we first formulate the problem as a mixed integer linear programming problem, and then to make the problem tractable in large networks, we propose a Monitor Placement and Measurement Path Selection (MP-MPS) algorithm that adheres to shortest path routing constraints. Extensive simulations on random and real networks show that the MP- MPS gets near-optimal solutions in small networks, and MP- MPS significantly outperforms a baseline solution in large networks

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-provider-independent overlays for resilience and quality of service.

PhDOverlay networks are viewed as one of the solutions addressing the inefficiency and slow evolution of the Internet and have been the subject of significant research. Most existing overlays providing resilience and/or Quality of Service (QoS) need cooperation among different network providers, but an inter-trust issue arises and cannot be easily solved. In this thesis, we mainly focus on network-provider-independent overlays and investigate their performance in providing two different types of service. Specifically, this thesis addresses the following problems: Provider-independent overlay architecture: A provider-independent overlay framework named Resilient Overlay for Mission-Critical Applications (ROMCA) is proposed. We elaborate its structure including component composition and functions and also provide several operational examples. Overlay topology construction for providing resilience service: We investigate the topology design problem of provider-independent overlays aiming to provide resilience service. To be more specific, based on the ROMCA framework, we formulate this problem mathematically and prove its NP-hardness. Three heuristics are proposed and extensive simulations are carried out to verify their effectiveness. Application mapping with resilience and QoS guarantees: Assuming application mapping is the targeted service for ROMCA, we formulate this problem as an Integer Linear Program (ILP). Moreover, a simple but effective heuristic is proposed to address this issue in a time-efficient manner. Simulations with both synthetic and real networks prove the superiority of both solutions over existing ones. Substrate topology information availability and the impact of its accuracy on overlay performance: Based on our survey that summarizes the methodologies available for inferring the selective substrate topology formed among a group of nodes through active probing, we find that such information is usually inaccurate and additional mechanisms are needed to secure a better inferred topology. Therefore, we examine the impact of inferred substrate topology accuracy on overlay performance given only inferred substrate topology information

Binder jetting: a microstructural perspective

The investigations carried out in this work aimed to accomplish the objectives cited in the Chapter 3. Therefore, the general conclusions are divided to address each proposed goal. 1. Develop automated characterization methods that allow for metrics subtraction and comparison between BJ green parts: • The lack of green part microstructural characterization in BJ 3D printing pointed towards the development of image treatment algorithms for SEM and OM micrographs and XCT volumes allowing the subtraction of relevant metrics for understanding BJ processes. • These algorithms were not only developed, but also employed in several studies, which proved their high sensitivity to a variety of BJ parameters, printed geometries, or sintering conditions. • The extracted metrics presented a high correlation with relevant process parameters, exhibiting a great potential for process predictability at different stages. 2. Contribute to the existing knowledge of BJ printing process through the addition of a deep characterization of green parts and their respective sintered parts outcome: • The Chapter 5 explored the relationship of BJ part with some printing parameters already available in the literature. However, the addition of a new perspective combining commonly seen characterization with the microstructural readings of the green parts allowed to get new insights into BJ printing mechanisms. 3. Investigate the impact of part geometry, size, and features on the resulting microstructure: • Part geometry was found to significantly influence the microstructural observations. A relationship was established between a variety of geometrical features and the resulting microstructural metrics. • The geometrical influences exploration emphasized the need for adaptative print modes that consider the previously mentioned relationships. 4. Advance in the understanding of the surface defects relationship with the printing recipe and its link to posterior machinability: • OM microstructural metrics were developed to characterize porosity gradients in BJ sintered parts. • Several process parameters were found to promote or hinder the formation of porosity in the sintered state. An optimization of these parameters was proposed. • The resulting variability of porosity metrics within the part also evidences the need for adaptative print modes the optimize porosity locally.Programa de Doctorado en Ciencia e Ingeniería de Materiales por la Universidad Carlos III de MadridPresidente: Íñigo Agote Beloki.- Secretaria: Paula Alvaredo Olmos.- Vocal: Gemma Herranz Sánchez-Cosgall