Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

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

Traffic Receipts for Network Transparency

Today's Internet is not transparent: when packets get lost or delayed, there is typically no information about where the problem occurred, hence no information about who is responsible. This results in Internet service providers (ISPs) offering service level agreements (SLAs) that cannot be verified, and governments enacting neutrality regulations that cannot be enforced. To remedy this, we propose a "transparency system," where each participating network emits receipts for traffic it receives and delivers; an independent monitor collects these receipts and makes decisions regarding the network's performance and neutrality (or lack thereof). The main challenge we face is misbehavior: On the one hand, a network that participates in such a system has a clear incentive to game the system and influence the monitor's decisions to its advantage, by manipulating either the receipts it emits or the corresponding traffic. On the other hand, the monitor (or, more precisely, an adversary who has access to the same information as the monitor, e.g., a government that has subpoenaed the monitor's records) may have an incentive to use the receipts emitted by a network in order to infer information that is otherwise private to the network, in particular, its internal topology. We make three contributions, each one to prevent a different type of misbehavior: (1) Incentive-compatible reporting, which ensures that networks have no incentive to manipulate the receipts they emit in order to claim better performance or fake neutrality. The key to our solution is a trade-off that we discover between network performance and neutrality: we design our system such that the more a network tries to exaggerate its estimated performance the more likely it is to be perceived to violate neutrality (and vice versa). (2) Unbiased reporting, which ensures that networks cannot manipulate the traffic for which they emit receipts in order to claim better performance. The key to our solution is delayed disclosure: we design receipt generation such that, by the time a network has all the information it needs to emit a correct receipt, the network has already forwarded the traffic that this receipt concerns, hence cannot manipulate it. (3) Topology-obfuscation reporting, which enables networks to emit the information that is necessary for the monitor to make correct decisions without leaking any information about internal network topology. The key to our solution is the observation that topology inference exploits the diversity of pairwise similarities between the delay vectors of different network paths; hence, we design receipt generation such that any delay vectors that the monitor might compute have almost 0 pairwise similarities. We conclude that it is possible to design a transparency system that enables networks to report on their own performance such that networks have no incentive to game the system and no fear of leaking information about their private topology

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

Doctor of Philosophy

dissertationWe are seeing an extensive proliferation of wireless devices including various types and forms of sensor nodes that are increasingly becoming ingrained in our daily lives. There has been a significant growth in wireless devices capabilities as well. This proliferation and rapid growth of wireless devices and their capabilities has led to the development of many distributed sensing and computing applications. In this dissertation, we propose and evaluate novel, efficient approaches for localization and computation offloading that harness distributed sensing and computing in wireless networks. In a significant part of this dissertation, we exploit distributed sensing to create efficient localization applications. First, using the sensing power of a set of Radio frequency (RF) sensors, we propose energy efficient approaches for target tracking application. Second, leveraging the sensing power of a distributed set of existing wireless devices, e.g., smartphones, internet-of-things devices, laptops, and modems, etc., we propose a novel approach to locate spectrum offenders. Third, we build efficient sampling approaches to select mobile sensing devices required for spectrum offenders localization. We also enhance our sampling approaches to take into account selfish behaviors of mobile devices. Finally, we investigate an attack on location privacy where the location of people moving inside a private area can be inferred using the radio characteristics of wireless links that are leaked by legitimate transmitters deployed inside the private area, and develop the first solution to mitigate this attack. While we focus on harnessing distributed sensing for localization in a big part of this dissertation, in the remaining part of this dissertation, we harness the computing power of nearby wireless devices for a computation offloading application. Specially, we propose a multidimensional auction for allocating the tasks of a job among nearby mobile devices based on their computational capabilities and also the cost of computation at these devices with the goal of reducing the overall job completion time and being beneficial to all the parties involved