Techniques d'ingénierie de trafic dynamique pour l'internet

Network convergence and new applications running on end-hosts result in increasingly variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, Dynamic Load-Balancing (DLB) has proved itself an excellent tool to face this uncertainty. The objective in DLB is to distribute traffic among these paths in real-time so that a certain objective function is optimized. In these dynamic schemes, paths are established a priori and the amount of traffic sent through each of them depends on the current traffic demand and network condition. In this thesis we study and propose various DLB mechanisms, differing in two important aspects. The first difference resides in the assumption, or not, that resources are reserved for each path. The second lies on the objective function, which clearly dictates the performance obtained from the network. However, a performance benchmarking of the possible choices has not been carried out so far. In this sense, for the case in which no reservations are performed, we study and compare several objective functions, including a proposal of ours. We will also propose and study a new distributed algorithm to attain the optimum of these objective functions. Its advantage with respect to previous proposals is its complete self-configuration (i. E. Convergence is guaranteed without any parametrization). Finally, we present the first complete comparative study between DLB and Robust Routing (a fixed routing configuration for all possible traffic demands). In particular, we analyze which scheme is more convenient in each given situation, and highlight some of their respective shortcomings and virtues.Avec la multiplication des services dans un même réseau et les diversités des applications utilisées par les usagers finaux, le trafic transporté est devenu très complexe et dynamique. Le Partage de la Charge Dynamique (PCD) constitue une alternative intéressante pour résoudre cette problématique. Si une paire Source-Destination est connectée par plusieurs chemins, le problème est le suivant : comment distribuer le trafic parmi ces chemins de telle façon qu’une fonction objective soit optimisé. Dans ce cas les chemins sont fixés a priori et la quantité de trafic acheminée sur chaque route est déterminée dynamiquement en fonction de la demande de trafic et de la situation actuelle du réseau. Dans cette thèse nous étudions puis nous proposons plusieurs mécanismes de PCD. Tout d'abord, nous distinguons deux types d’architecture : celles dans lesquelles les ressources sont réservées pour chaque chemin, et celles pour lesquelles aucune réservation n'est effectuée. La simplification faite dans le premier type d’architecture nous permet de proposer l'utilisation d'un nouveau mécanisme pour gérer les chemins. Partant de ce mécanisme, nous définissons un nouvel algorithme de PCD. Concernant la deuxième architecture, nous étudions et comparons plusieurs fonctions objectives. À partir de notre étude, nous proposons un nouvel algorithme distribué permettant d’atteindre l'optimum de ces fonctions objectives. La principale caractéristique de notre algorithme, et son avantage par rapport aux propositions antérieures, est sa capacité d'auto-configuration, dans la mesure où la convergence de l'algorithme est garantie sans aucun besoin de réglage préalable de ses paramètres

A nation-wide wi-fi RSSI dataset : Statistical analysis and resulting insights.

We present a dataset collected during ten months from a network comprising approximately 9500 double-band Access Points (APs), corresponding to Uruguay’s nation-wide one-to-one computing program’s internet provider. The dataset includes the transmission power, used channel and measured RSSI (Radio Signal Strength Indicator) that each AP senses every other AP in sight, with a granularity of an hour. This results in a total of more than 750 million measurements, one of the largest Wi-Fi datasets to date. In the study of this dataset we have first focused on a linklevel analysis. Our contributions are fourfold. We verify that approximately only half of the RSSI time-series are actually stationary, and that in that case, they present strong time correlations. Moreover, the typical assumption that the channel is symmetrical is not true, even in the long-term, and we show that interference plays an important role on this asymmetry. Finally, we study attenuation in the 5 GHZ band and show that its upper section is prone to larger attenuation than what is predicted by classic models. The practical consequences of these observations are discussed throughout the article. We also present networklevel indicators of the system (such as number of neighbors per AP and interference level). These are particularly useful for simulating a planned network such as the one discussed here

On the application of graph neural networks for indoor positioning systems.

Due to the inability of GPS (or other GNSS methods) to provide satisfactory precision for the indoor location scenario, indoor positioning systems resort to other signals already available on site, typically Wi-Fi given its ubiquity. However, instead of relying on an error-prone propagation model as in ranging methods, the popular fingerprinting positioning technique considers a more direct data-driven approach to the problem. First of all, the area of interest is divided into zones, and then a machine learning algorithm is trained to map, for instance, power measurements (RSSI) from APs to the localization zone, thus effectively turning the problem into a classification one. However, although the positioning problem is a geometrical one, virtually all methods proposed in the literature disregard the underlying structure of the data, using generic machine learning algorithms. In this chapter we consider instead a graph-based learning method, Graph Neural Networks, a paradigm that has emerged in the last few years and that constitutes the state of the art for several problems. After presenting the pertinent theoretical background, we discuss two possibilities to construct the underlying graph for the positioning problem. We then perform a thorough evaluation of both possibilities and compare it with some of the most popular machine learning alternatives. The main conclusion is that these graph-based methods obtain systematically better results, particularly with regard to practical aspects (e.g., gracefully tolerating faulty APs), which makes them a serious candidate to consider when deploying positioning systems

Online change point detection for weighted and directed random dot product graphs

Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to endow sequential change-point detection (CPD) techniques with a graph representation learning substrate based on the versatile Random Dot Product Graph (RDPG) model. We consider efficient, online updates of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal RDPG. This reference distribution is inferred via spectral embeddings of the first few graphs in the sequence. We characterize the distribution of this running statistic to select thresholds that guarantee error-rate control, and under simplifying approximations we offer insights on the algorithm’s detection resolution and delay. The end result is a lightweight online CPD algorithm, that is also explainable by virtue of the well-appreciated interpretability of RDPG embeddings. This is in stark contrast with most existing graph CPD approaches, which either rely on extensive computation, or they store and process the entire observed time series. An apparent limitation of the RDPG model is its suitability for undirected and unweighted graphs only, a gap we aim to close here to broaden the scope of the CPD framework. Unlike previous proposals, our non-parametric RDPG model for weighted graphs does not require a priori specification of the weights’ distribution to perform inference and estimation. This network modeling contribution is of independent interest beyond CPD. We offer an open-source implementation of the novel online CPD algorithm for weighted and direct graphs, whose effectiveness and efficiency are demonstrated via (reproducible) synthetic and real network data experimentsWork in this paper is supported in part by ANII (grant FMV 3 2018 1 148149) and the NSF (awards CCF-1750428, CCF-1934962 and ECCS-1809356). Part of the results in this paper were submitted to the 2021 EUSIPCO and Asilomar Conference

ModelNet-TE : an emulation tool for the study of P2P and Traffic Engineering interaction dynamics

Este artículo ha sido aceptado para su inclusión en la revista Peer-to-Peer Networking and ApplicationsIn the Internet, user-level performance of P2P applications may be determined by the interaction of two independent dynamics: on the one hand, by the end-to-end control policies applied at the P2P application layer (L7), on the other hand, by Traffic Engineering (TE) decisions taken at the network level (L3). Currently available tools do not allow to study L7/L3 interactions in realistic settings due to a number of limitations. Building over ModelNet, we develop a framework for the real-time emulation of TE capabilities, named ModelNet-TE, that we make available to the scientific community as open source software. ModelNet-TE allows (i) to deploy real unmodified Internet P2P applications, and to test their interaction with (ii) many TE algorithms, as its design allows to easily integrate other TE algorithms than those we already provide, (iii) in a furthermore controlled network environment. Due to these features, ModelNet-TE is a complementary tool with respect to hybrid simulation/protoyping toolkits (that constrain application development to a specific language and framework, and cannot be used with existing or proprietary applications) and to other open testbeds such as PlanetLab or Grid5000 (lacking of control or TE-capabilities respectively). ModelNet-TE can thus be useful to L7-researchers, as it allows to seamlessly and transparently test any existing P2P application without requiring any software modification. At the same time, ModelNet-TE can be useful to L3-researchers as well, since they can test their TE algorithms on the traffic generated by real applications. As a use case, in this work we carry on an experimental campaign of L7/L3 routing layers interaction through ModelNet-TE. As TE we consider the classic minimum congestion load-balancing, that we compare against standard IP routing. As example P2P applications, we take BitTorrent, one among the most popular file-sharing applications nowadays, and WineStreamer, an open source live-streaming application. We emulate BitTorrent and WineStreamer swarms over both realistic topologies (e.g., Abilene) and simplistic topologies that are commonly in use today (e.g., where the bottleneck is located at the network edge) under a variety of scenarios. Results of our experimental campaign show that user-level performance may be significantly affected by both the TE mechanism in use at L3 (e.g., due to interactions with TCP congestion control or P2P chunk trading logic), as well as scenario parameters that are difficult to control in the wild Internet, which thus testifies the interest for tools such as ModelNet-TE

Gradient-Based Spectral Embeddings of Random Dot Product Graphs

The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the embedding task of estimating these vectors from an observed graph is typically posed as a low-rank matrix factorization problem. The workhorse Adjacency Spectral Embedding (ASE) enjoys solid statistical properties, but it is formally solving a surrogate problem and can be computationally intensive. In this paper, we bring to bear recent advances in non-convex optimization and demonstrate their impact to RDPG inference. We advocate first-order gradient descent methods to better solve the embedding problem, and to organically accommodate broader network embedding applications of practical relevance. Notably, we argue that RDPG embeddings of directed graphs loose interpretability unless the factor matrices are constrained to have orthogonal columns. We thus develop a novel feasible optimization method in the resulting manifold. The effectiveness of the graph representation learning framework is demonstrated on reproducible experiments with both synthetic and real network data. Our open-source algorithm implementations are scalable, and unlike the ASE they are robust to missing edge data and can track slowly-varying latent positions from streaming graphs

Robust Routing mechanisms for intradomain Traffic Engineering in dynamic networks

International audienceInternet traffic is highly dynamic and difficult to predict in current network scenarios. This makes of traffic engineering (TE) a very challenging task for network management and resources optimization. We study the problem of intradomain routing optimization under this traffic uncertainty. Recent works have proposed robust optimization techniques to tackle the problem, conceiving the robust routing (RR) approach. RR copes with traffic uncertainty in an off-line preemptive fashion, computing a single static routing configuration that is optimized for traffic variations within some predefined uncertainty set. Despite achieving routing reliability with relatively low performance loss, RR presents various drawbacks and conception problems as it is currently proposed. This paper brings insight into the different robust routing shortcomings, introducing new mechanisms that improve previous proposals and alleviate these problems. Among others, we propose and evaluate new optimization objectives to attain better global performance from an end-to-end quality of service perspective