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

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

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

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

Routing Games for Traffic Engineering

Current data network scenario makes traffic engineering (TE) a very challenging task. The ever growing access rates and new applications running on end-hosts result in more variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, load-balancing has proved itself an excellent tool to face this uncertainty. In particular, mechanisms where routers greedily minimize a path cost function (thus requiring minimum coordination) have been studied from a game-theoretic perspective in what is known as a routing game (RG). The contribution of this paper is twofold. We first propose a new RG specifically designed for elastic traffic, where we maximize the total utility through load-balancing only. Secondly, we consider several important RGs from a TE perspective and, using several real topologies and traffic demands, present a thorough comparison of their performance. This paper brings insight into several RGs, which will help one in choosing an adequate dynamic load-balancing mechanism. The comparison shows that the performance gain of the proposed game can be important

Minimum delay load balancing through non parametric regression

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, load-balancing has proved itself an excellent tool to face this uncertainty. Formally, load-balancing is defined in terms of a convex link cost function of its load, where the objective is to minimize the total cost. Typically, the link queueing delay is used as this cost since it measures its congestion. Over-simplistic models are used to calculate it, which have been observed to result in suboptimal resource usage and total delay. In this paper we investigate the possibility of learning the delay function from measurements, thus converging to the actual minimum. A novel regression method is used to make the estimation, restricting the assumptions to the minimum (e.g. delay should increase with load). The framework is relatively simple to implement, and we discuss some possible variants. Document type: Part of book or chapter of boo