Study of Repair Protocols for Live Video Streaming Distributed Systems

International audience—We study distributed systems for live video streaming. These systems can be of two types: structured and un-structured. In an unstructured system, the diffusion is done opportunistically. The advantage is that it handles churn, that is the arrival and departure of users, which is very high in live streaming systems, in a smooth way. On the opposite, in a structured system, the diffusion of the video is done using explicit diffusion trees. The advantage is that the diffusion is very efficient, but the structure is broken by the churn. In this paper, we propose simple distributed repair protocols to maintain, under churn, the diffusion tree of a structured streaming system. We study these protocols using formal analysis and simulation. In particular, we provide an estimation of the system metrics, bandwidth usage, delay, or number of interruptions of the streaming. Our work shows that structured streaming systems can be efficient and resistant to churn

Order statistics and estimating cardinalities of massive data sets

International audienceA new class of algorithms to estimate the cardinality of very large multisets using constant memory and doing only one pass on the data is introduced here. It is based on order statistics rather than on bit patterns in binary representations of numbers. Three families of estimators are analyzed. They attain a standard error of using M units of storage, which places them in the same class as the best known algorithms so far. The algorithms have a very simple internal loop, which gives them an advantage in terms of processing speed. For instance, a memory of only 12 kB and only few seconds are sufficient to process a multiset with several million elements and to build an estimate with accuracy of order 2 percent. The algorithms are validated both by mathematical analysis and by experimentations on real internet traffic., OPTx-editorial-board=yes, OPTx-proceedings=yes, OPTx-international-audience=yes

Graphs with optimal forwarding indices: What is the best throughput you can get with a given number of edges?

The (edge) forwarding index of a graph is the minimum, over all possible routings of all the demands, of the maximum load of an edge. This metric is of a great interest since it captures the notion of global congestion in a precise way: the lesser the forwarding-index, the lesser the congestion. In this paper, we study the following design question: Given a number e of edges and a number n of vertices, what is the least congested graph that we can construct? and what forwarding-index can we achieve? Our problem has some distant similarities with the well-known (∆,D) problem, and we sometimes build upon results obtained on it. The goal of this paper is to study how to build graphs with low forwarding indices and to understand how the number of edges impacts the forwarding index. We answer here these questions for different families of graphs: general graphs, graphs with bounded degree, sparse graphs with a small number of edges by providing constructions, most of them asymptotically optimal. Hence, our results allow to understand how the forwarding-index drops when edges are added to a graph and also to determine what is the best (i.e least congested) structure with e edges. Doing so, we partially answer the practical problem that initially motivated our work: If an operator wants to power only e links of its network, in order to reduce the energy consumption (or wiring cost) of its networks, what should be those links and what performance can be expected

Sur la complexité du routage OSPF

International audienceCe travail montre que dans un réseau (général) où le protocole de routage est OSPF avec la stratégie d'équilibrage de charge ECMP, le problème qui consiste à maximiser un flot simple d'une source vers un puits ne peut être approché à une constante près

Nadege: When Graph Kernels meet Network Anomaly Detection

International audienceWith the continuous growing level of dynamicity, heterogeneity, and complexity of traffic data, anomaly detection remains one of the most critical tasks to ensure an efficient and flexible management of a network. Recently, driven by their empirical success in many domains, especially bioinformatics and computer vision, graph kernels have attracted increasing attention. Our work aims at investigating their discrimination power for detecting vulnerabilities and distilling traffic in the field of networking. In this paper, we propose Nadege, a new graph-based learning framework which aims at preventing anomalies from disrupting the network while providing assistance for traffic monitoring. Specifically, we design a graph kernel tailored for network profiling by leveraging propagation schemes which regularly adapt to contextual patterns. Moreover, we provide provably efficient algorithms and consider both offline and online detection policies. Finally, we demonstrate the potential of kernel-based models by conducting extensive experiments on a wide variety of network environments. Under different usage scenarios, Nadege significantly outperforms all baseline approaches

Preferential attachment hypergraph with high modularity

Numerous works have been proposed to generate random graphs preserving the same properties as real-life large scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist and no general model allows to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present a dynamic preferential attachment hypergraph model which features partition into communities. We prove that its degree distribution follows a power-law and we give theoretical lower bounds for its modularity. We compare its characteristics with a real-life co-authorship network and show that our model achieves good performances. We believe that our hypergraph model will be an interesting tool that may be used in many research domains in order to reflect better real-life phenomena

Une rencontre entre les noyaux de graphes et la détection d’anomalies dans les réseaux

International audienceLa détection d’anomalies demeure une tâche cruciale pour assurer une gestion efficace et flexible d’un réseau. Récemment, les noyaux de graphes ont connu un grand succès dans de nombreux domaines, notamment en bio-informatique et vision artificielle. Notre travail vise à étudier leur pouvoir de discrimination dans le domaine des réseaux afin de détecter les vulnérabilités et catégoriser le trafic. Dans cet article, nous présentons Nadege, un système d’apprentissage à l’intérieur duquel nous concevons un nouveau noyau de graphe adapté au profilage de réseaux. De surcroît, nousproposons des algorithmes avec des garanties d’approximation théoriques ainsi qu’une politique de détection hybride. Finalement, nous évaluons les performances de Nadege en menant des expériences approfondies sur une variété d’environnements réseaux. Pour différents scénarios, nous montrons son efficacité à empêcher les anomalies de perturber le réseau tout en fournissant une assistance pour la surveillance du trafic

On the Complexity of Compressing Two Dimensional Routing Tables with Order

International audienceMotivated by routing in telecommunication network using Software Defined Network (SDN) technologies, we consider the following problem of finding short routing lists using aggregation rules. We are given a set of communications X , which are distinct pairs (s, t) ⊆ S × T , (typically S is the set of sources and T the set of destinations), and a port function π : X → P where P is the set of ports. A routing list R is an ordered list of triples which are of the form (s, t, p), If r(s, t) = π(s, t), then we say that (s, t) is properly routed by R and if all communications of X are properly routed, we say that R emulates (X , π). The aim is to find a shortest routing list emulating (X , π). In this paper, we carry out a study of the complexity of the two dual decision problems associated to it. Given a set of communication X , a port function π and an integer k, the A preliminary short version of this work has appeared in [7]. 2 Frédéric Giroire et al. first one called Routing List (resp. the second one, called List Reduction) consists in deciding whether there is a routing list emulating (X , π) of size at most k (resp. |X | − k). We prove that both problems are NP-complete. We then give a 3-approximation for List Reduction, which can be generalized to higher dimensions. We also give a 4-approximation for Routing List in the fundamental case when there are only two ports (i.e. |P | = 2), X = S × T and |S| = |T |

Study of Repair Protocols for Live Video Streaming Distributed Systems

International audience—We study distributed systems for live video streaming. These systems can be of two types: structured and un-structured. In an unstructured system, the diffusion is done opportunistically. The advantage is that it handles churn, that is the arrival and departure of users, which is very high in live streaming systems, in a smooth way. On the opposite, in a structured system, the diffusion of the video is done using explicit diffusion trees. The advantage is that the diffusion is very efficient, but the structure is broken by the churn. In this paper, we propose simple distributed repair protocols to maintain, under churn, the diffusion tree of a structured streaming system. We study these protocols using formal analysis and simulation. In particular, we provide an estimation of the system metrics, bandwidth usage, delay, or number of interruptions of the streaming. Our work shows that structured streaming systems can be efficient and resistant to churn