Fabien Mathieu

Distributedly Testing Cycle-Freeness

We tackle local distributed testing of graph properties. This framework is well suited to contexts in which data dispersed among the nodes of a network can be collected by some central authority (like in, e.g., sensor networks). In local distributed testing, each node can provide the central authority with just a few information about what it perceives from its neighboring environment, and, based on the collected information, the central authority is aiming at deciding whether or not the network satisfies some property. We analyze in depth the prominent example of checking cycle-freeness, and establish tight bounds on the amount of information to be transferred by each node to the central authority for deciding cycle-freeness. In particular, we show that distributedly testing cycle-freeness requires at least (lceil log d rceil -1) bits of information per node in graphs with maximum degree (d), even for connected graphs. Our proof is based on a novel version of the seminal result by Naor and Stockmeyer (1995) enabling to reduce the study of certain kinds of algorithms to order-invariant algorithms, and on an appropriate use of the known fact that every free group can be linearly ordered.

Four months in daily motion: Dissecting user video requests

The growth of User-Generated Content (UGC) traffic makes the understanding of its nature a priority for network operators, content providers and equipment suppliers. In this paper, we study a four-month dataset that logs all video requests to DailyMotion made by a fixed subset of users. We were able to infer user sessions from raw data, to propose a Markovian model of these sessions, and to study video popularity and its evolution over time. The presented results are a first step for synthesizing an artificial (but realistic) traffic that could be used in simulations or experimental testbeds.

Peeking through the BitTorrent Seedbox Hosting Ecosystem

In this paper, we propose a lightweight method for detecting and classifying BitTorrent content providers with a minimal amount of resources. While heavy methodologies are typically used (which require long term observation and data exchange with peers of the swarm and/or a semantic analysis of torrent websites), we instead argue that such complexity can be avoided by analyzing the correlations between peers and torrents. We apply our methodology to study over 50K torrents injected in ThePirateBay during one month, collecting more than 400K IPs addresses. Shortly, we find that exploiting the correlations not only enhances the classification accuracy keeping the technique lightweight (our methodology reliably identifies about 150 seedboxes), but also uncovers seeding behaviors that were not previously noticed (e.g., as multi-port and multi-host seeding). Finally, we correlate the popularity of seedbox hosting in our dataset to criteria (e.g., cost, storage space, Web popularity) that can bias the selection process of BitTorrent content providers.

On the manipulability of voting systems: application to multi-operator networks

Internet is a large-scale and highly competitive economic ecosystem. In order to make fair decisions, while preventing the economic actors from manipulating the natural outcome of the decision process, game theory is a natural framework, and voting systems represent an interesting alternative that, to our knowledge, has not yet being considered. They allow competing entities to decide among different options. In this paper, we investigate their use for end-to-end path selection in multi-operator networks, analyzing their manipulability by tactical voting and their economic efficiency.We show that Instant Runoff Voting is much more efficient and resistant to tactical voting than the natural system which tries to get the economic optimum.

Semi-distributed Demand Response Solutions for Smart Homes

The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. It enables visibility and control on the various appliances that may consume, store or generate energy within a home. It has been shown that a centralized control on the appliances of a set of households leads to efficient DR mechanisms; unfortunately, such solutions raise privacy and scalability issues. In this chapter we propose an approach that deals with these issues. Specifically, we introduce a scalable two-levels control system where a centralized controller allocates power to each house on one side and, each household implements a DR local solution on the other side. A limited feedback to the centralized controller allows to enhance the performance with little impact on privacy. The solution is proposed for the general framework of capacity markets.

SDN-based Wi-Fi Direct Clustering for Cloud Access in Campus Networks

Mobile cloud is changing the way to enroll teaching activities in a university campus. Lectures and lab sessions can be carried out directly from tablets in a classroom by accessing a server in the cloud. In this paper, we address the problem of high-density cloud access with wireless devices in campus networks. We propose to use Wi-Fi direct clustering to solve the problem of quality of service (QoS) degradation when a high number of wireless devices want to access a content in the cloud at the same time. A centralized software-defined network controller is used in our proposed architecture to capture the network state and organize the Wi-Fi Direct groups. The optimized number of clusters can be calculated in function of the number of devices in the room. By simulations, we show that we can provide a better QoS in terms of download time and application’s throughput by reducing the interference in this dense wireless network environment.

Performance of Balanced Fairness in Resource Pools: A Recursive Approach

Understanding the performance of a pool of servers is crucial for proper dimensioning. One of the main challenges is to take into account the complex interactions between servers that are pooled to process jobs. In particular, a job can generally not be processed by any server of the cluster due to various constraints like data locality. In this paper, we represent these constraints by some assignment graph between jobs and servers. We present a recursive approach to computing performance metrics like mean response times when the server capacities are shared according to balanced fairness. While the computational cost of these formulas can be exponential in the number of servers in the worst case, we illustrate their practical interest by introducing broad classes of pool structures that can be exactly analyzed in polynomial time. This extends considerably the class of models for which explicit performance metrics are accessible.

Mutual service processes in Euclidean spaces: existence and ergodicity

Consider a set of objects, abstracted to points of a spatially stationary point process in

Advanced demand response solutions for capacity markets

mathbb {R}^d