Ph.D
Group : Parallel Systems

Synchronization and Fault-tolerance in Distributed Algorithms

Starts on 03/10/2011
Advisor : BEAUQUIER, Joffroy
[DELAET Sylvie]

Funding : contrat doctoral DIGITEO
Affiliation : Université Paris-Saclay
Laboratory : LRI - Parallélisme

Defended on 24/09/2014, committee :

Research activities :

Abstract :
n the first part of this thesis, we focus on a recent model, called population protocols, which describes large networks of tiny wireless mobile anonymous agents with very limited resources. The harsh constraints of the original model makes most of the classical problems of distributed algorithmics, such as data collection, consensus and leader election, either difficult to analyze or impossible to solve.

We first study the data collection problem, which mainly consists in transferring some values to a base station. By using a fairness assumption, known as cover times, we compute tight bounds on the convergence time of concrete protocols. Next, we focus on the problems of consensus and leader election. It is shown that these problems are impossible in the original model. To circumvent these issues, we augment the original model with oracles, and study their relative power. We develop by the way a formal framework general enough to encompass various sorts of oracles, as well as their relations.

In the second part of the thesis, we study the problem of state-machine replication in the more classical model of asynchronous message-passing communication. The Paxos algorithm is a famous (partial) solution to the state-machine replication problem which tolerates crash failures. Our contribution is the enhancement of Paxos in order to tolerate transient faults as well. Doing so, we define the notion of practically self-stabilizing replicated state-machine.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.