Ph.D
Group : Parallelism

Efficient Self-stabilization

Starts on 01/09/1995
Advisor : BEAUQUIER, Joffroy

Funding :
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 02/01/2000, committee :

Research activities :
   - Distributed algorithms
   - Self-stabilisation
   - Randomized algorithms
   - Graph Theory
   - Complexity

Abstract :
When a distributed system is subject to transient failures that arbitrarily modify its state, it is crucial to recover a correct behavior within finite time. Self-stabilization offers such a guarantee, but usually uses large resources. In this thesis, we focus on minimizing these resources when such solutions exist.

We introduced the concept of transient failure detectors, oracles that are called by processors, which notify if transient failures occurred within constant time. Our implementation enables classifying classic problems according to the specific resources dedicated to error detection.
A natural extension was to show that a local property on the local code executed by each processor is sufficient to guarantee self-stabilization of the whole system, whatever the computation assumptions and communication graph may be. Since the original algorithm is not modified, it is overhead-free. Similarly, we developed two synchronizers that enable dynamic tasks to be solved self-stabilizingly, whose memory overhead is constant. Moreover, one of them is snap-stabilizing. Finally, we presented a general technique to systematically reduce the communication
cost, assuming a bounded retransmission delay, and we gave a general framework and tools to design self-stabilizing algorithms in this context.

More information: http://tel.archives-ouvertes.fr/tel-00124843

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.