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Algorithmes distribués efficaces adaptés à un contexte incertain

Authors: Durand, Anaïs;

Algorithmes distribués efficaces adaptés à un contexte incertain

Abstract

Les systèmes distribués sont de plus en plus grands et complexes, alors que leur utilisation s'étend à de nombreux domaines (par exemple, les communications, la domotique, la surveillance, le ``cloud''). Par conséquent, les contextes d'exécution des systèmes distribués sont très divers. Dans cette thèse, nous nous focalisons sur des contextes incertains, autrement dit, le contexte n'est pas complètement connu au départ ou il est changeant. Plus précisément, nous nous focalisons sur deux principaux types d'incertitudes : une identification incomplète des processus et la présence de fautes. L'absence d'identification est fréquente dans de grands réseaux composés d'appareils produits et déployés en masse. De plus, l'anonymat est souvent une demande pour la sécurité et la confidentialité. De la même façon, les grands réseaux sont exposés aux pannes comme la panne définitive d'un processus ou une perte de connexion sans fil. Néanmoins, le service fourni doit rester disponible.Cette thèse est composée de quatre contributions principales. Premièrement, nous étudions le problème de l'élection de leader dans les anneaux unidirectionnels de processus homonymes (les processus sont identifiés mais leur ID n'est pas forcément unique). Par la suite, nous proposons un algorithme d'élection de leader silencieux et autostabilisant pour tout réseau connecté. Il s'agit du premier algorithme fonctionnant sous de telles conditions qui stabilise en un nombre polynomial de pas de calcul. La troisième contribution est une nouvelle propriété de stabilisation conçue pour les réseaux dynamiques qui garantit des convergences rapides et progressives après des changements topologiques. Nous illustrons cette propriété avec un algorithme de synchronisation d'horloges. Finalement, nous considérons la question de la concurrence dans les problèmes d'allocation de ressources. En particulier, nous étudions le niveau de concurrence qui peut être atteint dans une grande classe de problèmes d'allocation de ressources, l'allocation de ressources locales.

Distributed systems become increasingly wide and complex, while their usage extends to various domains (e.g., communication, home automation, monitoring, cloud computing). Thus, distributed systems are executed in diverse contexts. In this thesis, we focus on uncertain contexts, i.e., the context is not completely known a priori or is unsettled. More precisely, we consider two main kinds of uncertainty: processes that are not completely identified and the presence of faults. The absence of identification is frequent in large networks composed of massively produced and deployed devices. In addition, anonymity is often required for security and privacy. Similarly, large networks are exposed to faults (e.g, process crashes, wireless connection drop), but the service must remain available.This thesis is composed of four main contributions. First, we study the leader election problem in unidirectional rings of homonym processes, i.e., processes are identified but their ID is not necessarily unique. Then, we propose a silent self-stabilizing leader election algorithm for arbitrary connected network. This is the first algorithm under such conditions that stabilizes in a polynomial number of steps. The third contribution is a new stabilizing property designed for dynamic networks that ensures fast and gradual convergences after topological changes. We illustrate this property with a clock synchronizing algorithm. Finally, we consider the issue of concurrency in resource allocation problems. In particular, we study the level of concurrency that can be achieved in a wide class of resource allocation problem, i.e., the local resource allocation.

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France
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Keywords

[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Fault-Tolerance, Dynamic networks, Autostabilisation, Réseaux dynamiques, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Anonymat, Distributed algorithms, Tolérance aux pannes, Algorithmes distribués, Anonymity, Self-Stabilization

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    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
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    impulse
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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