Vincent Villain

When graph theory helps self-stabilization

We propose a general self-stabilizing scheme for solving any synchronization problem whose safety specification can be defined using a local property. We demonstrate the versatility of our scheme by showing that very memory-efficient solutions to many well-known problems (e.g., asynchronous phase clock, local mutual exclusion, local reader-writers, and local group mutual exclusion) can be derived using the proposed framework. We show that all these algorithms use a phase clock whose minimum size in terms of number of states per process is equal to CG + TG - 1, where CG is the length of the maximal cycle of the shortest maximum cycle basis if the graph contains cycles and 2 (otherwise) for tree networks, and TG is the length of the longest chordless cycle (i.e., hole) if the graph contains cycles and 2 for tree networks. In particular, for the asynchronous phase clock problem, our solution significantly improves all existing self-stabilizing solutions---all of them require quadratic space in terms of the number of states.As a by-product of our scheme, we present a silent bounded algorithm which can be used to transform any serial system into a distributed one. Thus, it answers an open question in [16], if there exists a bounded system transformation which is silent.

Snap-stabilization and PIF in tree networks

The contribution of this paper is threefold. First, we present the paradigm of snap-stabilization. A snap- stabilizing protocol guarantees that, starting from an arbitrary system configuration, the protocol always behaves according to its specification. So, a snap-stabilizing protocol is a time optimal self-stabilizing protocol (because it stabilizes in 0 rounds). Second, we propose a new Propagation of Information with Feedback (PIF) cycle, called Propagation of Information with Feedback and Cleaning (

How to meet asynchronously at polynomial cost

\mathcal{PFC}

A self-stabilizing link-cluster algorithm in mobile ad hoc networks

). We show three different implementations of this new PIF. The first one is a basic

Snap-stabilizing PIF and useless computations

\mathcal{PFC}

Snap-Stabilizing optimal binary search tree

cycle which is inherently snap-stabilizing. However, the first PIF cycle can be delayed O(h2) rounds (where h is the height of the tree) due to some undesirable local states. The second algorithm improves the worst delay of the basic