Systems ” of PERSYVAL-LAB.

Scientific Context. Modern networks are very large-scale (about 100 000 nodes). Now, the more a network contains nodes, the greater the probability of failures is. Hence, today fault-tolerance is a main concern for distributed algorithm designers. In particular, since several decades, there is a growing interest for self-repairing methods. For example, (deterministic) self-stabilization [Dij74] is a versatile property, enabling a distributed algorithm to withstand transient faults in a network. Indeed, a self-stabilizing algorithm, after transient faults (e.g., memory corruptions, message losses, topological changes, etc.) hit and place the network in some arbitrary state, enables the network to recover without external (e.g., human) intervention in finite time. A main advantage of self-stabilization is its cost both in terms of time complexity and resource consump-tion. Indeed, classical robust algorithms follow a pessimistic approach, suspecting all received information, and consequently precede all steps by sufficient checks to guarantee that their specification is never vio-lated. In contrast, self-stabilization is considered as a lightweight optimistic approach, which assumes that processes operate correctly most of the time, but guarantees an eventual recovery after some of them fail. Self-stabilizing algorithms are also attractive for self-organizing systems, as they allow autonomous entities to converge to a distributed global structure such as a cluster or a spanning tree without requiring an