Unreliable failure detectors for asynchronous distributed systems

Distributed computing is very attractive, but comes with new problems : information losses, overflow, or breakdowns. Most often, they are neglected. Indeed, it has been shown that the Consensus (a fundamental problem which requires that the processes agree on a common value) is unsolvable in a realistic computing model, i.e. completely asynchronous with possible crash failures [FLP85]. Intuitively, in an asynchronous environment, a process cannot decide if a component is either crashed or very slow. Several approaches were designed to “bypass” that impossibility. One of them is self-stabilization, studied at LaRIA, which deals with transient faults. The principle is to design algorithms which can be executed from any initial state, and eventually work according to its specification. Snap-stabilization is stronger : from any initial step, the algorithm always behaves according to its specification. The first snap-stabilized algorithms were designed at LaRIA. Another approach, which we are going to study, cope with definitive (crash) failures. Ideally, a black box should be attached to each process to indicate precisely the failures of the network. This black box is called a failure detector. But, the result of [FLP85] implies that it is impossible to implement such a perfect failure detector. That is why Chandra and Toueg introduces in [CHT96] the notion of unreliable failure detectors. Even if such detectors are still impossible to implement, practically, this approach allows to implement semi-algorithms. Theoretically, this approach also allows to introduce a hierarchy of the unreliable