The Time-Complexity of Local Decision in Distributed Agreement

Agreement is at the heart of distributed computing. In its simple form, it requires a set of processes to decide on a common value out of the values they propose. The time-complexity of distributed agreement problems is generally measured in terms of the number of communication rounds needed to achieve a global decision; i.e., for all non-faulty (correct) processes to reach a decision. This paper studies the time-complexity of local decisions in agreement problems, which we de¯ne as the number of communication rounds needed for at least one correct process to decide. We explore bounds for early local decision, that depend on the number f of actual failures (that occur in a given run of an algorithm), out of the maximum number t of failures tolerated (by the algorithm). We ¯rst consider the synchronous message-passing model where we give tight local decision bounds for three variants of agreement: consensus, uniform consensus and (non-blocking) atomic commit. We use these results to (1) show that, for consensus, local decision bounds are not compatible with global decision bounds (roughly speaking, they cannot be reached by the same algorithm), and (2) draw the ¯rst sharp line between the time-complexity of uniform consensus and atomic commit. Then we consider the eventually synchronous model where we give tight local decision bounds for synchronous runs of uniform consensus. (In this model, consensus and uniform consensus are similar, atomic commit is impossible, and one cannot bound the number of rounds to reach a decision in non-synchronous runs of consensus algorithms.) We prove a counter-intuitive result that the early local decision bound is the same as the early global decision bound. We also give a matching early deciding consensus algorithm that is signi¯cantly better than previous eventually synchronous consensus algorithms

Distributed eventual leader election in the crash-recovery and general omission failure models.

102 p.Distributed applications are present in many aspects of everyday life. Banking, healthcare or transportation are examples of such applications. These applications are built on top of distributed systems. Roughly speaking, a distributed system is composed of a set of processes that collaborate among them to achieve a common goal. When building such systems, designers have to cope with several issues, such as different synchrony assumptions and failure occurrence. Distributed systems must ensure that the delivered service is trustworthy.Agreement problems compose a fundamental class of problems in distributed systems. All agreement problems follow the same pattern: all processes must agree on some common decision. Most of the agreement problems can be considered as a particular instance of the Consensus problem. Hence, they can be solved by reduction to consensus. However, a fundamental impossibility result, namely (FLP), states that in an asynchronous distributed system it is impossible to achieve consensus deterministically when at least one process may fail. A way to circumvent this obstacle is by using unreliable failure detectors. A failure detector allows to encapsulate synchrony assumptions of the system, providing (possibly incorrect) information about process failures. A particular failure detector, called Omega, has been shown to be the weakest failure detector for solving consensus with a majority of correct processes. Informally, Omega lies on providing an eventual leader election mechanism