Easy Consensus Algorithms for the Crash-Recovery Model

In the crash-recovery failure model of asynchronous distributed systems, processes can temporarily stop to execute steps and later restart their computation from a predefined local state. The crash-recovery model is much more realistic than the crash-stop failure model in which processes merely are allowed to stop executing steps. The additional complexity is reflected in the multitude of assumptions and the technical complexity of algorithms which have been developed for that model. We focus on the problem of consensus in the crash-recovery model, but instead of developing completely new algorithms from scratch, our approach aims at reusing existing crash-stop consensus algorithms in a modular way using the abstraction of failure detectors. As a result, we present three new and relatively simple consensus algorithms for the crash-recovery model for different types of assumptions

You Only Live Multiple Times: A Blackbox Solution for Reusing Crash-Stop Algorithms In Realistic Crash-Recovery Settings

Distributed agreement-based algorithms are often specified in a crash-stop asynchronous model augmented by Chandra and Toueg\u27s unreliable failure detectors. In such models, correct nodes stay up forever, incorrect nodes eventually crash and remain down forever, and failure detectors behave correctly forever eventually, However, in reality, nodes as well as communication links both crash and recover without deterministic guarantees to remain in some state forever. In this paper, we capture this realistic temporary and probabilitic behaviour in a simple new system model. Moreover, we identify a large algorithm class for which we devise a property-preserving transformation. Using this transformation, many algorithms written for the asynchronous crash-stop model run correctly and unchanged in real systems

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

LIPIcs

Fault-tolerant distributed algorithms play an important role in many critical/high-availability applications. These algorithms are notoriously difficult to implement correctly, due to asynchronous communication and the occurrence of faults, such as the network dropping messages or computers crashing. Nonetheless there is surprisingly little language and verification support to build distributed systems based on fault-tolerant algorithms. In this paper, we present some of the challenges that a designer has to overcome to implement a fault-tolerant distributed system. Then we review different models that have been proposed to reason about distributed algorithms and sketch how such a model can form the basis for a domain-specific programming language. Adopting a high-level programming model can simplify the programmer's life and make the code amenable to automated verification, while still compiling to efficiently executable code. We conclude by summarizing the current status of an ongoing language design and implementation project that is based on this idea