Implementing the weakest failure detector for solving consensus

The concept of unreliable failure detector was introduced by Chandra and Toueg as a mechanism that provides information about process failures. This mechanism has been used to solve several agreement problems, such as the consensus problem. In this paper, algorithms that implement failure detectors in partially synchronous systems are presented. First two simple algorithms of the weakest class to solve the consensus problem, namely the Eventually Strong class (⋄S), are presented. While the first algorithm is wait-free, the second algorithm is f-resilient, where f is a known upper bound on the number of faulty processes. Both algorithms guarantee that, eventually, all the correct processes agree permanently on a common correct process, i.e. they also implement a failure detector of the class Omega (Ω). They are also shown to be optimal in terms of the number of communication links used forever. Additionally, a wait-free algorithm that implements a failure detector of the Eventually Perfect class (⋄P) is presented. This algorithm is shown to be optimal in terms of the number of bidirectional links used forever

The Failure Detector Abstraction

A failure detector is a fundamental abstraction in distributed computing. This paper surveys this abstraction through two dimensions. First we study failure detectors as building blocks to simplify the design of reliable distributed algorithms. In particular, we illustrate how failure detectors can factor out timing assumptions to detect failures in distributed agreement algorithms. Second, we study failure detectors as computability benchmarks. That is, we survey the weakest failure detector question and illustrate how failure detectors can be used to classify problems. We also highlight some limitations of the failure detector abstraction along each of the dimensions

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Eventually consistent failure detectors �

The concept of unreliable failure detector was introduced by Chandra and Toueg as a mechanism that provides information about process failures. This mechanism has been used to solve different problems in asynchronous systems, in particular the Consensus problem. In this paper, we present a new class of unreliable failure detectors, which we call Eventually Consistent and denote by ♦C. This class combines the failure detection capabilities of class ♦S with the eventual leader election capability of class �. This capability allows all correct processes to eventually choose the same correct process as leader. We study the relationship between ♦C and other classes of failure detectors. We also propose an efficient algorithm to transform ♦C into ♦P in models of partial synchrony. Finally, to show the power of this new class of failure detectors, we present a Consensus algorithm based on ♦C. This algorithm successfully exploits both the leader election and the failure detection capabilities of the failure detector, and performs better in number of rounds than all the previously proposed algorithms for ♦S