Understanding distributed computing is not an easy task. This is due to the many facets of uncertainty one has to cope with and master in order to produce correct distributed software. A previous book Communication and Agreement Abstraction for Fault-tolerant Asynchronous Distributed Systems (published by Morgan & Claypool, 2010) was devoted to the problems created by crash failures in asynchronous message-passing systems. The present book focuses on the way to cope with the uncertainty created by process failures (crash, omission failures and Byzantine behavior) in synchronous message-passing systems (i.e., systems whose progress is governed by the passage of time). To that end, the book considers fundamental problems that distributed synchronous processes have to solve. These fundamental problems concern agreement among processes (if processes are unable to agree in one way or another in presence of failures, no non-trivial problem can be solved). They are consensus, interactive consistency, k-set agreement and non-blocking atomic commit. Being able to solve these basic problems efficiently with provable guarantees allows applications designers to give a precise meaning to the words "cooperate" and "agree" despite failures, and write distributed synchronous programs with properties that can be stated and proved. Hence, the aim of the book is to present a comprehensive view of agreement problems, algorithms that solve them and associated computability bounds in synchronous message-passing distributed systems. Table of Contents: List of Figures / Synchronous Model, Failure Models, and Agreement Problems / Consensus and Interactive Consistency in the Crash Failure Model / Expedite Decision in the Crash Failure Model / Simultaneous Consensus Despite Crash Failures / From Consensus to k-Set Agreement / Non-Blocking Atomic Commit in Presence of Crash Failures / k-Set Agreement Despite Omission Failures / Consensus Despite Byzantine Failures / Byzantine Consensus in Enriched Models

Understanding distributed computing is not an easy task. This is due to the many facets of uncertainty one has to cope with and master in order to produce correct distributed software. Considering the uncertainty created by asynchrony and process crash failures in the context of message-passing systems, the book focuses on the main abstractions that one has to understand and master in order to be able to produce software with guaranteed properties. These fundamental abstractions are communication abstractions that allow the processes to communicate consistently (namely the register abstraction and the reliable broadcast abstraction), and the consensus agreement abstractions that allows them to cooperate despite failures. As they give a precise meaning to the words "communicate" and "agree" despite asynchrony and failures, these abstractions allow distributed programs to be designed with properties that can be stated and proved. Impossibility results are associated with these abstractions. Hence, in order to circumvent these impossibilities, the book relies on the failure detector approach, and, consequently, that approach to fault-tolerance is central to the book. Table of Contents: List of Figures / The Atomic Register Abstraction / Implementing an Atomic Register in a Crash-Prone Asynchronous System / The Uniform Reliable Broadcast Abstraction / Uniform Reliable Broadcast Abstraction Despite Unreliable Channels / The Consensus Abstraction / Consensus Algorithms for Asynchronous Systems Enriched with Various Failure Detectors / Constructing Failure Detectors

Understanding distributed computing is not an easy task. This is due to the many facets of uncertainty one has to cope with and master in order to produce correct distributed software. A previous book Communication and Agreement Abstraction for Fault-tolerant Asynchronous Distributed Systems (published by Morgan & Claypool, 2010) was devoted to the problems created by crash failures in asynchronous message-passing systems. The present book focuses on the way to cope with the uncertainty created by process failures (crash, omission failures and Byzantine behavior) in synchronous message-passing systems (i.e., systems whose progress is governed by the passage of time). To that end, the book considers fundamental problems that distributed synchronous processes have to solve. These fundamental problems concern agreement among processes (if processes are unable to agree in one way or another in presence of failures, no non-trivial problem can be solved). They are consensus, interactive consistency, k-set agreement and non-blocking atomic commit. Being able to solve these basic problems efficiently with provable guarantees allows applications designers to give a precise meaning to the words ""cooperate"" and ""agree"" despite failures, and write distributed synchronous programs with properties that can be stated and proved. Hence, the aim of the book is to present a comprehensive view of agreement problems, algorithms that solve them and associated computability bounds in synchronous message-passing distributed systems. Table of Contents: List of Figures / Synchronous Model, Failure Models, and Agreement Problems / Consensus and Interactive Consistency in the Crash Failure Model / Expedite Decision in the Crash Failure Model / Simultaneous Consensus Despite Crash Failures / From Consensus to k-Set Agreement / Non-Blocking Atomic Commit in Presence of Crash Failures / k-Set Agreement Despite Omission Failures / Consensus Despite Byzantine Failures / Byzantine Consensus in Enriched Models

As the structure of contemporary communication networks grows more complex, practical networked distributed systems become prone to component failures. Fault-tolerant consensus in message-passing systems allows participants in the system to agree on a common value despite the malfunction or misbehavior of some components. It is a task of fundamental importance for distributed computing, due to its numerous applications. We summarize studies on the topological conditions that determine the feasibility of consensus, mainly focusing on directed networks and the case of restricted topology knowledge at each participant. Recently, significant efforts have been devoted to fully characterize the underlying communication networks in which variations of fault-tolerant consensus can be achieved. Although the deduction of analogous topological conditions for undirected networks of known topology had shortly followed the introduction of the problem, their extension to the directed network case has been proven a highly non-trivial task. Moreover, global knowledge restrictions, inherent in modern large-scale networks, require more elaborate arguments concerning the locality of distributed computations. In this work, we present the techniques and ideas used to resolve these issues. Recent studies indicate a number of parameters that affect the topological conditions under which consensus can be achieved, namely, the fault model, the degree of system synchrony (synchronous vs. asynchronous), the type of agreement (exact vs. approximate), the level of topology knowledge, and the algorithm class used (general vs. iterative). We outline the feasibility and impossibility results for various combinations of the above parameters, extensively illustrating the relation between network topology and consensus.

This book presents the most important fault-tolerant distributed programming abstractions and their associated distributed algorithms, in particular in terms of reliable communication and agreement, which lie at the heart of nearly all distributed applications. These programming abstractions, distributed objects or services, allow software designers and programmers to cope with asynchrony and the most important types of failures such as process crashes, message losses, and malicious behaviors of computing entities, widely known under the term "Byzantine fault-tolerance". The author introduces these notions in an incremental manner, starting from a clear specification, followed by algorithms which are first described intuitively and then proved correct. The book also presents impossibility results in classic distributed computing models, along with strategies, mainly failure detectors and randomization, that allow us to enrich these models. In this sense, the book constitutes an introduction to the science of distributed computing, with applications in all domains of distributed systems, such as cloud computing and blockchains. Each chapter comes with exercises and bibliographic notes to help the reader approach, understand, and master the fascinating field of fault-tolerant distributed computing.

This book aims at being a comprehensive and pedagogical introduction to the concept of self-stabilization, introduced by Edsger Wybe Dijkstra in 1973. Self-stabilization characterizes the ability of a distributed algorithm to converge within finite time to a configuration from which its behavior is correct (i.e., satisfies a given specification), regardless the arbitrary initial configuration of the system. This arbitrary initial configuration may be the result of the occurrence of a finite number of transient faults. Hence, self-stabilization is actually considered as a versatile non-masking fault tolerance approach, since it recovers from the effect of any finite number of such faults in a unified manner. Another major interest of such an automatic recovery method comes from the difficulty of resetting malfunctioning devices in a large-scale (and so, geographically spread) distributed system (the Internet, Pair-to-Pair networks, and Delay Tolerant Networks are examples of such distributed systems). Furthermore, self-stabilization is usually recognized as a lightweight property to achieve fault tolerance as compared to other classical fault tolerance approaches. Indeed, the overhead, both in terms of time and space, of state-of-the-art self-stabilizing algorithms is commonly small. This makes self-stabilization very attractive for distributed systems equipped of processes with low computational and memory capabilities, such as wireless sensor networks. After more than 40 years of existence, self-stabilization is now sufficiently established as an important field of research in theoretical distributed computing to justify its teaching in advanced research-oriented graduate courses. This book is an initiation course, which consists of the formal definition of self-stabilization and its related concepts, followed by a deep review and study of classical (simple) algorithms, commonly used proof schemes and design patterns, as well as premium results issued from the self-stabilizing community. As often happens in the self-stabilizing area, in this book we focus on the proof of correctness and the analytical complexity of the studied distributed self-stabilizing algorithms. Finally, we underline that most of the algorithms studied in this book are actually dedicated to the high-level atomic-state model, which is the most commonly used computational model in the self-stabilizing area. However, in the last chapter, we present general techniques to achieve self-stabilization in the low-level message passing model, as well as example algorithms.