Crash-tolerant Consensus in Directed Graph Revisited

Fault-tolerant distributed consensus is a fundamental problem in secure distributed computing. In this work, we consider the problem of distributed consensus in directed graphs tolerating crash failures. Tseng and Vaidya (PODC\u2715) presented necessary and sufficient condition for the existence of consensus protocols in directed graphs. We improve the round and communication complexity of their protocol. Moreover, we prove that our protocol requires the optimal number of communication rounds, required by any protocol belonging to a restricted class of crash-tolerant consensus protocols in directed graphs

Simple Bivalency Proofs of the Lower Bounds in Synchronous Consensus Problems

A fundamental problem of fault-tolerant distributed computing is for the reliable processes to reach a consensus. For a synchronous distributed system of n processes with up to t crash failures and f failures actually occur, we prove using a straightforward bivalency argument that the lower bound for reaching uniform consensus is (f + 2)-rounds in the case of 0 < f ⤠t â2, and a new lower bound for early-stopping consensus is min (t + 1, f + 2)-rounds where 0 ⤠f ⤠t. Both proofs are simpler and more intuitive than the traditional methods such as backward induction. Our main contribution is that we solve the open problem of proving that bivalency can be applied to show the (f + 2)-rounds lower bound for synchronous uniform consensus.Singapore-MIT Alliance (SMA

Model Checking of Consensus Algorithms

We show for the first time that standard model checking allows one to completely verify asynchronous algorithms for solving consensus, a fundamental problem in fault-tolerant distributed computing. Model checking is a powerful verification methodology based on state exploration. However it has rarely been applied to consensus algorithms, because these algorithms induce huge, often infinite state spaces. Here we focus on consensus algorithms based on the Heard-Of model (HO model, for short), a new computation model for distributed computing. By making use of the high abstraction level provided by this computation model, we develop a methodology for verifying consensus algorithms in every possible state by model checking. This paper describes the proposed verification methodology and the results of applying it to various consensus algorithms

Introduction to the special section on dependable network computing

Dependable network computing is becoming a key part of our daily economic and social life. Every day, millions of users and businesses are utilizing the Internet infrastructure for real-time electronic commerce transactions, scheduling important events, and building relationships. While network traffic and the number of users are rapidly growing, the mean-time between failures (MTTF) is surprisingly short; according to recent studies, in the majority of Internet backbone paths, the MTTF is 28 days. This leads to a strong requirement for highly dependable networks, servers, and software systems. The challenge is to build interconnected systems, based on available technology, that are inexpensive, accessible, scalable, and dependable. This special section provides insights into a number of these exciting challenges

Fault-tolerant distributed computing scheme based on erasure codes

Some emerging classes of distributed computing systems, such peer-to-peer or grid computing computing systems, are composed of heterogeneous computing resources potentially unreliable. This paper proposes to use erasure codes to improve the fault-tolerance of parallel distributed computing applications in this context. A general method to generate redundant processes from a set of parallel processes is presented. This scheme allows the recovery of the result of the application even if some of the processes crash

Computing in the RAIN: a reliable array of independent nodes

The RAIN project is a research collaboration between Caltech and NASA-JPL on distributed computing and data-storage systems for future spaceborne missions. The goal of the project is to identify and develop key building blocks for reliable distributed systems built with inexpensive off-the-shelf components. The RAIN platform consists of a heterogeneous cluster of computing and/or storage nodes connected via multiple interfaces to networks configured in fault-tolerant topologies. The RAIN software components run in conjunction with operating system services and standard network protocols. Through software-implemented fault tolerance, the system tolerates multiple node, link, and switch failures, with no single point of failure. The RAIN-technology has been transferred to Rainfinity, a start-up company focusing on creating clustered solutions for improving the performance and availability of Internet data centers. In this paper, we describe the following contributions: 1) fault-tolerant interconnect topologies and communication protocols providing consistent error reporting of link failures, 2) fault management techniques based on group membership, and 3) data storage schemes based on computationally efficient error-control codes. We present several proof-of-concept applications: a highly-available video server, a highly-available Web server, and a distributed checkpointing system. Also, we describe a commercial product, Rainwall, built with the RAIN technology

Coordination-Free Byzantine Replication with Minimal Communication Costs

State-of-the-art fault-tolerant and federated data management systems rely on fully-replicated designs in which all participants have equivalent roles. Consequently, these systems have only limited scalability and are ill-suited for high-performance data management. As an alternative, we propose a hierarchical design in which a Byzantine cluster manages data, while an arbitrary number of learners can reliable learn these updates and use the corresponding data. To realize our design, we propose the delayed-replication algorithm, an efficient solution to the Byzantine learner problem that is central to our design. The delayed-replication algorithm is coordination-free, scalable, and has minimal communication cost for all participants involved. In doing so, the delayed-broadcast algorithm opens the door to new high-performance fault-tolerant and federated data management systems. To illustrate this, we show that the delayed-replication algorithm is not only useful to support specialized learners, but can also be used to reduce the overall communication cost of permissioned blockchains and to improve their storage scalability