Atomic Broadcast, an important abstraction in dependable distributed computing, is usually implemented by solving infinitely many instances of the well-known consensus problem. Some asynchronous consensus algorithms achieve the optimal latency of two (message) steps but cannot guarantee this latency even in good runs, those with timely message delivery and no crashes. This is due to collisions, a result of concurrent proposals. Collision-fast consensus algorithms, which decide within two steps in good runs, exist under certain conditions. Their direct application to solving atomic broadcast, though, does not guarantee delivery in two steps for all messages unless a single failure is tolerated. We show a simple way to build a fault-tolerant collision-fast Atomic Broadcast algorithm based on a variation of the consensus problem we call M-Consensus. Our solution to M-Consensus extends the Paxos protocol to allow multiple processes, instead of the single leader, to have their proposals learned in two steps.

Facebook recently deployed Facebook Messages, its first ever user-facing application built on the Apache Hadoop platform. Apache HBase is a database-like layer built on Hadoop designed to support billions of messages per day. This paper describes the reasons why Facebook chose Hadoop and HBase over other systems such as Apache Cassandra and Voldemort and discusses the application's requirements for consistency, availability, partition tolerance, data model and scalability. We explore the enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the system, and how this solution has significant advantages over the sharded MySQL database scheme used in other applications at Facebook and many other web-scale companies. We discuss the motivations behind our design choices, the challenges that we face in day-to-day operations, and future capabilities and improvements still under development. We offer these observations on the deployment as a model for other companies who are contemplating a Hadoop-based solution over traditional sharded RDBMS deployments.

Adaptability and graceful degradation are important features in distributed systems. Yet, consensus and other agreement protocols, basic building blocks of reliable distributed systems, lack these features and must perform expensive reconfiguration even in face of single failures. In this paper we describe multicoordinated mode of execution for agreement protocols that has improved availability and tolerates failures in a graceful manner. We exemplify our approach by presenting a generic broadcast algorithm. Our protocol can adapt to environment changes by switching to different execution modes. Finally, we show how our algorithm can solve the generalized consensus and its many instances (e.g., consensus and atomic broadcast).

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The paper presents a primary-backup protocol to manage replicated in-memory database systems (IMDBs). The protocol exploits two features of IMDBs: coarse-grain concurrency control and deferred disk writes. Primary crashes are quickly detected by backups and a new primary is elected whenever the current one is suspected to have failed. False failure suspicions are tolerated and never lead to incorrect behavior. The protocol uses a consensus-like algorithm tailor-made for our replication environment. Under normal circumstances (i.e., no failures or false suspicions), transactions can be committed after two communication steps, as seen by the applications. Performance experiments have shown that the protocol has very low overhead and scales linearly with the number of replicas.

Communication-induced checkpointing protocols that ensure rollback-dependency trackability (RDT) guarantee important properties to the recovery system without explicit coordination. However, there was no garbage collection algorithm for them which did not use some type of process synchronization, like time assumptions or reliable control message exchanges. This paper addresses the problem of garbage collection for RDT checkpointing protocols and presents an optimal solution for the case where coordination is done only by means of timestamps piggybacked in application messages. The algorithm uses the same timestamps as off-the-shelf RDT protocols and ensures the tight upper bound on the number of uncollected checkpoints for each process during all the system execution

Current cluster architectures provide the ideal environment to run federations of main-memory database systems (FMMDBs). In FMMDBs, data resides in the main memory of the federation servers, significantly improving performance by avoiding I/O during the execution of read operations. To maximize the performance of update transactions as well, some applications recur to deferred disk writes. This means that update transactions commit before their modifications are written on stable storage and durability must be ensured outside the database. While deferred disk writes in centralized MMDBs relax the durability property of transactions only, in FMMDBs transaction atomicity may be also violated in case of failures. We address this issue from the perspective of log-based rollback-recovery in distributed systems and provide an efficient solution to the problem.

In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic unreliable model. Our approach consists in first defining the optimality of probabilistic reliable broadcast algorithms and the adaptiveness of algorithms that aim at converging toward such optimality. Then, we propose an algorithm that precisely converges toward the optimal behavior, thanks to an adaptive strategy based on Bayesian statistical inference. We compare the performance of our algorithm with that of a typical gossip algorithm through simulation. Our results show, for example, that our adaptive algorithm quickly converges toward such exact knowledge.