Abstract-The last fifteen years have seen an impressive amount of work on protocols for Byzantine fault-tolerant (BFT) state machine replication (SMR). However, there is still a need for practical and reliable software libraries implementing this technique. BFT-SMART is an open-source Java-based library implementing robust BFT state machine replication. Some of the key features of this library that distinguishes it from similar works (e.g., PBFT and UpRight) are improved reliability, modularity as a first-class property, multicore-awareness, reconfiguration support and a flexible programming interface. When compared to other SMR libraries, BFT-SMART achieves better performance and is able to withstand a number of realworld faults that previous implementations cannot.

Hyperledger Fabric (HLF) is a flexible permissioned blockchain platform designed for business applications beyond the basic digital coin addressed by Bitcoin and other existing networks. A key property of HLF is its extensibility, and in particular the support for multiple ordering services for building the blockchain. Nonetheless, the version 1.0 was launched in early 2017 without an implementation of a Byzantine fault-tolerant (BFT) ordering service. To overcome this limitation, we designed, implemented, and evaluated a BFT ordering service for HLF on top of the BFT-SMART state machine replication/consensus library, implementing also optimizations for wide-area deployment. Our results show that HLF with our ordering service can achieve up to ten thousand transactions per second and write a transaction irrevocably in the blockchain in half a second, even with peers spread in different continents.1

Abstract-State machine replication is a fundamental technique for implementing consistent fault-tolerant services. In the last years, several protocols have been proposed for improving the latency of this technique when the replicas are deployed in geographically-dispersed locations. In this work we evaluate some representative optimizations proposed in the literature by implementing them on an open-source state machine replication library and running the experiments in geographically-diverse PlanetLab nodes and Amazon EC2 regions. Interestingly, our results show that some optimizations widely used for improving the latency of geo-replicated state machines do not bring significant benefits, while others -not yet considered in this context -are very effective. Based on this evaluation, we propose WHEAT, a configurable crash and Byzantine fault-tolerant state machine replication library that uses the optimizations we observed as most effective in reducing SMR latency. WHEAT employs novel voting assignment schemes that, by using few additional spare replicas, enables the system to make progress without needing to access a majority of replicas. Our evaluation shows that a WHEAT system deployed in several Amazon EC2 regions presents a median latency up to 56% lower than a "normal" SMR protocol.

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