With the growing interest in blockchains, permissioned approaches to consensus have received increasing attention. Unfortunately, the BFT consensus algorithms that are the backbone of most of these blockchains scale poorly and offer limited throughput. In fact, many state-of-the-art BFT consensus algorithms require a single leader process to receive and validate votes from a quorum of processes and then broadcast the result, which is inherently non-scalable. Recent approaches avoid this bottleneck by using dissemination/aggregation trees to propagate values and collect and validate votes. However, the use of trees increases the round latency, which limits the throughput for deeper trees. In this article, we propose Kauri, a BFT communication abstraction that sustains high throughput as the system size grows by leveraging a novel pipelining technique to perform scalable dissemination and aggregation on trees. Furthermore, when the number of faults is moderate (arguably the most common case in practice), our construction is able to recover from faults in an optimal number of reconfiguration steps. We implemented and experimentally evaluated Kauri with up to 800 processes. Our results show that Kauri outperforms the throughput of state-of-the-art permissioned blockchain protocols, by up to 58x without compromising latency. Interestingly, in some cases, the parallelization provided by Kauri can also decrease the latency.

With the growing commercial interest in blockchains, permissioned implementations have received increasing attention. Unfortunately, the BFT consensus algorithms that are the backbone of most of these blockchains scale poorly and offer limited throughput. Many state-of-the-art algorithms require a single leader process to receive and validate votes from a quorum of processes and then broadcast the result, which is inherently non-scalable. Recent approaches avoid this bottleneck by using dissemination/aggregation trees to propagate values and collect and validate votes. However, the use of trees increases the round latency, which ultimately limits the throughput for deeper trees. In this paper we propose Kauri, a BFT communication abstraction that can sustain high throughput as the system size grows, leveraging a novel pipelining technique to perform scalable dissemination and aggregation on trees. Our evaluation shows that Kauri outperforms the throughput of state-of-the-art permissioned blockchain protocols, such as HotStuff, by up to 28x. Interestingly, in many scenarios, the parallelization provided by Kauri can also decrease the latency.

Although graph-databases have been assuming an increasing relevance in applications that exhibit strong dependability requirements, including tolerance to malicious faults, few works have addressed Byzantine fault tolerance in this particular context, and previous attempts suffer from lack of flexibility and poor performance. This article describes and evaluates Fireplug, a flexible architecture to build robust geo-replicated graph databases. Fireplug can be configured to tolerate from crash to Byzantine faults, both within and across different datacenters. Furthermore, Fireplug is robust to bugs in existing graph database implementations, as it allows to combine multiple graph database instances in a cohesive manner. Thus, Fireplug can support many different deployments, according to the performance/robustness trade-offs imposed by the target application. Our evaluation shows that Fireplug is able implement Byzantine fault tolerance without penalty when compared to the built-in replication mechanism of Neo4j, which only supports crash faults. Additionally, performance optimizations introduced by Fireplug improve the overall performance by up to 900 percent in geo-replicated scenarios.

Transparency is a problem in the hiring process of many institutions. For applicants, this process is often difficult to follow since applicants never know the state of their application and can't be sure that they are being evaluated in a fair manner. This is especially important in the public sector. Based on these premises, we propose a transparent and decentral model for the application processes by using Blockchain and smart contracts. In this system, vacancies are published to the blockchain as smart contracts, and candidates, as well as application reviewers and institutions, are rewarded for their honest participation in the process. This paper evaluates the proposed system and offers an example application to prove the viability of the model.

Modern fault-tolerant distributed architectures can be configured to tolerate a wide-range of faults. For instance, Fireplug is a distributed BFT graph database, based on n-version programming, that can be configured to tolerate crash or Byzantine faults, uncorrelated faults in individual machines, correlated faults that affect all replicas running a given software version, or correlated faults that affect an entire datacenter. Interestingly, in such a system, fault handling heavily depends on the type of faults the system is configured to tolerate. To hardwire all possible behaviours in the fault-handling code is inflexible and may even be impractical. In this paper, we explore a different alternative that consists in specifying not only the system configuration, but also the fault-handling behaviour, and how the system adapts to changes in the workload, in a policy language, that is processed externally to the managed system. We show that, using this approach, a single simplified codebase of the managed system can be used effectively to address a wide range of dependability constraints.