Byzantine Fault-Tolerant (BFT) protocols play an important role in blockchains. As the deployment of such systems extends to wide-area networks, the scalability of BFT protocols becomes a critical concern. Optimizations that assign specific roles to individual replicas can significantly improve the performance of BFT systems. However, such role assignment is highly sensitive to faults, potentially undermining the optimizations' effectiveness.

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Blockchain technology sparked renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works predominantly focused on improving the scalability and throughput of these protocols, few of them addressed latency. We present Mercury, a novel transformation to autonomously optimize the latency of quorum-based BFT consensus. Mercury employs a dual resilience threshold that enables faster transaction ordering when the system contains few faulty replicas. Mercury allows forming compact quorums that substantially accelerate consensus using a smaller resilience threshold. Nevertheless, Mercury upholds standard SMR safety and liveness guarantees with optimal resilience, thanks to its judicious use of a dual operation mode and BFT forensics techniques. Our experiments spread tens of replicas across continents and reveal that Mercury can order transactions with finality in less than 0.4s, half the time of a PBFT-like protocol (optimal in terms of number of communication steps and resilience) in the same network. Furthermore, Mercury matches the latency of running its base protocol on theoretically optimal internet links (transmitting at 67% of the speed of light).

Novel Byzantine fault-tolerant (BFT) state machine replication protocols improve scalability for their practical use in distributed ledger technology, where hundreds of replicas must reach consensus. Assessing that BFT protocol implementations meet their performance expectations requires careful evaluation. We propose a new methodology using scalable network simulations to predict BFT protocol performance. Our simulation architecture allows for the integration of existing BFT implementations without modification or re-implementation, offering a cost-effective alternative to large-scale cloud experiments. We validate our method by comparing simulation results with real-world cloud deployments, showing that simulations can accurately predict performance at larger scales when network limitations dominate. In our study, we applied this methodology to assess the performance of several “blockchain-generation” BFT protocols, including HotStuff, Kauri, Narwhal & Tusk, and BullShark, under realistic network conditions (with constrained 25 Mbit/s bandwidth) and induced faults. Kauri emerges as the top performer, achieving 6,742 operations per second (op/s) with 128 replicas, outperforming BullShark (2,318 op/s) and Tusk (1,952 op/s). HotStuff, using secp256k1 and BLS signatures, reaches 494 op/s and 707 op/s, respectively, demonstrating the efficiency of BLS-signature aggregation for saving bandwidth. This study demonstrates that state-of-the-art asynchronous BFT protocols can achieve competitive throughput in large-scale, real-world scenarios.

With the advancement of blockchain systems, many recent research works have proposed distributed ledger technology (DLT) that employs Byzantine fault-tolerant (BFT) consensus protocols to decide which block to append next to the ledger. Notably, BFT consensus can offer high performance, energy efficiency, and provable correctness properties, and it is thus considered a promising building block for creating highly resilient and performant blockchain infrastructures. Yet, a major ongoing challenge is to make BFT consensus applicable to large-scale environments. A large body of recent work addresses this challenge by developing novel ideas to improve the scalability of BFT consensus, thus opening the path for a new generation of BFT protocols tailored to the needs of blockchain. In this survey, we create a systematization of knowledge about the novel scalability-enhancing techniques that state-of-the-art BFT consensus protocols use. For our comparison, we closely analyze the efforts, assumptions, and trade-offs these protocols make.

Blockchain technology has renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works focus on scalability and throughput, few address latency.We present the idea of FlashConsensus, a transformation for quorum-based BFT consensus that uses an adaptive resilience threshold. FlashConsensus employs adaptive weighted replication to assign high voting power to specific replicas, thus yielding smaller quorums that speed up consensus. To maintain SMR safety and liveness guarantees with optimal resilience, FlashConsensus employs two modes of operation and BFT forensics. Experiments with replicas worldwide show FlashConsensus orders client requests in less than 0.4 s, which is half the time needed by a PBFT-like protocol with optimal consensus latency.

Recent Byzantine fault-tolerant (BFT) state machine replication (SMR) protocols increasingly focus on scalability to meet the requirements of distributed ledger technology (DLT). Validating the performance of scalable BFT protocol implementations requires careful evaluation. Our solution uses network simulations to forecast the performance of BFT protocols while experimentally scaling the environment. Our method seamlessly plug-and-plays existing BFT implementations into the simulation without requiring code modification or re-implementation, which is often time-consuming and error-prone. Furthermore, our approach is also significantly cheaper than experiments with real large-scale cloud deployments. In this paper, we first explain our simulation architecture, which enables scalable performance evaluations of BFT systems through high-performance network simulations. We validate the accuracy of these simulations for predicting the performance of BFT systems by comparing simulation results with measurements of real systems deployed on cloud infrastructures. We found that simulation results display a reasonable approximation at a larger system scale, because the network eventually becomes the dominating factor limiting system performance. In the second part of our paper, we use our simulation method to evaluate the performance of PBFT and BFT protocols from the "blockchain generation", such as HotStuff and Kauri, in large-scale and realistic wide-area network scenarios, as well as under induced faults.

With upcoming blockchain infrastructures, world-spanning Byzantine consensus is getting practical and necessary. In geographically distributed systems, the pace at which consensus is achieved is limited by the heterogeneous latencies of connections between replicas. If deployed on a wide-area network, consensus-based systems benefit from weighted replication, an approach that utilizes extra replicas and assigns higher voting weights to well-connected replicas. This approach enables more choice in quorum formation and replicas can leverage proportionally smaller quorums to advance, thus decreasing consensus latency. However, the system needs a solution to autonomously adjust to its environment if network conditions change or faults occur. We present Adaptive Wide-Area REplication (AWARE), a mechanism that improves the geographical scalability of consensus with nodes being widely spread across the world. Essentially, AWARE is an automated and dynamic voting-weight tuning and leader positioning scheme, which supports the emergence of fast quorums in the system. It employs a reliable self-monitoring process and provides a prediction model seeking to minimize the system’s consensus latency. In experiments using several AWS EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast configuration yielding latency gains observed by clients located across the globe.

In geo-replicated systems, the heterogeneous latencies of connections between replicas limit the system's ability to achieve consensus fast. State machine replication (SMR) protocols can be refined for their deployment in wide-area networks by using a weighting scheme for active replication that employs additional replicas and assigns higher voting power to faster replicas. Utilizing more variability in quorum formation allows replicas to swiftly proceed to subsequent protocol stages, thus decreasing consensus latency. However, if network conditions vary during the system's lifespan or faults occur, the system needs a solution to autonomously adjust to new conditions. We incorporate the idea of self-optimization into geographically distributed, weighted replication by introducing AWARE, an automated and dynamic voting weight tuning and leader positioning scheme. AWARE measures replica-to-replica latencies and uses a prediction model, thriving to minimize the system's consensus latency. In experiments using different Amazon EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast weight configuration yielding latency gains observed by clients located across the globe.