Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains

Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains and one of the Hyperledger projects hosted by the Linux Foundation (www.hyperledger.org). Fabric is the first truly extensible blockchain system for running distributed applications. It supports modular consensus protocols, which allows the system to be tailored to particular use cases and trust models. Fabric is also the first blockchain system that runs distributed applications written in standard, general-purpose programming languages, without systemic dependency on a native cryptocurrency. This stands in sharp contrast to existing blockchain platforms that require "smart-contracts" to be written in domain-specific languages or rely on a cryptocurrency. Fabric realizes the permissioned model using a portable notion of membership, which may be integrated with industry-standard identity management. To support such flexibility, Fabric introduces an entirely novel blockchain design and revamps the way blockchains cope with non-determinism, resource exhaustion, and performance attacks. This paper describes Fabric, its architecture, the rationale behind various design decisions, its most prominent implementation aspects, as well as its distributed application programming model. We further evaluate Fabric by implementing and benchmarking a Bitcoin-inspired digital currency. We show that Fabric achieves end-to-end throughput of more than 3500 transactions per second in certain popular deployment configurations, with sub-second latency, scaling well to over 100 peers.Comment: Appears in proceedings of EuroSys 2018 conferenc

Byzantine-Resilient Federated Learning with Heterogeneous Data Distribution

For mitigating Byzantine behaviors in federated learning (FL), most state-of-the-art approaches, such as Bulyan, tend to leverage the similarity of updates from the benign clients. However, in many practical FL scenarios, data is non-IID across clients, thus the updates received from even the benign clients are quite dissimilar. Hence, using similarity based methods result in wasted opportunities to train a model from interesting non-IID data, and also slower model convergence. We propose DiverseFL to overcome this challenge in heterogeneous data distribution settings. Rather than comparing each client's update with other client updates to detect Byzantine clients, DiverseFL compares each client's update with a guiding update of that client. Any client whose update diverges from its associated guiding update is then tagged as a Byzantine node. The FL server in DiverseFL computes the guiding update in every round for each client over a small sample of the client's local data that is received only once before start of the training. However, sharing even a small sample of client's data with the FL server can compromise client's data privacy needs. To tackle this challenge, DiverseFL creates a Trusted Execution Environment (TEE)-based enclave to receive each client's sample and to compute its guiding updates. TEE provides a hardware assisted verification and attestation to each client that its data is not leaked outside of TEE. Through experiments involving neural networks, benchmark datasets and popular Byzantine attacks, we demonstrate that DiverseFL not only performs Byzantine mitigation quite effectively, it also almost matches the performance of OracleSGD, where the server only aggregates the updates from the benign clients

The Bedrock of Byzantine Fault Tolerance: A Unified Platform for BFT Protocol Design and Implementation

Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by decentralized data management systems with non-trustworthy infrastructures, e.g., permissioned blockchains. BFT protocols cover a broad spectrum of design dimensions from infrastructure settings such as the communication topology, to more technical features such as commitment strategy and even fundamental social choice properties like order-fairness. The proliferation of different BFT protocols has rendered it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. This paper presents Bedrock, a unified platform for BFT protocols design, analysis, implementation, and experiments. Bedrock proposes a design space consisting of a set of design choices capturing the trade-offs between different design space dimensions and providing fundamentally new insights into the strengths and weaknesses of BFT protocols. Bedrock enables users to analyze and experiment with BFT protocols within the space of plausible choices, evolve current protocols to design new ones, and even uncover previously unknown protocols. Our experimental results demonstrate the capability of Bedrock to uniformly evaluate BFT protocols in new ways that were not possible before due to the diverse assumptions made by these protocols. The results validate Bedrock's ability to analyze and derive BFT protocols

Keeping checkpoint/restart viable for exascale systems

Next-generation exascale systems, those capable of performing a quintillion operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoints) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms

Sui Lutris: A Blockchain Combining Broadcast and Consensus

Sui Lutris is the first smart-contract platform to sustainably achieve sub-second finality. It achieves this significant decrease in latency by employing consensusless agreement not only for simple payments but for a large variety of transactions. Unlike prior work, Sui Lutris neither compromises expressiveness nor throughput and can run perpetually without restarts. Sui Lutris achieves this by safely integrating consensuless agreement with a high-throughput consensus protocol that is invoked out of the critical finality path but makes sure that when a transaction is at risk of inconsistent concurrent accesses its settlement is delayed until the total ordering is resolved. Building such a hybrid architecture is especially delicate during reconfiguration events, where the system needs to preserve the safety of the consensusless path without compromising the long-term liveness of potentially misconfigured clients. We thus develop a novel reconfiguration protocol, the first to show the safe and efficient reconfiguration of a consensusless blockchain. Sui Lutris is currently running in production as part of a major smart-contract platform. Combined with the Move Programming language it enables the safe execution of smart contracts that expose objects as a first-class resource. In our experiments Sui Lutris achieves latency lower than 0.5 seconds for throughput up to 5,000 certificates per second (150k ops/s with bundling), compared to the state-of-the-art real-world consensus latencies of 3 seconds. Furthermore, it gracefully handles validators crash-recovery and does not suffer visible performance degradation during reconfiguration