Brief Announcement: Revisiting Consensus Protocols through Wait-Free Parallelization

In this brief announcement, we propose a protocol-agnostic approach to improve the design of primary-backup consensus protocols. At the core of our approach is a novel wait-free design of running several instances of the underlying consensus protocol in parallel. To yield a high-performance parallelized design, we present coordination-free techniques to order operations across parallel instances, deal with instance failures, and assign clients to specific instances. Consequently, the design we present is able to reduce the load on individual instances and primaries, while also reducing the adverse effects of any malicious replicas. Our design is fine-tuned such that the instances coordinated by non-faulty replicas are wait-free: they can continuously make consensus decisions, independent of the behavior of any other instances

Robust Validation: Confident Predictions Even When Distributions Shift

While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy---coming from robust statistics and optimization---is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.'s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity.Comment: 35 pages, 6 figure

Dissecting BFT Consensus: In Trusted Components we Trust!

The growing interest in reliable multi-party applications has fostered widespread adoption of Byzantine Fault-Tolerant (BFT) consensus protocols. Existing BFT protocols need f more replicas than Paxos-style protocols to prevent equivocation attacks. Trust-BFT protocols instead seek to minimize this cost by making use of trusted components at replicas. This paper makes two contributions. First, we analyze the design of existing Trust-BFT protocols and uncover three fundamental limitations that preclude most practical deployments. Some of these limitations are fundamental, while others are linked to the state of trusted components today. Second, we introduce a novel suite of consensus protocols, FlexiTrust, that attempts to sidestep these issues. We show that our FlexiTrust protocols achieve up to 185% more throughput than their Trust-BFT counterparts